ENERGY PARTNERS A Division of Energy Strategies Pty Ltd ACN 085 841 416 ABN 60 085 841 416
Peer Review of New Zealand Climate Data For House Energy Rating: Final Report
Report Prepared By
Energy Partners In Association with
Victoria University of Wellington US Department of Energy Adelaide Applied Algebra
June 2008
Level 1, Manuka Arcade, 20 Franklin Street (PO Box 4170), Manuka ACT 2603, Australia Phone: (02) 6260 6173, Fax: (02) 6260 6555 Email:
[email protected], Web: www.enerstrat.com.au
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TABLE OF CONTENTS 1.
EXECUTIVE SUMMARY ............................................................................... 3
2.
INTRODUCTION ........................................................................................... 4
3.
CRITIQUE OF THE NIWA REPORT ............................................................. 5 3.1. 3.2. 3.3. 3.4. 3.5.
SECTION SUMMARY ............................................................................ 5 STATISTICAL RELIABILITY .................................................................. 7 TMY ANALYSIS ..................................................................................... 8 CLIMATE CLASSIFICATION ................................................................. 9 SELECTION OF LOCATIONS FOR SIMULATION PROGRAM WEATHER FILES................................................................................. 11 3.5.1. 3.5.2.
4.
Central Otago and Queenstown Lakes ............................................. 13 Rotorua Taupo and Bay of Plenty ....................................................... 3
REVIEW OF NIWA WEATHER FILES .......................................................... 6 4.1. CONTEXT FOR THIS SECTION ............................................................ 6 4.2. TECHNICAL ASPECTS OF THE TMY FILES ........................................ 6 4.2.1.
Examination by the EnergyPlus Weather Processor ........................... 6
4.3. CONCLUSION...................................................................................... 10 5.
MANUAL VERIFICATION FOCUSED ON ANZHERS ................................ 13 5.1. ANALYSIS OF THE TMY FILES .......................................................... 13 5.2. VERIFICATION THROUGH HOUSE SIMULATION ............................. 14
CONCLUSIONS AND RECOMMENDATIONS .................................................... 20 6.
REFERENCES ............................................................................................ 22
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1. Executive Summary In May 2007 EECA commissioned the National Institute of Water & Atmospheric Research Ltd (NIWA) to develop New Zealand climate files for the Home Energy Rating Scheme (HERS). The climate file development for both TMY2 and E/E formats was completed in August 2007. This report describes the analysis and peer review of the NIWA report and climate files. The climate files are assessed for correctness and fitness for use in a regulatory context. Several areas for improving the NIWA report and climate files are discussed. These include the following: In several cases, representative climate files are derived from short records (as few as 12 years). With regard to this, the report should be explicit regarding likely error in the degree of reliability of the representative nature of those weather files. NIWA has adopted a new process for the generation of this weather data. The modified process deviates from that in published, peer-reviewed journals. The reasons for rejecting the published procedures should be thoroughly documented. The significance of the weighting factors applied to weather element in the formation of the TMY should be made explicit in the documentation of the files – preferably as part of the meta-data in the header files. Where local measurements are unavailable, the process of managing the generation of solar radiation data appears robust. The approaches to dealing with the perceived lack of clarity in the TMY manual seem clear and well documented. However, the requirement for the approach, which deviates from the published, peer reviewed technique, is not clear. The weather files should include WMO station numbers in the meta-data. This information is used by the EnergyPlus software to extract design conditions from the ASHRAE Fundamentals 2005 Handbook. A number of concerns in the NIWA TMY files in EnergyPlus/ESP-r (EE) format arose when processing the files through the EnergyPlus weather processor. The errors requiring attention are described in Table 9. Three sites (IN, NL, WN) have many potentially significant deviations from the irradiance identity Global = Diffuse + Direct.
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2. Introduction In May 2007 EECA commissioned the National Institute of Water & Atmospheric Research Ltd (NIWA) to develop New Zealand climate files for the Home Energy Rating Scheme (HERS). The climate file development for both TMY2 format and E/E format (US DoE, 2006 used by e.g. EnergyPlus) was completed in August 2007. EECA commissioned Energy Partners (EP) to peer review the New Zealand climate files in both TMY2 and E/E formats to confirm that they were correct and fit for use in a regulatory context. The peer review comprised two parts. Firstly, EP was to confirm that the methodology used for developing the climate files met the original brief, and was correct and robust. Climate Zone selection and site selection was not required to be peer reviewed, however some comment is offered on this subject within the body of this report. Secondly, a random check of all 16 AccuRate climate files was required. This was primarily to confirm the climate files had been accurately derived from the raw data, identifying any systematic errors within the files and confirming that the provided data conformed to the correct format for TMY2 and E/E. It was established prior to this project the AccuRate TMY2 files had been tested for compatibility with the software. Confirmation was required that the information in the files was correct and that the files enabled AccuRate to interpret this information correctly. The E/E files had not been tested with any software. As such, EP was required to confirm that the files were readable by the relevant software, the information in the files was correct, and the files enabled software to interpret the information correctly.
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3. Critique of the NIWA Report This was undertaken by Dr Mike Donn of the Victoria University of Wellington. 3.1. Section Summary The NIWA document is a most sound report. It has carefully and thoroughly explained the processes undertaken in developing a set of TMY data for New Zealand. The processes reported have two parts: a) selection of a set of representative weather files for climate zones in New Zealand b) establishment of a 12 month weather data file comprising the 12 most ‗representative‘ months from the years of data available. The processes undergone to complete these tasks are well-founded in the literature, and in the areas where the report suggests the literature has shortcomings, I am satisfied that the NIWA team has undertaken and documented a sound analysis. What has resulted is a set of welldocumented data that comprises the best-founded set of weather data for R&D purposes that has ever existed in New Zealand. However, I do believe that the data has several areas in which it could be improved: a) there are some files that are, I believe, redundant and which therefore ought not to be a part of the HERS data set: specifically, I suggest that there is only a need for one of the CO-Lauder and QL-Queenstown files; and that there is only a need for one of the TP-Taupo and the RO-Rotorua files (see Table 1: Climate Zone classifications – North Island and Table 2: Climate Zone classifications – South Island for a comparison),i b) the ‗Wellington‘ file is comprised of Paraparaumu data and is representative of the climate from the Manawatu to the Wairarapa and on down to Cook Strait. This seems to me an unreasonable simplification particularly in light of the similarity of the files listed in a) above, and if one takes the likely differences solar gain and wind in respect of any analysis of say natural cooling through opening windows – to take one example; at the very least the East Coast / West Coast split either side of the main divide should in my opinion be recognized if possible.ii c) I believe that the report should be more explicit about the likely error in the degree of reliability of the representative nature of those weather files that are from short records down as low as 8-12 years in several cases.iii d) the underlying reason for the above critical questions is the inadequate description of the rationale for the selection of the recommended climate zones as combinations of Territorial Authority regions and climate data. e) NIWA has adopted a new process for the generation of this weather data, one which apparently deviates from that in published, peer-reviewed journals. Because this weather data has such a crucial impact on any HERS ratings, as well as upon government policy through the likely other uses, I believe that the reasons for rejecting the published procedures need to be more thoroughly documented.
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f) Finally, I suggest that the significance of the weighting factors used in the formation of the TMY (NIWA, page 11, Table 2) be made explicit in the documentation of the files – preferably as part of the meta-data in the header files. It should also be noted that the NIWA Report suggests that there is a need for an altitude adjustment of the weather data. I am inclined to agree with them, but would need more detail / data on this adjustment. To a certain extent, the adjustment is already made through the existence of the Central North Island climate zone. It is probable that the Central South Island climate zones in Lauder and Queenstown are also altitude related. The accompanying spreadsheet of places suggests that these climates encompass a number of different ski fields. However, the wind and the solar radiation maps seem to follow topographical patterns that such an altitude adjustment might well make clear – albeit at the expense of complexity for the analyst using the data. In summary, I have examined the report and – as far as I can check the weather data analysis processes, am convinced that the data analysis has been thorough from a statistical point of view. However, as a result of the points listed above, I have several inter-related reservations. First, I do not believe that the case has been made that the selection of 16 locations best represents the geo-political climate in New Zealand; and second, as a result of selecting some smaller locations, I believe that the data on which some of the locations‘ weather files are based is barely adequate. Third, I believe that the report – and in fact the meta-data in the headers to the files themselves needs some improved documentation to record properly the processes by which the data was generated and to establish its appropriate applications. In conclusion, it should be noted that the differences in climate for all the files are small in international terms. The EnergyPlus weather data statistical analysis package that I have used to assist with this audit suggests there are probably only five distinct but very similar climates from a Heating and Cooling point of view, amongst the proposed 16 climates.
Csb 3C - Cs, Dry Summer Subtropical Csb 3C - Cs, Dry Summer Subtropical Csb 3C - Cs, Dry Summer Subtropical Csb 4C - Cb, Marine (Cool Summer) Csb 3C - Cs, Dry Summer Subtropical Cfb 3C - Cs, Dry Summer Subtropical Csb 3C - Cs, Dry Summer Subtropical Csb 4C - Cb, Marine (Cool Summer) Csb 3C - Cs, Dry Summer Subtropical
NL AK HN RO BP EC NP TP WN
Csb 3C - Cs, Dry Summer Subtropical Csb 4C - Cb, Marine (Cool Summer) Cfb 4C - Cb, Marine (Cool Summer) Cfb 5C - Cfb, Marine (Cool Summer) Cfb 4C - Cb, Marine (Cool Summer) Cfb 5C - Cfb, Marine (Cool Summer) Cfb 4C - Cb, Marine (Cool Summer) Table 2: Climate Zone classifications – South Island
Table 1: Climate Zone classifications – North Island
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NM WC CH CO DN QL IN
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3.2.
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Statistical Reliability
The NIWA report describes the basic data availability for the climate zones in Table 3, below: CliDB is the unique NIWA Climate Database code.iv
Table 3: Basic data availability for climate zones
In the TMY methodology used, five candidate months are selected from which to start selecting a ‗representative‘ month. In the case of Hamilton, Tauranga, Taupo/Turangi, Napier, and Dunedin, this means that the ‗statistical‘ representation was based on selecting almost half the available data for this analysis. This seems neither robust nor advisable when the goal is selecting a representative file upon which investment decisions may be made. It is inevitable that with the greater access to solar radiation data for these files than has ever been possible in the past, not only will these investment decisions be made for the individual house owner, but also at a national level for government policy. In fact, only three locations are reported as having a sufficient database of years that they would meet a high quality score in terms of their representation of ‗typical‘ climates internationally. These are Invercargill, Auckland and Christchurch which all have 38 years of available data. There seems to be a need for looking at this chart to report the following ‗reliability score‘ or ‗index of representation‘: (Excellent, Good and Acceptable) GOOD EXCELLENT ACCEPTABLE ACCEPTABLE GOOD ACCEPTABLE GOOD ACCEPTABLE GOOD
NL AK HN RO BP EC NP TP WN
Northland Auckland Hamilton Rotorua Bay of Plenty East Coast New Plymouth Taupo Wellington
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GOOD GOOD EXCELLENT GOOD GOOD ACCEPTABLE EXCELLENT
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Nelson – Marlborough West Coast Christchurch Central Otago Dunedin Queenstown / Lakes Invercargill
Table 4: “Reliability” of the base data for each climate zone
I should point out in concluding this section that this issue is covered to a degree in the NIWA report: ―Note that in some instances, such as New Plymouth and Nelson-Marlborough, all the selected years come from a narrow range, even though a much longer period is considered. This suggests that the shorter usable period for Hokitika is not unduly restrictive.”(Page 15). I believe that it would still be useful to rank the reliability of these data sets in light of the papers in the literature comparing building energy use predictions for TMY years with building energy use predictions for each of the constituent years on which the TMY is based. 3.3. TMY Analysis v I have read the NIWA TMY process described in their report and I believe that it is a robust and reasoned approach to using the methodologies described in the cited literature. The process of managing the generation of solar radiation data where local measurements are unavailable appears robust. The approaches to dealing with the perceived lack of clarity in the TMY manual seem clear and well documented. However, it is a puzzle to me that the process is considered unclear as the primary reference for the critique of the TRY methodology1 that is quoted by NIWA has itself a primary reference2 that provides the following text in its stepby-step guide: “The five candidate months are ranked on the basis of the closeness of the month to the long term mean and median. Relative differences are calculated between the mean and median air temperature and global radiation of each specific month and the respective mean and medians over the 20 year time-series. The maximum of the four relative differences is assigned to the month.” The NIWA report states that their primary reference (Sawaqed) suggests that the ―…TMY2 prescription is not described with enough detail to allow straightforward implementation. They counter or perhaps illustrate this by describing in good detail a procedure which is evidently wrong in several respects.” I cannot understand how NIWA conclude that this process or indeed the Argiriou process is ‗evidently wrong‘. Nor can I find anywhere in the Sawaqed paper where they state that the ―…TMY2 prescription is not described with enough detail to allow straightforward implementation …‖ Whilst I have no reason to doubt or 1
Sawaqed, N.M.; Zurigat, Y.H.; Al-Hinai, H. (2005). A step-by-step application of Sandia method in developing typical meteorological years for different locations in Oman. International Journal of Energy Research 29: 723-737. 2
A. Argiriou, A. , Lykoudis, S. Kontoyiannidis, S. Balaras, C. A. Asimakopoulos, D. Petrakis, M. and Kassomenos, P. (1999) Comparison Of Methodologies For TMY Generation Using 20 Years Data For Athens, Greece, Solar Energy Vol. 66, No. 1, pp. 33–45. June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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question the process used by NIWA in the generation of the TMY data, I do believe such a statement needs to be supported by evidence. The reason is that NIWA has adopted a new process for the generation of this weather data, one which apparently deviates from that in published, peer-reviewed journals. Because this weather data has such a crucial impact on any HERS ratings as well as upon government policy through the likely other uses I believe that the reasons for rejecting the published procedures need to be thoroughly documented. Finally, and crucially in light of the Sawaqed and Argiriou papers and the literature these review papers quote, I suggest that the significance of the weighting factors used in the formation of the TMY (page 11, Table 2) be made explicit in the documentation of the files – preferably as part of the meta-data in the header files. The weighting has an effect on the potential use of the data. For example, Argiriou suggests that if one were planning to use the TMY data to do the analysis needed to design a highly solar driven process such as a desalination plant, then the weighting of the solar data would be higher. It occurs to me that I have never seen an analysis of the usefulness of this particular weighting for a highly solar driven house. At the very least there ought to be a caveat in the meta data that accompanies the data that suggests that the selection of the weather data has been optimized for building applications and not necessarily for solar water heating or photovoltaic energy generators on the building. 3.4.
Climate Classification
Using the Energy Plus statistical analysis weather program, the following climate categorizations have been made using the ―Köppen classification … derived algorithmically from the source weather data.‖ [which] ―… may not be indicative of the long term climate for this location.‖ These classifications are detailed in Table 5, below. The classification in Table 5 separates the North Island into 3 separate climate zones (coloured yellow, green and orange). If one looks closely at the descriptors, one can see that in international terms these climates are only a little differentiated. The groupings are interesting in that on this basis Taupo and Rotorua are grouped together as expected. However there is no real differentiation between North and South, so that Wellington is grouped with New Plymouth, Northland, Bay of Plenty, Auckland and Hamilton. The East Coast is separated because it apparently has a warm summer and ‗rain all year‘ rather than the other climates which have dry warm summers.
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- Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S)
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Northland
Auckland
Hamilton
Rotorua
Bay of Plenty
East Coast
New Plymouth
Taupo
Wellington
- Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) Table 5: Climate Zone classifications – North Island
Table 6 shows the same analysis for the South Island. The Nelson Marlborough climate is classified as similar to the majority of the North Island climates and the West Coast climate as similar to the Central North Island climate. The rest of the climates are grouped into a coastal
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set (Christchurch, Dunedin and Invercargill) and an inland set (Central Otago and Queenstown). - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "3C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Warm - Marine, Probable Köppen classification=Cs, Dry Summer Subtropical (Mediterranean) - Climate type "Csb" (Köppen classification)** - Mediterranean climate (dry warm summer, mild winter, lat. 30-45°S) - Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S) - Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S) - Climate type "5C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Cool - Marine, Probable Köppen classification=Cfb, Marine (Cool Summer) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S) - Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S) - Climate type "5C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Cool - Marine, Probable Köppen classification=Cfb, Marine (Cool Summer) - Climate type "Cfb" (Köppen classification)** - Marine west coastal (warm summer, mild winter, rain all year, lat. 35-60°S)
Nelson – Marlboroug h
West Coast
Christchurch
Central Otago
Dunedin
Queenstown / Lakes
Invercargill
- Climate type "4C" (ASHRAE Standards 90.1-2004 and 90.2-2004 Climate Zone)** - Mixed - Marine, Probable Köppen classification=Cb, Marine (Cool Summer) Table 6: Climate Zone classifications – South Island
In the following pages (Appendix C) I have appended the Ecotect analyses of these climates. In addition to the above information, I believe they make the case that several of these files are sufficiently similar that there is little or no need for their retention in a data set of this type.
3.5.
Selection of Locations for simulation program weather files
From the discussions at various standards meetings in the past, my understanding has been that the selection of climate data files has been a geo-political process. First, one examines the latitude distribution and determines that of course there is a difference between North and South, so the country cannot be represented by one, or even two climate zones. Then, one looks at the likely other ‗causes of climate difference and notes that there will be a coastal / June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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inland split differentiating those areas more or less influenced by the sea. And finally, one looks at the East / West split across the main dividing mountains down the middle of the two main islands. On this basis each of the two main islands might prima facie be split into North / South / East / West coastal zones and a central zone. This results in approximately ten climate zones in total, plus perhaps a few more that are adjustments for high altitude that are not already dealt with by the coastal / central split.vi The analysis is far from over. In the past, the process has then examined the likelihood of manufacturers producing a range of insulation and energy efficiency products differentiated for each of these 10 zones. This is the geo-political decision. Table 7 and Table 8 are created from the Territorial Authority breakdown tables produced by the New Zealand Statistics Department for the 2006 census. Table 7 shows the population ‗represented‘ by each of the NIWA selected climate zones; and Table 8 shows the number of households for each zone.
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148,440 1,345,293 304,536 65,901 194,910 192,102 146,913 53,349 607,587 133,686 31,326 512,859 30,560 145,634 22,959 90,873
NL AK HN RO BP EC NP TP WN NM WC CH CO DN QL IN
Peer Review of New Zealand Climate Data: Interim Report
Northland Auckland Hamilton Rotorua Bay of Plenty East Coast New Plymouth Taupo Wellington Nelson – Marlborough West Coast Christchurch Central Otago Dunedin Queenstown / Lakes Invercargill
54,441 449,988 108,807 23,220 72,687 70,068 56,844 19,677 226,350 51,561 12,462 196,431 12,281 55,130 8,568 35,487
NL AK HN RO BP EC NP TP WN NM WC CH CO DN QL IN
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Northland Auckland Hamilton Rotorua Bay of Plenty East Coast New Plymouth Taupo Wellington Nelson – Marlborough West Coast Christchurch Central Otago Dunedin Queenstown / Lakes Invercargill
Table 7: Population represented by each climate zone Table 8: Households represented by each climate zone
Looking purely at these tables there seems no justification whatsoever for a differentiation of Rotorua and Taupo; nor for a differentiation of Central Otago from Queenstown Lakes. Looking at these in relation also to the East / West split of climate across the two islands, there seems every reason to separate the West Coast from Christchurch, even though in reality there will be no specific West Coast products developed for around 12,000 households. However, for the same East / West split reasons there seems every reason to ask why Wellington is so large? In this case ―Wellington‖ includes the East Coast Wairarapa region; the mountains in the middle; the West Coast Horowhenua / Manawatu; and what I understood was the distinctly different Southern Cook Strait windy climate of Wellington City itself. For building thermal performance analyses that are increasingly going to be examining the influence of wind availability, this seems to me to be too broad a geo-political area by comparison with the breakdowns in other regions. I have investigated this data further by using the Energy Plus Weather file analyser to create summary statistics for the above zones This is discussed in the following sections. 3.5.1.
Central Otago and Queenstown Lakes
Figure 1 plots the average hourly temperature and solar radiation difference for each month between the climate zone for Central Otago (CO) and the climate zone for Queenstown Lakes (QL). They show that there are differences. As the QL temperatures are subtracted from the CO temperatures they show a difference in daily pattern: CO seems to be warmer at night and vice versa during the day. Similarly, on this basis, QL appears to be slightly sunnier than CO. All this data is confirmed when one examines the heating degree-day totals for these two locations: 3184 degree-days for QL and 3154 degree-days for CO. As these totals have been an accepted first order predictor of heating energy use in a house for over 50 years, this merely adds further to the query as to why these two locations are warranted.
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Figure 1: Comparison of the average hourly temperature and solar radiation for each month between Central Otago and Queenstown Lakes climate zones
However, the significance of these graphed differences is difficult to fathom. There is no easy ruler to apply to determine how big these differences really are. Figure 2 shows the differences between two climates that are acknowledged to be different: Auckland (AK) and Wellington (WN). At first glance the temperature graph seems to show much smaller variation than the similar one for CO-QL. However, the legend shows that yellow and blue are 0-1°C and 1-2°C differences; dark red and orange are 2-3°C and 3-4°C differences respectively. What these then show is that AK is consistently much warmer than WN almost all the time. The average temperature difference between CO and QL is 0.13°C, while the average difference between AK and WN is 2.15°C. The degree-day total on an 18°C base for Auckland is 1,165 K-days and for Wellington is 1,824 K-days. Substantial differences in solar radiation also support the need for both these climate zones. Solar Radiation Differences
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Months
Figure 2: Comparison of average hourly temperature and solar irradiation for each month between Auckland (AK) and Wellington (WN) climate zones
I believe that this analysis shows a need for further justification of the climate zones than has been provided.
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Heating Degree Day Difference
Heating Degree Day Difference
0
50
-20 -40
0
-60 -80
-50
-100 -120
AK-WN HDD
-140 -160
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-150
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-200
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Jul
Aug
Sep
Feb
Mar
Apr
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Jun
AK-WN HDD
Oct
Nov
Jul
Aug
Sep
Oct
Dec
CO-QL HDD Nov
Dec
Figure 3: Monthly degree-day differences. AK-WN on the left, CO-QL on the right
3.5.2.
Rotorua Taupo and Bay of Plenty
Figure 4 represents the differences between Rotorua (RO) and Taupo (TP), in terms of temperature and solar radiation. Solar Radiation Differences
Temp Diff 300
8 7
200
6
7-8
5
6-7
4
5-6
Deg C 3
4-5
100 W/m2
200-300 100-200
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-100-0
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-300--200
-1-0
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11 13 15
Hours
17 19
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N S ov J ep M ul M ay Ja ar n
-300
-2--1
1
Month
3
5
7
9
11 13 15
17
19
Hours
21
23
N S ov J u ep M l Maray J an
Months
Figure 4: Comparison of average hourly temperate and solar radiation for each month between RO and TP climate zones
Figure 5 represents the differences between Rotorua (RO) and the Bay of Plenty (BP) climate zones. Solar Radiation Differences
Temp Diff 300
8 7
200
6
7-8
5
6-7
4
5-6
Deg C 3
4-5
100 W/m2
200-300 100-200
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-1-0
-2 1
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11 13 15
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N S ov J ep M ul a M y Ja ar n
-2--1 Month
-300
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3
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7
9
11
Hours
13 15
17
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21
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N S ov J u ep M l Maray J an
Months
Figure 5: Comparison of the average hourly temperate and solar radiation for each month between RO and BP climate zones
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This is more by way of a test that the process adopted for the CO-QL comparison works. It clearly supports the NIWA report contention that RO and BP are different. The heating degree-day totals are 2145 K-day and 1,274 K-day for RO and BP respectively. The total for Taupo (TP) 2,327 K-day. This is clearly very like RO and both are very different than the BP file. I believe that this consistency suggests that the above CO-QL conclusions are relevant. However, in conjunction with the second, RO-BP comparison, it also demonstrates the concern that RO and TP are probably not sufficiently different to warrant a separate climate zone. Heating Degree Day Difference
Heating Degree Day Difference 50
50
0
0
-50
-50
-100
-100
RO-BP HDD
RO-TP HDD
-150
-150
-200
-200 Jan
Feb
Mar
Jan Apr
May
Jun
Jul
Aug
Sep
Oct
Feb
Mar
Apr
May
RO-TP HDD Nov
Jun
Dec
Jul
Aug
Sep
Oct
RO-BP HDD Nov
Dec
Figure 6: Monthly degree-day differences. Left; RO-TP and right; BP-RO
As with the Central Otago vs Queenstown heating degree-days, the monthly breakdowns of the Rotorua vs Taupo degree-day totals reveals very little reason to define these as different climates. And as, noted above, the differences shown at right between monthly Heating Degree Day (HDD) totals for Bay of Plenty and Rotorua are larger than those between Auckland and Wellington. Finally, whilst we are looking at climate similarities, there seems very little reason on the basis of HDDs to suggest that there is value in having the Northland climate ‗zone‘ data. Figure 7 shows the breakdown of the monthly differences in HDDs between Auckland and Northland:
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Heating Degree Day Difference 50
0
-50
-100
AK-NL HDD
-150
-200 Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
AK-NL HDD Nov
Dec
Figure 7: Monthly degree-day differences between Auckland and Northland weather files.
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4. Review of NIWA Weather Files Subsequent to Section 3, this section was undertaken by Drury B. Crawley, a program manager for the U.S. Department of Energy in Washington, D.C., USA
4.1.
Context for this Section
Michael Donn's review provides significantly more discussion about the selection of the particular locations for geo-political rather than climatic zone reasons. This section of the review does not discuss climatic zone selection but instead focuses on the resulting weather files. In my experience the more weather files/locations for designers the better. For building energy code evaluation, Michael is correct, fewer locations would be better. I also concur with Michael's review of the report. It is one of the better documented reports on developing TMY files that I have seen. I could easily follow and repeat the calculations. Michael has provided an extensive summary of the weather files using various tools.
4.2. 4.2.1.
Technica l Aspects of the TMY Files Examination by the EnergyPlus Weather Processor
This section of the review focuses on the technical aspects of the TMY files--specifically the EE (or EnergyPlus/ESP-r) format TMY files. We use the EnergyPlus weather processor to convert weather files from other formats into the EE format. Part of that processing does extensive checks on the source data--looking for anomalies such as missing data, data that are out of range or sudden step changes in dry-bulb or dew-point temperatures. These checks have evolved over the last 10 years of processing weather data sets for use in EnergyPlus—we use it to process, summarize, and make available more than 1,300 TMY-type weather files for download from the EnergyPlus web site (www.energyplus.gov). We also recently reviewed the new data set of hourly weather data for the US – 1,450+ locations, 1991-2005 (nearly 22,000 station-years, a multi-gigabyte dataset) using the EnergyPlus weather processor. This review identified inconsistencies in the data which were subsequently fixed before the data were released (now available for free download from the U. S. National Climatic Data Center). With that background, I processed the NIWA TMYs in EE format using the EnergyPlus weather processor. The rest of this section discusses what the weather processor identified as issues for the NIWA TMY files. First, there are no WMO station numbers in the files. While this does not affect the simulation programs, the EnergyPlus weather processor uses these to look up design conditions for the location in the ASHRAE Fundamentals 2005 Handbook. Table 9 contains WMO station numbers for 15 of the 16 files (no WMO station number for Lauder).
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TMY WMO Station/s Station Number AK 931190 Auckland
BP
931850
Tauranga
CH
937800
Christchurch
CO DN
-938910
EC
933730
Lauder Dunedin / aero Napier
HN
931730
IN NL NM NP
938450 930120 935460 933090
QL RO TP
938310 932470 932450
WC WN
936150 934200
Ruakura / Hamilton aero Invercargill Kaitaia Nelson New Plymouth Queenstown Rotorua Turangi / Taupo Hokitika Paraparaumu
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Territorial Local Authorities
Rodney, North Shore City, Waitakere City, Auckland City, Manukau City, Papakura, Franklin, ThamesCoromandel Western Bay of Plenty, Tauranga, Whakatane, Kawerau, Opotiki Hurunui, Waimakariri, Christchurch City, Banks Peninsula, Selwyn, Ashburton, Timaru, Waimate Mackenzie, Western Waitaki, Central Otago Eastern Waitaki, Dunedin City, Clutha Gisborne, Wairoa, Hastings, Napier City, Central Hawke‘s Bay Hauraki, Waikato, Matamata-Piako, Hamilton City, Waipa, Otorohanga, South Waikato, Waitomo Southland, Gore, Invercargill City Far North, Whangarei, Kaipara Tasman, Nelson City, Marlborough, Kaikoura New Plymouth, Stratford, South Taranaki, Wanganui Queenstown-Lakes Rotorua Taupo, Ruapehu, northern Rangitikei Buller, Grey, Westland Southern Rangitikei, Manawatu, Palmerston North City, Tararua, Horowhenua, Kapiti Coast, Porirua City, Upper Hutt City, Hutt City, Wellington City, Masterton, Carterton, South Wairarapa
Table 9: WMO Station Numbers
Using weather processor definition files, the weather processor inserted the WMO station numbers into the EPWs along with additional design day information and the names of the territorial location authorities. The weather processor creates several output files for each weather file: .Audit: Out of range data, missing data, statistics, and other issues found while processing the source data. .Stat: Statistics about the weather files (duplicates information in the .Audit) .DDY: Design day conditions (if the location is included in the ASHRAE Fundamentals 2005 Handbook), location information – latitude, longitude, elevation, and time zone in EnergyPlus input format. The weather processor also creates summary statistics in .CSV format files (and a .KML file for use in Google Earth). For this review, the .Audit file provides the most insight to
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any inconsistencies or issues found in reading and processing the file. The .Audit files for all 16 weather files are available as an Attachment.
TMY Issues Identified in .Audit File AK BP CH
CO
DN EC
HN IN NL NM NP
QL RO
TP WC WN
Many hours have Wind Direction of 990. 3 Many hours have Wind Direction of 990. Change in DP for Mar 13: -12.20°C, Hour= 7; 11.40°C, Hour=11. Many hours have Wind Direction of 990. Change in DP for Jan 2, 12.80°C, Hour=10. Change in DP for Jan 16, 11.40°C, Hour=14. Change in DP for Mar 2, 10.50°C, Hour=19. Change in DB for Mar 10, 11.00°C, Hour=18. Change in DP for May 15, 11.00°C, Hour=16. Change in DP for Nov 5, 10.20°C, Hour=10. Change in DB for Jan 17, 10.70°C, Hour=13. Change in DB for Jan 27, -10.30°C, Hour=20. Change in DB for Apr 11, 10.50°C, Hour=14. No issues found. Many hours have Wind Direction of 990. Zenith Luminance = -31.0CD/m² on 8/12 at hour=18. Change in DP for Mar 1, 14.30°C, Hour=17. Change in DP for Jun 9, -10.00°C, Hour= 8. Change in DP for Nov 11, -11.60°C, Hour=17. Change in DP for Nov 24, 10.30°C, Hour= 7. Change in DP for Dec 25, -11.20°C, Hour=15. No major issues found. Many hours have Wind Direction of 990. Many hours have Wind Direction of 990. Many hours have Wind Direction of 990. Many hours have Wind Direction of 990. Zenith Luminance = -6.0CD/m² on 2/10 at Hour=20. Zenith Luminance = -24.0CD/m² on 10/ 1 at hour=19. Change for DP for Mar 14: 39.80°C, Hour= 2; -38.60°C, Hour= 3; 39.40°C, Hour= 6; -38.80°C, Hour= 7; 36.10°C, Hour= 8; -40.30°C, Hour= 9 Many hours have Wind Direction of 990. Change in DP Feb 21, -10.10°C, Hour=20. Many hours have Wind Direction of 990. Change in DP for Mar 12: -10.90°C, Hour= 7; -17.50°C, Hour=10; 13.30°C, Hour=11. Many hours have Wind Direction of 990. Zenith Luminance = -6.0CD/m² on 7/26 at 8 AM. Many hours have Wind Direction of 990. Many hours have Wind Direction of 990.
Table 10: Issues Identified in the TMYs
3
Missing wind direction indicator in EE format is 999.
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A summary of the issues found for each TMY file is shown in Table 10, below. Items listed in bold typeface should be fixed. These include wind directions of 990 degrees or zenith illuminance less than 0. The other items note significant step changes (>10°C) between hours in dry-bulb or dew point temperatures. The one major exception is NP, where in the early morning hours of March 14, the dew point is oscillating ±40°C. This is an obvious error in the source data and should be fixed. Appendix A shows, as an example, the .Audit output file for Auckland (AK). Some of the repeating errors about wind direction are omitted. The full .Audit file for AK can be provided on request. The .Stat file created by the weather processor is tab-delimited, making it easy to bring into a spreadsheet and plot the data. Four other critical checks can then be made of the monthly average diurnal patterns. These can reveal issues with incorrectly calculated solar radiation, time shifts, or incorrectly translated variables. Appendix B shows three graphs for TMY AK (Auckland) – Monthly Average Hourly Dry Bulb Temperature, Monthly Average Hourly Relative Humidity, and Monthly Average Hourly Direct Normal Solar Radiation; and a table of Monthly Average Wind Direction. Similar graphs for all 16 locations can be provided in separate spreadsheets. For the TMY AK, both the dry bulb temperature and relative humidity graphs show normal/typical diurnal patterns. The direct normal solar radiation patterns also appear correct. February (emphasized with a heavy weight line) shows a high shoulder and low noon pattern, which normally would indicate a problem with the calculations (usually it indicates that radians were not converted to degrees before the calculations were made). But given that all the other months exhibit a normal pattern (more of a bell shape), these data are likely the normal pattern—they may indicate a typical cloud cover moves over the location during the middle of the day. Other locations did not show this issue. The last critical item to check is the wind direction. If the wind direction has no values beyond South, then there is likely an issue with the conversion of Wind Direction to degrees. In all cases, Wind Direction seems to have been converted correctly—with the exception of the hours identified above that had values of 990—which may be a conversion of a code for ‗missing‘ in the source data. (The weather converter changed these to 270 degrees: 990 – 360 – 360 = 270.) Monthly Wind Direction % {N=0 or 360,E=90,S=180,W=270} Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec North 7 10 13 5 15 9 13 15 17 12 7 16 NorthEast 15 11 11 12 18 12 7 8 12 14 3 6 East 3 7 11 19 10 5 6 3 1 2 0 4 SouthEast 3 8 9 22 8 8 10 6 0 2 1 3 South 7 9 7 10 9 14 15 8 2 6 3 10 SouthWest 37 37 16 19 17 23 16 22 19 26 34 39 West 22 14 20 9 15 23 22 30 27 33 38 9 NorthWest 7 4 13 4 8 6 12 7 22 4 14 14 Table 11: Example Monthly Wind Direction for TMY AK (Auckland)
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Several other outputs from the weather processor include the .KML for use in Google Earth (see the image at the end of this review) and two composite spreadsheets—one showing the statistics, the other showing the issues identified from all 16 weather files. 4.3.
Conclusion
The NIWA TMY data set is well-constructed and robust. There are a few minor items (noted in Table 10) that should be corrected before the TMYs are released for widespread use. The spreadsheets, .Audit files, and revised EPWs can be provided in a separate .ZIP file.
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5. Manual Verification focused on ANZHERS 5.1.
Analysis of the TMY files
Subsequent to Section 4, this section was undertaken by Brett Stokes, a computer programmer and mathematician with detailed experience of the weather file creation program in Australia. An examination of particular weather elements was undertaken to ensure correctness of the TMY files. All New Zealand climate files were checked for errors of high relative humidity, low absolute moisture content and maintenance of the irradiance identity global = diffuse + direct. High Relative Humidity checks revealed a few sites with a few hours where relative humidity (calculated from absolute moisture, temperature and pressure) was just above 100%. No values greater than 100.6% were detected. Full details are presented in Appendix D. Low Absolute Moisture Content checks revealed no significant concerns. No zero or negative moisture values were detected. The only minor concern is one day at one site with three low values for moisture (the site of concern is NP, on the day 14th March 2006). See Appendix E for full details. Irradiation Component checks for ―significant deviation from Global=Diffuse+Direct‖ detected issues at only three sites (IN, NL, WN) where there are a large number of hours where solar altitude was not low (ie above 36degrees) but there was a greater than 10% deviation (up to 42%). The worst of these seem to be associated with very low or zero NormDirect. See Appendix F for full details. Further checks assessed the consistency between the RMY/TMY and in the supplied source data files. For each climate zone, one day was selected at random from each month. The representative (RMY/TMY) data for each hour of this day was checked against the corresponding data in the supplied source data files. The analysis incorporated Global Solar Radiation, Air Temperature, Air Pressure, Relative Humidity, Wind Speed, Wind Direction, Cloud Cover. Consistency was determined to be sufficient except for the following (examples are presented in Appendix G): In all cases, at times of non-zero Wind Speed4, Wind Direction values are in error by 1. It is possible that this error was produced by a miscalculation in translating from the source data (degrees East of North) into the required numeric descriptors (1=NNE, 2=NE, 3=ENE, 4=E, ...16=N). Many Wind Speed values are very slightly lower than expected. These values were rounded down (to the nearest lower tenth of a metre per second) rather than the expected (to the nearest tenth of a metre per second). This error affects some (but not all) files.
4
The zero value for "calm" is correct.
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Occasionally, a cloud cover value of 8 in the source data appears as 7 in the RMY/TMY. No pattern to this error was determined. Some actual/estimate flags are wrong. For example, flags for climate zone IN indicate Air Temperature estimates as actual measurements. Most wind data (both Wind Speed and Wind Direction) in climate zones NM and NP appears to be wrong. This error was apparent for all but one of the 12 days checked. There is no consistency between the RMY/TMY and the supplied wind source data file. This error may have been produced by errors in the supplied wind source data file, or because an alternative source data file was used for wind. Some Wind Directions are 9905. In climate zone IN, some Air Temperatures are inconsistent between RMY/TMY and source data files. It appears that Air Temperatures in source data are rounded to integer values (where the source is from years earlier than 2000AD), whereas the TMY/RMY data appears ―smooth‖. The overall shape of the Air Temperature diurnal curve is consistent between TMY/RMY and source data.
5.2.
Verification Through House Simulation
A rough correspondence between the New Zealand locations and similar Australian sites was determined according to latitude, altitude above sea level and proximity to the coast (see Table 12). NZ Kaitaia/Northland Auckland Tauranga Hamilton Rotorua Turangi/Taupo New Plymouth Napier Paraparaumu Nelson Queenstown Lauder Hokitika Christchurch Dunedin/Aero Invercargill
Lat -35.1 -37.0 -37.7 -37.8 -38.1 -39.0 -39.0 -39.5 -40.9 -41.3 -45.0 -45.0 -42.7 -43.5 -45.9 -46.4
Alt (m) 85 33 4 40 283 375 30 3 5 4 354 370 38 37 2 0
AUS Nowra Mt Gambier Mt Gambier Mt Gambier Melbourne Ballarat Cape Otway Low Head Low Head Low Head Launceston Launceston Hobart Hobart Hobart Hobart
Lat -35.0 -37.8 -37.8 -37.8 -37.8 -37.5 -38.9 -41.1 -41.1 -41.1 -41.4 -41.4 -42.8 -42.8 -42.8 -42.8
Alt (m) 109 63 63 63 112 436 20 4 4 4 171 171 4 4 4 4
Table 12: AUS and NZ position and winter severity comparison
5
The indicator for missing wind direction in EnergyPlus/ESP format is 999.
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The Australian equivalent climate files were applied to building simulations in AccuRate, a commercially available software package developed by the CSIRO. Simulation results of ―corresponding‖ climates were then compared. An indicative house meeting BCA Deemed-To-Satisfy 5 stars energy efficiency requirements in the Melbourne climate was selected as the basis of comparison, and a simulation model created in AccuRate. This was expanded to two sets of four models: the first set with suspended timber floors, the second with a concrete slab on ground; each was rotated to all four cardinal points of the compass with glazing areas adjusted to maintain the 5 star compliance. These eight models were simulated in the selected New Zealand climates and the results compared with those of the same house in the ―corresponding‖ Australian Climate. The evaluation compared total energy including the separate components of heating, sensible cooling and latent cooling (mostly negligible). Figure 8 presents the comparison of results for Auckland (NZ) and Mt Gambier (AUS). On average, the heating energy for Mt Gambier is 50% higher than that of Auckland in all of the eight models. The cooling energy requirements in Auckland and Mt Gambier do however (with the exception of the North-facing concrete model) correlate reasonably. Mt Gambier Vs Auckland 250
Energy MJ/m²
200
MG Cooling Energy
150
MG Heating Energy AK Cooling Energy 100
AK Heating Energy
50
0 N
E
S
W
Concrete
N
E
S
W
Timber
Figure 8: Mt Gambier and Auckland
The results for Hobart and Christchurch are presented in Figure 9. As in the Mt Gambier and Auckland comparison, a radical difference in overall space conditioning energy requirements are apparent between the Australian and New Zealand pairs. The difference is mostly attributed to heating energy demand. Heating energy in all eight models was 25-30% higher in Christchurch than Hobart. This could be attributed to the fact that Hobart is situated along an estuary and will therefore be more sheltered from wind than Christchurch, which is situated nearer to the coast. The correlation between cooling requirements is clearer. However, these are not as well correlated as those of Mt Gambier and Auckland.
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Hobart Vs Christchurch 300
Energy MJ/m²
250
HO Cooling Energy
200
HO Heating Energy CH Cooling Energy
150
CH Heating Energy 100
50
0 N
E
S
W
Concrete
N
E
S
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Timber
Figure 9: Hobart and Christchurch
Overall, the generalised comparisons above indicate nothing of concern aside from the difficulty of pairing New Zealand and Australian climates. 5.2.1.
Climate zones ranked by heating energy demand
The 16 New Zealand sites were compared to each other according to a similar methodology as that above. For clarity, the results were condensed by averaging the energy demand of the four orientations for each house configuration. The averaging process provides the advantage of diminishing differences in the solar and temperature components of the climates by diluting the passive solar performance advantage. Figure 10 to Figure 15 present the results of conditioning energy for the example house in all 16 New Zealand climates, ranked by heating, cooling, and total energy requirements respectively. Separate results have been plotted for the timber and the concrete floored archetypes.
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Sensible Cooling
Latent Cooling
400 350 300 250 200 150 100 50 0 North land Auck land Bay of Pl enty East Coas t Ham ilton New Plym outh Nels on-M arlbo roug h Well ingto n Roto rua Taup o Wes t Coa st Chris tchur ch Dune Quee din nstow n-La kes Inver carg ill Cent ral O tago
Energy (MJ/m2)
Heating
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Figure 10: Concrete slab, ranked by heating energy
Sensible Cooling
Latent Cooling
Taup o Wes t Coa st Chris tchur ch Dune Quee din nstow n-La kes Cent ral O tago Inver carg ill
400 350 300 250 200 150 100 50 0 North land Auck land Bay of Pl enty Ham ilton East Coas t New Plym Nels outh on-M arlbo roug h Well ingto n Roto rua
Energy (MJ/m2)
Heating
Figure 11: Timber floor, ranked by heating energy
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5.2.2.
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Climate zones ranked by cooling energy demand Heating
Sensible Cooling
Latent Cooling
Nels
Auck land on-M ar lbo roug h Ham ilton Cent ral O t ago New Plym outh T aup o Chr is t chur ch Quee nstow n-La kes Nor th land Welli ngt on Wes t Coa st Rot o rua Inver car gi ll Dune di n
enty
B ay
East
C oas
t
400 350 300 250 200 150 100 50 0 of Pl
Energy (MJ/m2)
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Figure 12: Concrete floor, ranked by total cooling energy
Sensible Cooling
Latent Cooling
400 350 300 250 200 150 100 50 0
East
C oas t B ay of Pl enty Auck land Nels on-M ar lbo roug h Ham ilton Cent ral O t ago New Plym outh T aup o Chr is t chur Quee ch nstow n-La kes Nor th land Wes t Coa st Welli ngt on Rot o rua Inver car gi ll Dune di n
Energy (MJ/m2)
Heating
Figure 13: Timber floor, ranked by total cooling energy
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5.2.3.
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Climate Zones ranked by total energy demand
Heating
Sensible Cooling
Latent Cooling
400 350 300 250 200 150 100 50 0 North land Auck land Bay of Pl enty Ham ilton New Plym outh East Coas Nels t on-M arlbo roug h Well ingto n Roto rua Wes t Coa st Taup o Chris tchur ch Dune din Inver carg Quee ill nstow n-La kes Cent ral O tago
Energy (MJ/m2)
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Figure 14: Concrete floor, ranked by total conditioning energy
Sensible Cooling
Latent Cooling
400 350 300 250 200 150 100 50 0 North land Auck land Bay of Pl enty Ham ilton New Plym outh East Coas t Well i n gton Nels on-M arlbo roug h Roto rua Wes t Coa st Taup o Chris tchur ch Dune din Inver carg Quee ill nstow n-La kes Cent ral O tago
Energy (MJ/m2)
Heating
Figure 15: Timber floor, ranked by total conditioning energy
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Conclusions and Recommendations Our several analyses have established that the 16 data sets are overall fit for the purposes of housing and other building energy analysis. While some errors have been detected, they are generally nuances in the context of the simulation of building energy performance simulation and even more so in a context of comparing alternative designs for the one climate. For example, while unlikely to materially affect the energy rating of a compliant house, if the software was used to thermally optimise a high performance design in a warm climate, the incorrect wind direction could provide misleading advice on the matter of ideal orientation. Accordingly, we recommend that the matters below be attended to at the time of the next update of the software and/or its climate data inputs: In several cases, representative climate files are derived from short records (as few as 12 years). With regard to this, the report should be explicit regarding likely error in the degree of reliability of the representative nature of those weather files. NIWA has adopted a new process for the generation of this weather data. The modified process deviates from that in published, peer-reviewed journals. The reasons for rejecting the published procedures should be thoroughly documented. The significance of the weighting factors applied to weather element in the formation of the TMY should be made explicit in the documentation of the files – preferably as part of the meta-data in the header files. Where local measurements are unavailable, the process of managing the generation of solar radiation data appears robust. The approaches to dealing with the perceived lack of clarity in the TMY manual seem clear and well documented. However, the requirement for the approach, which deviates from the published, peer reviewed technique, is not clear. The weather files should include WMO station numbers in the meta-data. This information is used by the EnergyPlus software to extract design conditions from the ASHRAE Fundamentals 2005 Handbook. A number of concerns in the NIWA TMY files in EnergyPlus/ESP-r (EE) format arose when processing the files through the EnergyPlus weather processor. The errors requiring attention are described in Table 9. Three sites (IN, NL, WN) have many potentially significant deviations from the irradiance identity Global = Diffuse + Direct. In all cases, at times of non-zero Wind Speed6, Wind Direction values are in error by 1. It is possible that this error was produced by a miscalculation in translating from the source data (degrees East of North) into the required numeric descriptors (1=NNE, 2=NE, 3=ENE, 4=E, ...16=N). Many Wind Speed values are very slightly lower than expected. These values were rounded down (to the nearest lower tenth of a metre per second) rather than the 6
The zero value for "calm" is correct.
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expected (to the nearest tenth of a metre per second). This error affects some (but not all) files. Occasionally, a cloud cover value of 8 in the source data appears as 7 in the RMY/TMY. No pattern to this error was determined. Some actual/estimate flags are wrong. For example, flags for climate zone IN indicate Air Temperature estimates as actual measurements. Most wind data (both Wind Speed and Wind Direction) in climate zones NM and NP appears to be wrong. This error was apparent for all but one of the 12 days checked. There is no consistency between the RMY/TMY and the supplied wind source data file. This error may have been produced by errors in the supplied wind source data file, or because an alternative source data file was used for wind. Some Wind Directions are 9907.
7
The indicator for missing wind direction in EnergyPlus/ESP format is 999.
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6. References Akasaka, H., H. Nimiya, et al. (2000). "Development of expanded AMeDAS weather data for building energy calculation in Japan." ASHRAE Transactions 106. The most popular dense array system for weather data acquisition in Japan is known as AMeDAS, which is the abbreviation for Automated Meteorological Data Acquisition System. However, the data from AMeDAS are not available for building energy calculation because of a lack of climatic elements and missing data, etc. Therefore, new weather data, expanded from the original AMeDAS data, have been developed for building energy calculations. The new weather data have been designated as Expanded AMeDAS weather data (EA weather data). EA weather data, including 842 stations throughout Japan, are available as hourly based weather data sets. EA weather data are composed of the following eight climatic elements: (1) atmospheric temperature, (2) humidity ratio, (3) horizontal global irradiation, (4) atmospheric irradiation, (5) wind direction, (6) wind speed, (7) precipitation, and (8) sunshine duration. There are two types of EA weather data: one is 15-year EA weather data (from 1981 to 1995); the other is Standard EA weather data. Standard EA weather data are similar to Test Reference Year (TRY) and Design Reference Year (DRY) data in European countries and Typical Meteorological Year (TMY) data in the U.S. They were selected from the 15-year EA weather data. Both types of EA weather data are available on five CD-ROMs. A program is also available that enables the user to calculate beam, diffuse, and tilted surface irradiation from horizontal global irradiation and underground temperature at a designated depth as well as to search for a target station and year from the five CD-ROMs. This paper outlines the EA weather data, including its compilation from the source data, treatment for missing data, data estimation and supplementation of non-observed climatic elements, and the selection method of Standard EA weather data. Barbaro, S., S. Costanzo, et al. (2003). Generation of a reduced temperature year as an input data for building climatization purposes: An application for Palermo (Italy). Sustainable World, Halkidiki. A reference year of the reduced temperature data, called reduced temperature year (RTY), was analyzed. The method was derived by Erbs et al. using data from nine American locations. The statistical analysis of data provided by three selected measurement stations helped in determining the local typical meteorological year (TMY). The results show that the methodology can be successfully extended to all the locations having an appropriate set of data temperature and that the reliability of the RTY can be tested through the comparison of the yearly energy demand. Chow, W. K. and S. K. Fong (1997). "Typical meteorological year for building energy simulation in Hong Kong." Architectural Science Review 40(1): 11-15. The Typical Meteorological Years (TMY) for Hong Kong is generated by using the weather data recorded by the Royal Observatory of Hong Kong from year 1979 to 1988. Data used in the analysis included the hourly dry bulb temperature, dew point temperature, wind velocity, total global solar radiation, etc. The monthly Cumulative Distribution Function (CDF) of those selected data was compared with the long term values of the CDF for each month of a year. The persistence structure was studied by Finkelstein-Schafer (FS) statistics. The deviations of CDF of the mean dry bulb temperature and total global solar radiation was examined. The weather data of the chosen typical year is compiled in TMY format. With this TMY, the building energy loads in a higher education institute in Hong Kong were simulated using the energy simulation program BLAST. The results were compared with the building energy loads of all the years from 1979 to 1988. Gugliermetti, F. and F. Bisegna (2003). "Meteorological days for HVAC system design in Mediterranean climate." Building and Environment 38(8): 1063-1074. In this paper several real days belonging to a Typical Meteorological Year (TMY) are proposed as design days for HVAC systems in summer conditions. A parameter, the equivalent temperature, that is a different combination of related values of hourly solar radiation, wet and dry bulb temperature, is defined. Real days with maximum values of these parameters are selected as Design Meteorological Days (DMDs) to evaluate
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summer cooling load. Results are analysed in terms of energetic performances of the system to identify the most suitable DMDs to be used in the design process. The DMD selection is based on the criterion of reducing HVAC part-load working periods and on the risk level to alter indoor design conditions. The approach uses the computer package Integrated ENergy Use Simulation (IENUS), specifically developed for the Mediterranean climate, to characterise both energetic and environmental aspects. Simulations are referred to a non-residential building designed on the base of an energy saving criteria. Typical Meteorological Years are used both as meteorological data inputs as far as data base to select DMDs. © 2003 Elsevier Science Ltd. All rights reserved. Gugliermetti, F., G. Passerini, et al. (2004). "Climate models for the assessment of office buildings energy performance." Building and Environment 39(1): 39-50. In the last few years many advanced computer packages, characterised by a considerable integration between thermal and visual aspects, were developed to support designers and to study building energy performance, innovative materials and daylight control strategies and systems. These packages, as a function of their complexity and final use, require different types of outdoor data, ranging from monthly (MTD) or seasonal typical days (STD) to more complex typical meteorological years (TMY). Both the deterministic and the stochastic components of outdoor data are present in TMYs, while MTDs and STDs take into account only the deterministic component. The use of MTDs or STDs produces a sensible reduction of the calculation time, above all appreciable in the first phase of the building design process, although it introduces an element of uncertainty in simulation results due to the absence of the stochastic component of outdoor data. This uncertainty is not easily predictable, as reported by many authors. The aim of the present work is to investigate the influence of the stochastic component of meteorological data in evaluating office building energy performance in Mediterranean climate. The study is performed by an advanced numerical computer package, Integrated ENergy Use Simulation (IENUS), which can process different types of climatic data. Different typologies, systems and space managements are investigated. © 2003 Published by Elsevier Ltd. Haberl, J. S., D. J. Bronson, et al. (1995). Impact of using measured weather data vs. TMY weather data in a DOE-2 simulation. ASHRAE Transactions, San Diego, CA, USA, ASHRAE. DOE-2 and BLAST hourly building energy simulation models are effective methods of simulating the energy use in new buildings during the design stage. Although these models are increasingly being used to evaluate retrofits in existing buildings and for evaluations of demand-side management (DSM), little agreement exists among the users as to how to calibrate the simulation to measured data from a building. Hence in this article, an attempt is made to evaluate the impact of using measured weather data vs. TMY weather data for a DOE-2 simulation of a large institutional building in central Texas. Results from the application of the procedure to simulated energy using TMY weather data and measured weather data repacked into TRY formats are compared to actual energy use. Additionally, specific recommendations concerning the impact of the use of measured weather data and the use of these analysis aids on the calibration of the DOE-2 program are presented. Hong, T., S. K. Chou, et al. (1999). "A design day for building load and energy estimation." Building and Environment 34(4): 469-477. We describe how a design day for building energy performance simulation can be selected from a typical meteorological year of a location. The advantages of the design day weather file are its simplicity and flexibility in use with simulation programs. The design day is selected using a weather parameter comprising the daily average dry bulb temperature and total solar insolation. The selection criterion addresses the balance between the need to minimise the part-load performance of the air-conditioning systems and plants and the number of hours of load not met. To validate the versatility of the design day weather file, we compare simulation results of the peak load and load profile of a building obtained from the DOE-2.1E code and a specially developed load estimation program, PEAKLOAD. PEAKLOAD is developed using the transfer function method and ASHRAE databases. Comparative results are in good agreement, indicating that a design day thus selected can be used when quick answers are required and simulations using a TMY file cannot be easily done or justified.
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Huang, J. (1998). Impact of different weather data on simulated residential heating and cooling loads. ASHRAE Transactions, Toronto, Can, ASHRAE. Since 1995, two major new sources of typical year weather data - ASHRAE's Weather Year for Energy Calculations, Version 2 (WYEC2), for 59 U.S. and Canadian locations and NREL's Typical Meteorological Year, Version 2 (TMY2), for 239 U.S. locations - have become available for use in building energy simulations. Both of these data sets represent several years of effort in correcting data anomalies and adding improved solar models to the earlier WYEC and TMY weather sets. Although it is straightforward to tabulate and compare the changes in climate statistics, e.g., degree-days, wind speed, average solar heat gain, etc., the impact that such changes have on the simulated energy consumption of a building is less clear. The purpose of this study is to use DOE-2 simulations of prototypical residential buildings to (1) determine the ability of various typical year weather data such as TMY2, TMY, WYEC2, WYEC, and TRY to reproduce the long-term average heating and cooling energy consumption when simulated using 30 years of historical weather data and (2) compare the simulated energy consumption from different typical year data and determine if there are systematic differences due to the type of weather data. Kalogirou, S. A. (2003). "Generation of typical meteorological year (TMY-2) for Nicosia, Cyprus." Renewable Energy 28(15): 2317-2334. The present paper presents the generation of a type 2 Typical Meteorological Year (TMY2) for Nicosia, Cyprus. This tool may be useful for the prediction and comparison of the performance of passive and active solar systems and for building thermal analysis. The present TMY-2 is generated from a simple TMY created in the past from available hourly meteorological data recorded during the period 1986-1992 using the Filkenstein-Schafer statistical method. The present TMY-2 contains much more data which leads to more accurate predictions especially in building simulations. This includes in addition to solar radiation values, illuminance, and other meteorological elements such as visibility, precipitation and snowfall records. © 2003 Elsevier Science Ltd. All rights reserved. Levermore, G. J. and N. O. Doylend (2002). North American and European hourly based weather data and methods for HVAC building energy analyses and design by simulation. ASHRAE Transactions, Honolulu, HI. Computer simulation of buildings and plants is being used increasingly in energy assessments and design. Simulation often requires hourly weather data. In the first part of this paper the North American WYEC2, WYEC1, TMY, TMY2, and IWEC selection methods are discussed and compared with the European Test Reference Years (TRYs) and Design Reference Years (DRYs). The latter are the basis of a new proposed standard, prEN ISO15927-4. It is proposed that the robust Finkelstein-Schafer statistic, used in some of these selection methods but not mentioned in prEN ISO15927-4, should be adopted as the basic selection tool. In the second part of the paper the lack of extensive near-extreme hourly data for plant sizing is discussed. ASHRAE 1, 3, 5, and 7 day period data and U.K. and European design summer years are considered. In conclusion, it is proposed that an extension of the Finkelstein-Schafer method be used for selecting more near-extreme hourly weather data. Massie, D. D. and J. F. Kreider (2001). "Comparison of and discrepancies between TMY and TMY2S predictions for simple photovoltaic and wind energy simulations." Journal of Solar Energy Engineering, Transactions of the ASME 123(1): 6-9. A new typical meteorological year (TMY2s) data set has been derived from the 1961-1990 National Solar Radiation Data Base (NSRDB). This paper compares PV and wind energy system simulation results using new TMY2s data with results using the original TMY data set. PV and aerogenerator simulations are compared in seven different climatic regions of the United States. Results indicate that PV simulations using TMY2s data provide higher energy values for cloudy regions where the clearness index is low and lower energy values for sunnier climates. TMY2s wind simulations produce lower energy predictions in nearly all cases tested.
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Mellit, A., M. Benghanem, et al. (2005). "A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach." Solar Energy 79(5): 469-482. The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 19912000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model. © 2005 Elsevier Ltd. All rights reserved. Pissimanis, D., G. Karras, et al. (1988). "GENERATION OF A 'TYPICAL METEOROLOGICAL YEAR' FOR THE CITY OF ATHENS." Solar energy 40(5): 405-411. In recent years, the evaluation of the efficiency of the performance of solar energy units is not done by using long-term averages of weather data as input but preferably by using data sets representative of the climatological features of the site that are generated for this purpose. Such data sets, which are usually called 'Test Reference Year' or 'Short Reference Year', consist mainly of solar radiation data, but they may also include other meteorological data, like temperature, wind velocity, etc, which may affect the response of the units. In this article, an attempt is made for the generation of such a representative data set for the city of Athens mainly by following a method that has been proposed by Hall et al. This data set, which includes global solar radiation data and six other meteorological parameters referring to temperature, dew point, and wind velocity, has been characterized by Hall as a 'Typical Meteorological Year'. Sawaqed, N. M., Y. H. Zurigat, et al. (2005). "A step-by-step application of Sandia method in developing typical meteorological years for different locations in Oman." International Journal of Energy Research 29(8): 723-737. This paper reports on the development of typical meteorological years (TMYs) for seven different locations in Oman based on measured meteorological data. Depending on the availability of data the TMYs developed using Sandia method used data covering 7-17 years. The method as implemented here in a step-by-step procedure with illustrations is made simple. The procedure described herein is computerized and can handle any number of data sets in an easy-to-use manner. This should facilitate the development of TMYs for any location where enough data is available. Sensitivity analysis of different weights assigned to different weather parameters shows that Sandia method is highly affected by solar flux even if its weight is reduced by half while the weights of other parameters such as temperature, wind, and relative humidity have less impact on the selection of TMY. Copyright © 2005 John Wiley & Sons, Ltd. Skeiker, K. (2007). "Comparison of methodologies for TMY generation using 10 years data for Damascus, Syria." Energy Conversion and Management 48(7): 2090-2102. The generation of a typical meteorological year (TMY) is of great importance for
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calculations concerning many applications in the field of thermal engineering. The need of an accurate TMY for simulations has been well recognized over the years. Various methods for deriving TMYs have been developed, but their final results can be significantly different. In this paper, the major methodologies reported in the literature were applied to 10 year hourly measurements of weather data from Damascus, Syria. The TMYs obtained were evaluated according to their impact on the typical Syrian building's thermal system in order to decide which method should be recommended for generating typical meteorological years and for predicting the performance of thermal systems in buildings. Based on simulation results for seasonally, monthly and daily building thermal loads, three widely used statistical estimators, namely, root mean square difference RMSD, total standard error SEE and chi square ?2 were calculated to assess the performance of each TMY. The findings showed that the TMY giving the closest performance to the average performance of the building's thermal system as predicted using the 10 year weather data is the one generated by using the modified Sandia method. This method gives sufficiently accurate results compared with the other methods reported in the literature. © 2007 Elsevier Ltd. All rights reserved. Stoffel, T. L. and M. D. Rymes (1998). Production of the weather year for energy calculations version 2 (WYEC2) data files. ASHRAE Transactions, Toronto, Can, ASHRAE. Representative climate data are important for comparing computer simulations of building designs and their resultant energy needs. In 1969, recognizing a growing interest in such data, ASHRAE Technical Committee 4.2 - Weather Information (TC4.2), commissioned development of hourly weather files. The resulting data for 51 locations in the U.S. and Canada are called Weather Year for Energy Calculations (WYEC). In 1988, TC4.2 initiated a major revision of the WYEC files. This effort was limited to adding the 26 Typical Meteorological Year (TMY) hourly weather files to the original WYEC database and making many significant improvements and enhancements to the available data elements. Specifically, the need existed to use a consistent time convention, correct excessive solar radiation values, screen the meteorological data for physically impossible values, provide model estimates of additional solar irradiance and illuminance values, and include data quality indicators. The work of revising and improving the WYEC database was done at the National Renewable Energy Laboratory (NREL). The resulting set of 77 revised and corrected hourly weather files are known as WYEC Version 2 or WYEC2 files. This paper describes the NREL production of the WYEC2 data files for ASHRAE. Talbert, S. G., K. E. Herold, et al. DEVELOPING HOURLY WEATHER DATA FOR LOCATIONS HAVING ONLY DAILY WEATHER DATA, Minneapolis, MN, USA, American Solar Energy Soc. A methodology was developed to modify an hourly TMY weather tape to be representative of a location for which only average daily weather parameters were available. Typical hourly and daily variations in solar flux, and other parameters, were needed to properly exercise a computer model to predict the transient performance of a solar controlled greenhouse being designed for Riyadh, Saudi Arabia. Yang, H. and L. Lu (2004). Study of typical meteorological years and their effect on building energy and renewable energy simulations. ASHRAE Transactions, Nashville, TX. This paper investigates the generation of typical meteorological years (TMYs) and example weather years (EWYs)for Hong Kong, and studies their effects on the simulation results of the performance of building energy and renewable energy systems, i.e., solar and wind energy systems. According to various methodologies, different TMYs and EWYs were calculated using Hong Kong's weather data from the past 22 years. The results were used for a building energy simulation and for a hybrid solar-wind energy simulation, as two case studies, to study their effect on the simulation results. To validate the effect, deviations of the simulation results for different methods were compared. The results show that the difference could be very significant: -20% for hybrid solar-wind energy systems and a relatively smaller difference for building energy systems, ±5%. A larger error was produced by using EWYs compared with TMYs for both the building energy simulation and the hybrid solar-wind energy simulation. This proves that generating the right TMY is important for meeting different needs and various application systems. ©2004.
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Yang, H. and L. Lu (2004). Study of typical meteorological years and their effect on building energy and renewable energy simulations. 2004 Annual Meeting - Technical and Symposium Papers, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Nashville, TN. This paper investigates the generation of typical meteorological years (TMYs) and example weather years (EWYs) for Hong Kong, and studies their effects on the simulation results of the performance of building energy and renewable energy systems, i.e., solar and wind energy systems. According to various methodologies, different TMYs and EWYs were calculated using Hong Kong's weather data from the past 22 years. The results were used for a building energy simulation and for a hybrid solar-wind energy simulation, as two case studies, to study their effect on the simulation results. To validate the effect, deviations of the simulation results for different methods were compared. The results show that the difference could be very significant: -20% for hybrid solar-wind energy systems and a relatively smaller difference for building energy systems, ±5%. A larger error was produced by using EWYs compared with TMYs for both the building energy simulation and the hybrid solar-wind energy simulation. This proves that generating the right TMY is important for meeting different needs and various application systems. Yang, L., J. C. Lam, et al. (2007). "Analysis of typical meteorological years in different climates of China." Energy Conversion and Management 48(2): 654-668. Typical meteorological years (TMYs) for 60 cities in the five major climatic zones (severe cold, cold, hot summer and cold winter, hot summer and warm winter, mild) in China were investigated. Long term (1971-2000) measured weather data such as dry bulb and dew point temperatures, wind speed and global solar radiation were gathered and analysed. A total of seven climatic indices were used to select the 12 typical meteorological months (TMMs) that made up the TMY for each city. In general, the cumulative distribution functions of the TMMs selected tended to follow their long term counterparts quite well. There was no persistent trend in any particular years being more representative than the others, though 1978 and 1982 tended to be picked most often. This paper presents the work and its findings. Future work on the assessment of TMYs in building energy simulation is also discussed. © 2006 Elsevier Ltd. All rights reserved. Yang, L., J. C. Lam, et al. (2008). "Building energy simulation using multi-years and typical meteorological years in different climates." Energy Conversion and Management 49(1): 113-124. Detailed hourly energy simulation was conducted for office buildings in the five major climate zones - severe cold, cold, hot summer and cold winter, mild and hot summer and warm winter - in China using multi-year (1971-2000) weather databases as well as typical meteorological years (TMY). The primary aim was to compare the energy simulation results from the TMY with those from individual years and their long term means. A total of 154 simulation runs were performed. Building heating and cooling loads, their components and energy use for heating, ventilation and air-conditioning were analysed. Predicted monthly load and energy consumption profiles from the TMY tended to follow the long term mean quite closely. Mean bias errors ranged from -4.3% in Guangzhou to 0% in Beijing and root-mean-square errors from 3% in Harbin to 5.4% in Guangzhou. These percentages were not always the smallest compared with the 30 individual years, however, they are at the lower end of the percentage error ranges. This paper presents the work and its findings. © 2007 Elsevier Ltd. All rights reserved.
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Appendix A. Example of the .Audit file for Auckland -Input File Type=EPW, with FileName=TMY_AK_EE.epw -Out of Range Data items will NOT be corrected. -***Definitions file=tmy_ak_ee.def Def (Definitions) Location Processed Status Location WMO (InWMO) 931190 Def (Definitions) Weather Data Processed Status Weather data read -- end of file, no data processed. Def (Definitions) Misc Data Processed Status Misc Data - Comments 1 NIWA TMY AK – Rodney-North Shore CityWaitakere City-Auckland City-Manukau City-Papakura-FranklinThames-Coromandel. Misc Data - Comments 2 Typical Meteorological Years for the New Zealand Home Energy Rating Scheme August 2007 Period of record varies_ generally 1991-2007. Copyright Energy Misc Data - Source Data NIWA Def (Definitions) Data Control Data Processed Status Note: Data Control only used in "Custom" input formats DataControl data read -- end of file, no data processed. -***Processing Definitions file complete. Warning ** Did not find WMO for matching with Design Conditions, looking for WMO=C74082 Note ** Location Definitions taken from associated "def" file. Note ** Comment line 1 taken from associated "def" file. ReadCheck: WindDir changed from 990 to 270 deg. on date= 7/31 at hour= 6 Warning ** Diffuse Horizontal Illuminance= 81061.0lux on date=11/11 at hour=14 Warning ** Missing Data Found on Source Weather Data File ** Missing (and corrected) Visibility, Number of items= 1 ** Missing (and corrected) Aerosol Optical Depth, Number of items= 8760 ** Missing (and corrected) Days Since Last Snow, Number of items= 8760 - Start Date/End Date for Weather Source Start Date=Jan 1; End Date=Dec 31 - Actual Data Years for Monthly Data** Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2007 2006 2002 2001 2006 2005 2002 2002 2002 2003 2003 1997 - ** Not all weather data sources represent contiguous years. - ** Monthly data values may come from different years. - Days per Month for Monthly Data** Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 31 28 31 30 31 30 31 31 30 31 30 31 - ** Number of days per month seems okay.
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- Data Sources should be checked for relevancy to these statistics. Average Delta DB Change = 0.68°C; Std Dev = 0.67°C Average Delta DP Change = 0.63°C; Std Dev = 0.57°C Average Delta Relative Humidity Change = 3.29%; Std Dev = 3.34% Average Delta Wind Speed Change = 0.97m/s ; Std Dev = 0.93m/s Hourly Dry Bulb temperature change trigger = minimum of 10.04°C and 10.0°C 10.04°C = calculated trigger based on mean change in dry-bulb temperature and standard deviation shown above 10.0°C = trigger set by user
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Appendix B. Graphical Diurnal Analysis of Auckland Monthly Average Hourly Dry Bulb Temperatures 25
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Figure 16: Monthly average hourly dry bulb temperatures - Auckland
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Figure 17: Monthly average hourly relative humidity - Auckland
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Monthly Average Hourly Direct Normal Solar Radiation 600
Direct Normal Solar Radiation (Wh/m2)
500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
400
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00 1:0
:0
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Figure 18: Monthly average hourly direct normal solar radiation - Auckland
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Appendix C. Ecotect Analysis of Climates Invercargil
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -46.4° LONGITUDE: 168.3° TIMEZONE: +12.0 hrs
NAME: Invercargill LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: Not Available
8
12
10
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8
Wind 3pm
6
4
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Wind 9am
T E M P
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DEGREE HOURS (Heating, Cooling and Solar)
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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Dunedin
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -45.9° LONGITUDE: 170.5° TIMEZONE: +12.0 hrs
NAME: Dunedin LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 2.0 m
8
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Wind 3pm
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
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Lauder
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -45.0° LONGITUDE: 169.7° TIMEZONE: +12.0 hrs
NAME: Lauder LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 370.0 m
8
12
10
I R R A D
8
Wind 3pm
6
4
2
0
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T E M P
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20
10
0
100
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60
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6k
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8k
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S H
C 0
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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+61 2 6260 6173
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Queenstown
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -45.0° LONGITUDE: 168.7° TIMEZONE: +12.0 hrs
NAME: Queenstown LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 354.0 m
8
12
10
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8
Wind 3pm
6
4
2
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T E M P
40
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DEGREE HOURS (Heating, Cooling and Solar)
8k
6k
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S H
0
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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+61 2 6260 6173
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Rotorua
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -38.1° LONGITUDE: 176.3° TIMEZONE: +12.0 hrs
NAME: Rotorua LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 283.0 m
8
12
10
I R R A D
8
Wind 3pm
6
4
2
0
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Wind 9am
T E M P
40
30
20
10
0
100
500
80
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60
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40
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6k
4k
0
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8k
S
2k H 0k
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June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
Page 47
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Peer Review of New Zealand Climate Data: Interim Report
Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Turangi
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -39.0° LONGITUDE: 175.8° TIMEZONE: +12.0 hrs
NAME: T urangi LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 375.0 m
8
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8
Wind 3pm
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T E M P
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DEGREE HOURS (Heating, Cooling and Solar)
8k
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2k H C
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Peer Review of New Zealand Climate Data: Interim Report
Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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+61 2 6260 6173
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Paraparaumu
CLIMATE SUMMARY
0
LATITUDE: -40.9° LONGITUDE: 175.0° TIMEZONE: +12.0 hrs
NAME: Paraparaumu LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 5.0 m
D A Y L T
4
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8
Wind 3pm
6 4 2 0
50
Wind 9am
T E M P
40 30 20 10 0
100
500
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20
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DEGREE HOURS (Heating, Cooling and Solar)
8k
6k
4k S 2k H 0
J
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J
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CLIMATE SUMMARY
0
D A Y L T
4
C J
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LATITUDE: -41.3° LONGITUDE: 174.8° TIMEZONE: +12.0 hrs
NAME: W ELLINGT ON LOCATION: NZ L DESIGN SKY: Not Available ALTITUDE: 67.0 m
8
12
10
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8
Wind 3pm
6 4 2 0
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Wind 9am
T E M P
40 30 20 10 0
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June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Peer Review of New Zealand Climate Data: Interim Report
Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Christchurch
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -43.5° LONGITUDE: 172.6° TIMEZONE: +12.0 hrs
NAME: CHRIST CHURCH LOCATION: NZ L DESIGN SKY: Not Available ALTITUDE: 34.0 m
8
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8
Wind 3pm
6 4 2 0
50
Wind 9am
T E M P
40 30 20 10 0
100
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60
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DEGREE HOURS (Heating, Cooling and Solar)
8k
6k
4k
20
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
O
N
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June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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+61 2 6260 6173
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Auckland
CLIMATE SUMMARY
0
D A Y L T
4
LATITUDE: -37.0° LONGITUDE: 174.8° TIMEZONE: +12.0 hrs
NAME: Auckland LOCATION: New Z ealand DESIGN SKY: Not Available ALTITUDE: 33.0 m
8
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Wind 3pm
6 4 2 0
50
Wind 9am
T E M P
40 30 20 10 0
100
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80
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60
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40
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6k
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20 0
100
J
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0
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4
DEGREE HOURS (Heating, Cooling and Solar)
8k
S
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LATITUDE: -37.0° LONGITUDE: 174.8° TIMEZONE: +12.0 hrs
NAME: AUCKLAND LOCATION: NZ L DESIGN SKY: Not Available ALTITUDE: 6.0 m
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6 4 2 0
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Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
Average Temperature (°C)
Maximum Temperature (°C)
Minimum Temperature (°C)
Relative Humidity (%)
Direct Solar Radiation (W/ m²)
Diffuse Solar Radiation (W/ m²)
Average Wind Speed (km/ h)
Average Cloud Cover (%)
Average Daily Rainfall (mm)
+61 2 6260 6173
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June 11, 2008 S:\Energy Strategies\P710 - NZ-EECA Climate Data\Documents\Peer Review New Zealand Climate Data Report.doc
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Appendix D. Results of High Relative Humidity Checks reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_AK_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = AK020711 2 106 801007 25 2700000 0 0 0 01102
1.00026053686655 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_AK_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 1 ... ThisLineRelHumZeroToOneMax= 1.00026053686655 ... PercentFoulUps = .01 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_BP_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = BP950313 9 2101581004 87 1700000 136134 337 602
1.00035079976835 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = BP05100322 131 951004 3611700000 0 0 0 02122
1.00187539325796 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_BP_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 2 ... ThisLineRelHumZeroToOneMax= 1.00187539325796 ... PercentFoulUps = .02 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CH_AR.txt ... finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_CH_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 0 ... ThisLineRelHumZeroToOneMax= .995392993163722 ... PercentFoulUps = 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CO_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = CO020211 5 108 85 961 15 3100001 2 2 0 01152
1.00002506624768 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_CO_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 1 ... ThisLineRelHumZeroToOneMax= 1.00002506624768 ... PercentFoulUps = .01 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_DN_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = DN00030422 130 931017 2512800001 0 0 0 02202
1.00031094273469 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_DN_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 1 ... ThisLineRelHumZeroToOneMax= 1.00031094273469 ... PercentFoulUps = .01 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_EC_AR.txt ...
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finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_EC_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 0 ... ThisLineRelHumZeroToOneMax= .995002704273039 ... PercentFoulUps = 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_HN_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = HN980106 1 99 761011 11 3000000 0 0 0 01622
1.00031911986215 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980106 2 95 741011 8 2000000 0 0 0 01482
1.00080141620072 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980106 6 129 931012 11 2700000 122 77 177141072
1.00192213768973 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980107 5 122 891008 6 3700000 30 19 174 31152
1.00054075756126 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980120 4 1651181009 413700000 0 0 0 01242
1.00091771434363 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980121 3 1641171012 6 7800000 0 0 0 01352
1.0018963358566 ...
too high relative humidity ....ThisLineRelHumZeroToOne = HN980121 6 1721231012 18 3700000 19 18 2121062
1.00020633599644 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980121 9 1781281013 38 3700000 102102 047 782
1.00239698959381 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980131 0 2031491019 1115800000 0 0 0 01802
1.00105061201674 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = HN980131 4 1931401018 1114600001 0 0 0 01232
1.00116357689216 ...
ThisLine =
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_HN_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 50 ... ThisLineRelHumZeroToOneMax= 1.00239698959381 ... PercentFoulUps = .57 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_IN_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = IN980425 8 84 681020 5 2600001 72 70 0 7 612
1.0002916630978 ...
too high relative humidity ....ThisLineRelHumZeroToOne = IN981130 3 80 671008 15 1700000 0 0 0 01382
1.00095668566022 ...
ThisLine = ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_IN_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 2 ... ThisLineRelHumZeroToOneMax= 1.00095668566022 ... PercentFoulUps = .02 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NL_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = NL970211 7 1671201006 2014700000 136131 2717 942
1.00172492885326 ...
too high relative humidity ....ThisLineRelHumZeroToOne = NL960509 7 1511091002 58 7700011 8 9 0 3 662
1.0055664357539 ...
ThisLine = ThisLine =
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too high relative humidity ....ThisLineRelHumZeroToOne = NL96052123 165120 992 4113700000 0 0 0 02212
1.00041707852158 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL960522 8 172126 989 5616700000 69 73 011 542
1.00084185171562 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL950905 9 1501081000 3613600001 194198 030 532
1.00092594600664 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL95090510 1501081000 4114500000 436320 13839 382
1.00092594600664 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL950906 9 160116 993108 1700000 44 45 031 532
1.00001028047004 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL950925 5 112 831011 15 5800000 0 0 0 0 972
1.00069540399366 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL950929 4 130 95 997 15 5700000 0 0 0 01072
1.00141090473648 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = NL950930 4 1401011001 20 1600000 0 0 0 01082
1.00061043625027 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_NL_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 12 ... ThisLineRelHumZeroToOneMax= 1.0055664357539 ... PercentFoulUps = .13 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NM_AR.txt ... finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_NM_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 0 ... ThisLineRelHumZeroToOneMax= .991294564453732 ... PercentFoulUps = 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NP_AR.txt ... finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_NP_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 0 ... ThisLineRelHumZeroToOneMax= .996839074310361 ... PercentFoulUps = 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_QL_AR.txt ... finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_QL_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 0 ... ThisLineRelHumZeroToOneMax= .990316867903174 ... PercentFoulUps = 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_RO_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = RO960301 3 140103 982 514400000 0 0 0 01262
1.00073916790121 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO960301 6 150110 983 1011400000 63 24 429 5 952
1.00181413528153 ...
ThisLine =
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too high relative humidity ....ThisLineRelHumZeroToOne = RO960303 1 188142 973108 2700001 0 0 0 01582
1.0009944435051 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO960303 2 190144 972103 2700001 0 0 0 01402
1.0011550410264 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO960303 3 190144 971113 2700001 0 0 0 01262
1.00012504612822 ...
too high relative humidity ....ThisLineRelHumZeroToOne = RO960312 9 218171 977 4210200011 568117 74637 591
1.0011415790713 ...
too high relative humidity ....ThisLineRelHumZeroToOne = RO960319 9 170126 976 51 1700000 66 66 035 572
1.00026828457002 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO960322 6 160118 977 30 3700000 0 0 0 1 882
1.00054524360879 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO96032216 180135 972 92 1700000 13 13 0212882
1.00064086364966 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = RO960328 6 140102 992 5 6700000 0 0 0 0 862
1.00127349432536 ...
ThisLine =
ThisLine = ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_RO_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 12 ... ThisLineRelHumZeroToOneMax= 1.00181413528153 ... PercentFoulUps = .13 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_TP_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = TP060129 5 194147 976 10 8700000 2 2 18 01132
1.00050058989806 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP970204 1 125 96 954 20 4700001 0 0 0 01622
1.0003770859591 ...
too high relative humidity ....ThisLineRelHumZeroToOne = TP970204 3 134102 954 10 5700001 0 0 0 01332
1.00121118209868 ...
too high relative humidity ....ThisLineRelHumZeroToOne = TP98060717 110 85 975 41 3700000 2 2 0 02942
1.0011996748355 ...
too high relative humidity ....ThisLineRelHumZeroToOne = TP98060915 145108 969 1012700000 52 49 16133142
1.00166759231326 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP980610 3 109 85 968 10 3500001 0 0 0 0 962
1.00063572969413 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP98061313 132 99 969 56 5700000 61 61 0253412
1.00047313843361 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP98061316 133100 966 56 5700000 11 10 4 43042
1.00074208056299 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP980614 1 153115 958 77 2800001 0 0 0 01302
1.00043501734929 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = TP98061415 162122 958 1511700000 86 81 18133152
1.00085473553753 ...
ThisLine =
ThisLine = ThisLine = ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_TP_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 24 ... ThisLineRelHumZeroToOneMax= 1.00197416620701 ... PercentFoulUps = .27 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WC_AR.txt ...
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too high relative humidity ....ThisLineRelHumZeroToOne = WC000930 1 120 881007 10 3800001 0 0 0 01582
1.00157199212638 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WC000930 3 120 881006 36 1800001 0 0 0 01252
1.00057738240232 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_WC_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 2 ... ThisLineRelHumZeroToOneMax= 1.00157199212638 ... PercentFoulUps = .02 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WN_AR.txt ... too high relative humidity ....ThisLineRelHumZeroToOne = WN940105 3 160116 995 87 2800000 0 0 0 01362
1.00202439986675 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN94011012 1901381013 8216700000 147137 0713582
1.00085407234835 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN94012023 1801291017 46 1800000 0 0 0 01962
1.00138041224179 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN940121 0 1801291016 36 1800000 0 0 0 01802
1.00039577073516 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN96020714 1801301008 56 2700000 80 76 5543062
1.00005506782527 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN960219 3 2001481006 3616700000 0 0 0 01322
1.00017862080363 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN960219 7 2061541006 30 1700000 16 15 018 892
1.00193130251203 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN960219 8 2001481006 61 1700000 75 70 229 792
1.00017862080363 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN960219 9 2001481006 61 1700000 94 91 040 672
1.00017862080363 ...
ThisLine =
too high relative humidity ....ThisLineRelHumZeroToOne = WN96021911 2101581005 6116700000 305275 1157 302
1.00134716510676 ...
ThisLine =
finished reading in from the data file okay...C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008-TMYar\TMY_WN_AR.txt . . . NumberOfLinesReadIn = 8760 ... NumberOfRelHumTooHighsReadIn= 23 ... ThisLineRelHumZeroToOneMax= 1.00202439986675 ... PercentFoulUps = .26
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Appendix E. Results of Low Moisture Values Checks only concern hear is that one site has three low values on one day no other values less than 3 detected no zero values detected site of concern is NP, on 14th March 2006: too low moisture read in ....value = 2 ... NP060314 1 85 21023 20 7100001 0 0 0 01592 too low moisture read in ....value = NP060314 5 70 11022 25 7200000 0
1 ... 0 0 01022
too low moisture read in ....value = 2 ... NP060314 7 110 21022 30 6200000 172 38 60312 832 ================================================================================= reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_AK_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_BP_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CH_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CO_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_DN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_EC_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_HN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_IN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NL_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0
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reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NM_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NP_AR.txt ... too low moisture read in ....value = NP060314 1 85 21023 20 7100001 0
2 ... 0 0 01592
too low moisture read in ....value = NP060314 5 70 11022 25 7200000 0
1 ... 0 0 01022
too low moisture read in ....value = 2 ... NP060314 7 110 21022 30 6200000 172 38 60312 832 finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 3 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_QL_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_RO_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_TP_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WC_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0 reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 ... NumberOfMoistureTooLowsReadIn= 0
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Appendix F. Results of Irradiance Checks looking at points where solar altitude is above 36 degrees, 3 sites have issues (IN, NL, WN), max error >> 10% the other 13 sites look perfect ... max error < 3% for WN: months with issues are 9401 9602 9703 0504 9509 9510 0311 9712 so no issues with may to august (also april is not so bad) -= winter is okay ??? for NL: months with issues are 0201 9702 0703 0204 0108 9509 0010 9611 so no issues with may to july (also december is okay) -= winter is okay ??? for IN: months with issues are 0601 9602 9509 9510 9811 so no issues with march to august (also december is okay) -= winter is okay ??? all are flag 2 = ? measured global and estimated diffuse (and estimated direct presumably) all three "maxerrors" have zero or very low direct IN is most southerly zone WN is at south tip of north island NL is most northerly zone =========================================================== reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_AK_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 1.9 TheMaxErrorLine=AK060221 9 2201121015 2011400000
647 70 87940 692
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_BP_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.1 TheMaxErrorLine=BP020924 9 170 981017 6113400000
522 45 77437 532
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CH_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 1.9
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TheMaxErrorLine=CH96022615 200 721015 77 4200000
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611 86 816393002
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_CO_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.3 TheMaxErrorLine=CO050108 9 109 83 956 3313700000
44 43
043 772
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_DN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2 TheMaxErrorLine=DN06022515 201 681014 7311400000
683 59 970393042
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_EC_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.2 TheMaxErrorLine=EC03120915 170 881013 51 7700000
61 59
1432752
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_HN_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.1 TheMaxErrorLine=HN041108 8 178 851019 1410400000
650 80 90438 802
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_IN_AR.txt ... IN060101 9 148 711009 5111700000
375256
6643 772
536455
2561 412
597504
1666 112
602491
13653372
PercentErrorInGlobal = 19.7 ... SolarAltInDegrees = 43 ... IN06010111 152 691008 5112700000 PercentErrorInGlobal = 11 ... SolarAltInDegrees = 61 ... IN06010112 153 691007 3611700000 PercentErrorInGlobal = 13.1 ... SolarAltInDegrees = 66 ... IN06010113 154 691006 3611700000 PercentErrorInGlobal = 16.5 ... SolarAltInDegrees = 65 ... IN06010216 130 63 98320013700000
438235 186402782
PercentErrorInGlobal = 19.1 ... SolarAltInDegrees = 40 ... IN06010415 111 64 989 9213400000
436275 138502922
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PercentErrorInGlobal = 12.7 ... SolarAltInDegrees = 50 ... IN06010416 102 65 989 8714400000
330262
52402792
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 40 ... IN060105 9 102 63 996 7215700000
313201 10843 772
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 43 ... IN06010512 108 65 996 8214700000
447302 10565 122
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 65 ... IN06010514
97 62 996 9713700000
213174
19593112
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 59 ... IN06010616 141 70100911813700000
572336 277402792
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 40 ... IN060109 9 142 631022 9213700000
516253 30842 772
PercentErrorInGlobal = 11 ... SolarAltInDegrees = 42 ... IN06011014 216 901018 2016700000
711317 366583132
PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 58 ... IN06011210 136 541018 10 6400000
408183 23651 622
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 51 ... IN06011516
96 60100315913700000
119100
0402812
PercentErrorInGlobal = 16 ... SolarAltInDegrees = 40 ... IN06011814 115 641002 8213700000
574299 255573152
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 57 ... IN06011815 124 55100212813700000
522296 216492962
PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 49 ... IN06012010 134 691022 9713700000
461293 15850 612
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 50 ... IN06012014 148 771021 9712400000
908168 766573162
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 57 ... IN06012016 141 721020 8212400000
483308 158392822
PercentErrorInGlobal = 15.6 ... SolarAltInDegrees = 39 ... IN06012112 190 661015 41 1400000
855378 43362 142
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PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 62 ... IN06012113 197 681015 36 1700000
944464 427623432
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 62 ... IN06012214 2231061012 46 9400000
561375 133573172
PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 57 ... IN06012715 185 941015 8212200000
780 88 825482992
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 48 ... IN06012716 185 911015 8212200000
622 97 713382842
PercentErrorInGlobal = 13.8 ... SolarAltInDegrees = 38 ... IN06012816 190 961017 5612400000
402302
91382842
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 38 ... IN060129 9 2021031018 15 2200000
502195 38338 742
PercentErrorInGlobal = 14.2 ... SolarAltInDegrees = 38 ... IN06012910 2131091017 3611200000
669152 60048 602
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 48 ... IN06012912 2151101016 2512200000
744378 33660 142
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 60 ... IN96020110 1701001005 20 5700000
341305
047 592
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 47 ... IN96020111 1801061005 20 5700000
688427 22255 402
PercentErrorInGlobal = 11.5 ... SolarAltInDegrees = 55 ... IN960202 9 200115 993 25 2400000
141122
037 732
238213
047 592
PercentErrorInGlobal = 13.5 ... SolarAltInDegrees = 37 ... IN96020210 220114 994 1516400000 PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 47 ... IN96020211 210107 995 7710400000
388244 11155 402
PercentErrorInGlobal = 13.7 ... SolarAltInDegrees = 55 ... IN96020213 210107 995 25 6400000
472305 111593452
PercentErrorInGlobal = 15.2 ... SolarAltInDegrees = 59 ... IN96020215 190100 994 4111700000
277213
0473002
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+61 2 6260 6173
PercentErrorInGlobal = 23.1 ... SolarAltInDegrees = 47 ... IN96020216 190100 994 1512700000
211183
0372862
380336
054 392
636488
8359 142
PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 37 ... IN96020511 150 711010 9213700000 PercentErrorInGlobal = 11.6 ... SolarAltInDegrees = 54 ... IN96020512 160 71101011813700000 PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 59 ... IN96020513 160 71101011313700000
658366 250593462
PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 59 ... IN96020616 150 611016 6112700000
472152 444372872
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 37 ... IN96020711 160 661013 46 8700000
386336
053 392
466397
058 142
377305
2753 382
219183
0463022
PercentErrorInGlobal = 13 ... SolarAltInDegrees = 53 ... IN96020712 160 711013 61 8700000 PercentErrorInGlobal = 14.8 ... SolarAltInDegrees = 58 ... IN96020811 150 751013 56 6700000 PercentErrorInGlobal = 13.4 ... SolarAltInDegrees = 53 ... IN96020815 130 661013103 6700000 PercentErrorInGlobal = 16.4 ... SolarAltInDegrees = 46 ... IN96021013 210 861016 20 3700000
611397 166573462
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 57 ... IN96021210 1601001002 2012700000
75 61
044 562
116 91
052 372
144122
0523232
352275
2743 552
455397
0513232
PercentErrorInGlobal = 18.7 ... SolarAltInDegrees = 44 ... IN96021211 1601001002 15 4700000 PercentErrorInGlobal = 21.6 ... SolarAltInDegrees = 52 ... IN96021314 180 991011 46 9700000 PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 52 ... IN96021510 140 661018 56 8700000 PercentErrorInGlobal = 16.6 ... SolarAltInDegrees = 43 ... IN96021514 140 611017 56 8700000
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+61 2 6260 6173
PercentErrorInGlobal = 12.7 ... SolarAltInDegrees = 51 ... IN96021813 160 881006 6612700000
216183
0543472
205183
0503242
261192
38503242
388323
3842 532
PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 54 ... IN96021814 160 881006 5612700000 PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 50 ... IN96022014 100 571014 3012700000 PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 50 ... IN96022210 130 651022 3616700000 PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 42 ... IN96022414 150 861022 8212700000
550336 200483252
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 48 ... IN95090912
90 491010 7213600000
466247 27737
42
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 37 ... IN95091013 100 48101710312700000
311275
0373452
PercentErrorInGlobal = 11.6 ... SolarAltInDegrees = 37 ... IN95091212 140 641021 7715400000
300244
2739
42
475366
83383442
PercentErrorInGlobal = 13 ... SolarAltInDegrees = 39 ... IN95091313 130 671023 8712500000 PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 38 ... IN95091412 150 801020 2516300000
280244
239
42
PercentErrorInGlobal = 12.4 ... SolarAltInDegrees = 39 ... IN95091413 160 851018 2515300000
447244 252383442
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 38 ... IN95091511 100 631016 8212600000
83 64
237 232
PercentErrorInGlobal = 21.4 ... SolarAltInDegrees = 37 ... IN95091512 100 651016 7712600000
111 94
040
32
411183 27740
32
PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 40 ... IN95091612
90 421028 2011300000
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 40 ... IN95091711
90 491025 6112500000
466186 36138 232
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+61 2 6260 6173
PercentErrorInGlobal = 12.4 ... SolarAltInDegrees = 38 ... IN95091712
90 491024 4112700000
488305 19140
32
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 40 ... IN95091911 120 641024 4113400000
394259 12539 232
PercentErrorInGlobal = 14.3 ... SolarAltInDegrees = 39 ... IN95092212
90 57 995 6112700000
147122
242
32
194152
5373242
419369
044
PercentErrorInGlobal = 16.1 ... SolarAltInDegrees = 42 ... IN95092214 100 62 995 6113700000 PercentErrorInGlobal = 20.1 ... SolarAltInDegrees = 37 ... IN95092512
70 431025 92 6700000
32
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 44 ... IN95092514
80 441025 92 6600000
375305
27383232
411336
27383222
PercentErrorInGlobal = 14.2 ... SolarAltInDegrees = 38 ... IN95092714 130 691014 2010500000 PercentErrorInGlobal = 14.2 ... SolarAltInDegrees = 38 ... IN95092910
90 531020 41 5500000
358247 11637 412
PercentErrorInGlobal = 11.5 ... SolarAltInDegrees = 37 ... IN95100212 110 541013 1510500000
597168 50246
22
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 46 ... IN95100411
70 571002 92 5700000
180152
045 232
108 91
047
PercentErrorInGlobal = 15.6 ... SolarAltInDegrees = 45 ... IN95100412
80 611001 77 6700000
22
PercentErrorInGlobal = 15.7 ... SolarAltInDegrees = 47 ... IN95100414
84 63 999 76 6700011
38 30
0403202
355305
0473392
375305
041 432
400336
5423192
PercentErrorInGlobal = 21.1 ... SolarAltInDegrees = 40 ... IN95100713 170 801006 3014600000 PercentErrorInGlobal = 14.1 ... SolarAltInDegrees = 47 ... IN95100810 140 761001 41 1300000 PercentErrorInGlobal = 18.7 ... SolarAltInDegrees = 41 ... IN95100914 100 621005 61 8400000
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+61 2 6260 6173
PercentErrorInGlobal = 15.2 ... SolarAltInDegrees = 42 ... IN95101113 160 751002 9215700000
372305
33483382
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 48 ... IN95101213 100 521007 7712600000
611275 363483382
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 48 ... IN95101214
99 501007 8412300010
536213 388433182
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 43 ... IN95101513 120 58 99410313500000
433336
69493372
52 30
0372992
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 49 ... IN95101815
80 47100310312500000
PercentErrorInGlobal = 42.3 ... SolarAltInDegrees = 37 ... IN95102012 180 701000 7716700000
530305 20253
02
PercentErrorInGlobal = 12 ... SolarAltInDegrees = 53 ... IN95102013 180 79 99912815500000
361216 111513362
PercentErrorInGlobal = 16.3 ... SolarAltInDegrees = 51 ... IN95102014 180 88100012814600000
138122
2453152
75 61
0372992
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 45 ... IN95102015 170 83100111315600000 PercentErrorInGlobal = 18.7 ... SolarAltInDegrees = 37 ... IN95102114 110 51101510312200000
730 94 777463152
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 46 ... IN95102210 120 62101010313200000
741 61 83346 452
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 46 ... IN95102212 120 62100910311200000
622226 38854
02
PercentErrorInGlobal = 13.2 ... SolarAltInDegrees = 54 ... IN95102213 120 62100910313300000
555213 333523362
PercentErrorInGlobal = 14.3 ... SolarAltInDegrees = 52 ... IN95102215 110 62100912813500000
352152 225382982
PercentErrorInGlobal = 17.5 ... SolarAltInDegrees = 38 ... IN951024 9 100 521015 6611500000
555293 32538 622
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+61 2 6260 6173
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 38 ... IN95102410 110 561015 6612600000
658268 43346 462
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 46 ... IN95102614 140 711014 8211400000
675244 472473132
PercentErrorInGlobal = 12.7 ... SolarAltInDegrees = 47 ... IN951027 9 110 561016 6112500000
433336
5539 632
419336
2747 462
PercentErrorInGlobal = 14.4 ... SolarAltInDegrees = 39 ... IN95102710 110 551016 5112500000 PercentErrorInGlobal = 15.1 ... SolarAltInDegrees = 47 ... IN95102711 120 591016 6112400000
655427 19453 252
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 53 ... IN951028 9 130 711014 8210400000
519305 23339 632
PercentErrorInGlobal = 13 ... SolarAltInDegrees = 39 ... IN95102810 130 661014 7712500000
600366 22247 462
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 47 ... IN95102815 110 571014 6112600000
313275
0392962
PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 39 ... IN951030 9 170 881001 61 1700000
569213 44439 632
PercentErrorInGlobal = 13.5 ... SolarAltInDegrees = 39 ... IN95103010 180 951000 9216700000
511339 13848 472
PercentErrorInGlobal = 13.6 ... SolarAltInDegrees = 48 ... IN95103014 120 69 999 5113700000
108 91
0483122
141122
040 642
238213
048 472
247213
0483122
PercentErrorInGlobal = 15.7 ... SolarAltInDegrees = 48 ... IN951031 9
80 551003 77 9700000
PercentErrorInGlobal = 13.5 ... SolarAltInDegrees = 40 ... IN95103110
80 531004 92 8700000
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 48 ... IN95103114
80 491007 77 8700000
PercentErrorInGlobal = 13.8 ... SolarAltInDegrees = 48 ... IN98110315 120 531013 36 9700000
472342 108412942
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+61 2 6260 6173
PercentErrorInGlobal = 12.5 ... SolarAltInDegrees = 41 ... IN98110414 120 621007 9213700000
605464
97503112
PercentErrorInGlobal = 11 ... SolarAltInDegrees = 50 ... IN98110512 170 571012 5613700000
675400 24159
02
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 59 ... IN98110515 180 661011 1010700000
497354 105412942
PercentErrorInGlobal = 14.9 ... SolarAltInDegrees = 41 ... IN98110714 140 661022 4612400000
752134 683503102
PercentErrorInGlobal = 12.6 ... SolarAltInDegrees = 50 ... IN98111014 110 75101514913700000
527388 102513092
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 51 ... IN98112112 130 571009 4112700000
522372 10563
12
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 63 ... IN98112515 110 531009 9712700000
633449 166452902
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 45 ... IN98112612 100 401020 46 9700000
844504 28064
22
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 64 ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 42.3 TheMaxErrorLine=IN95101815
80 47100310312500000
52 30
0372992
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NL_AR.txt ... NL02010515 199 85 999 7012700001
388287
2492752
PercentErrorInGlobal = 25.6 ... SolarAltInDegrees = 49 ... NL02010811 202 781005 2016700001
738485 18371 512
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 71 ... NL02010814 198 771003 3713700001
511449
2612892
291253
2372672
PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 61 ... NL02010816 187 871002 4714700001 PercentErrorInGlobal = 12.6 ... SolarAltInDegrees = 37 ...
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NL020112 9 207124 985 4816400000
+61 2 6260 6173
455281 16647 842
PercentErrorInGlobal = 11.6 ... SolarAltInDegrees = 47 ... NL02011413 216130 991 7415700001
447394
8713152
394314
5372682
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 71 ... NL02011416 207128 991 8315700001 PercentErrorInGlobal = 19.5 ... SolarAltInDegrees = 37 ... NL02011512 212120 994 9613400001
797375 33375
52
PercentErrorInGlobal = 12.6 ... SolarAltInDegrees = 75 ... NL02011814 227137 999 3515600001
363323
2612922
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 61 ... NL02011911 2071081000 7811400001
833262 51969 492
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 69 ... NL02012216 224 981013 3214200001
250183
2372702
586488
8703212
363290
2492812
PercentErrorInGlobal = 26.3 ... SolarAltInDegrees = 37 ... NL02012413 2331091010 23 3700001 PercentErrorInGlobal = 15.4 ... SolarAltInDegrees = 70 ... NL02012415 2261101009 4314700001 PercentErrorInGlobal = 19.7 ... SolarAltInDegrees = 49 ... NL02012512 2451081009 20 4700001
533385
5073
72
PercentErrorInGlobal = 18.8 ... SolarAltInDegrees = 73 ... NL02012513 2341161009 2913700001
361317
2703212
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 70 ... NL02012610 2301071009 65 5700000
550403 10557 682
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 57 ... NL02012612 2371081008 68 5700001
602482
4473
72
PercentErrorInGlobal = 12.9 ... SolarAltInDegrees = 73 ... NL02012614 2311031009 64 5700001
455378
2602962
PercentErrorInGlobal = 16.5 ... SolarAltInDegrees = 60 ... NL02012912 2461071008 78 5700001
988317 60072
82
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 72 ...
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NL02013011 2491101009 60 5400000
+61 2 6260 6173
750366 33066 462
PercentErrorInGlobal = 11 ... SolarAltInDegrees = 66 ... NL02013110 2391021009 52 5700000
711385 29156 672
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 56 ... NL97020915 2131001007 5212400011
697103 708472872
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 47 ... NL97021610 2121251014 41 5700000
544284 23852 602
PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 52 ... NL07030915 2371181006 56 3700001
616275 411392972
PercentErrorInGlobal = 13.4 ... SolarAltInDegrees = 39 ... NL07031014 2351001007 57 4700001
577265 327493132
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 49 ... NL07031015 2341011007 57 4700001
527201 397392972
PercentErrorInGlobal = 14.5 ... SolarAltInDegrees = 39 ... NL07031314 203121 988 9313400001
480299 177483142
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 48 ... NL07031315 198112 988 9012400001
402259 152382982
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 38 ... NL07031612 204 911008 5711700001
630381 20256
22
PercentErrorInGlobal = 12.9 ... SolarAltInDegrees = 56 ... NL07031811 222 971004 8914700000
619479
9752 272
422314
50443162
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 52 ... NL07032314 2121051015 54 6400001 PercentErrorInGlobal = 17.4 ... SolarAltInDegrees = 44 ... NL07032411 2371001014 59 5500001
602385 20250 252
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 50 ... NL07032413 2131071013 65 4500001
472348
69513372
PercentErrorInGlobal = 14.9 ... SolarAltInDegrees = 51 ... NL07032510 2221191010 64 5600001
530265 29443 442
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 43 ...
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NL07032614 2301091008 61 5400001
419330
+61 2 6260 6173
44433172
PercentErrorInGlobal = 14.1 ... SolarAltInDegrees = 43 ... NL07033111 2171201000 4913700000
611320 30848 232
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 48 ... NL02040413 186 861008 4612700001
394330
5463372
422351
13393192
PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 46 ... NL02040814 181 751001 4911700001 PercentErrorInGlobal = 14.9 ... SolarAltInDegrees = 39 ... NL02041010 195 781015 45 7200000
552103 62238 382
PercentErrorInGlobal = 12 ... SolarAltInDegrees = 38 ... NL02041210 207 931020 49 5400000
469278 23038 382
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 38 ... NL02041211 208 971019 49 5400000
472333 11644 202
PercentErrorInGlobal = 12.4 ... SolarAltInDegrees = 44 ... NL02041412 212 911015 26 4700000
347278
11453592
388339
11403392
PercentErrorInGlobal = 17.6 ... SolarAltInDegrees = 45 ... NL02042113 199 921012 59 3600001 PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 40 ... NL01080512 143 681013 3413400001
275168 12737
02
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 37 ... NL01082412 151 821001 8214700001
375320
13433592
327284
13373202
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 43 ... NL01083114 167 88 993 40 2700001 PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 37 ... NL95091914 157 701012 43 9300011
525183 430423132
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 42 ... NL95092612 1861071013 62 1400011
594406 152553542
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 55 ... NL95093013 184 961000 44 1600011
341259
55533282
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 53 ...
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Peer Review of New Zealand Climate Data: Interim Report
NL001001 9 1861061011 6315700000
297256
039 592
438326
8051 452
350281
5042 622
338265
2542 622
+61 2 6260 6173
PercentErrorInGlobal = 13.8 ... SolarAltInDegrees = 39 ... NL00100710 161 901012 8913400000 PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 51 ... NL001008 9 1711031012 5913700000 PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 42 ... NL001010 9 153 691011 23 6700000 PercentErrorInGlobal = 16.6 ... SolarAltInDegrees = 42 ... NL00101310 145 671003 6210400000
574360 19153 462
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 53 ... NL00101311 154 631003 6111400001
508403
4160 222
PercentErrorInGlobal = 13.7 ... SolarAltInDegrees = 60 ... NL001021 9 163 561015 44 6700000
633391 24745 662
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 45 ... NL00102110 166 601015 54 6700000
622458 10256 492
PercentErrorInGlobal = 12.8 ... SolarAltInDegrees = 56 ... NL00102115 161 691014 30 6600001
222168
0382852
322268
1956 492
552464
16603172
463397
5502972
311278
0392842
466363
4765 242
430369
8512972
PercentErrorInGlobal = 24.3 ... SolarAltInDegrees = 38 ... NL00102210 161 601015 37 7200000 PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 56 ... NL00102413 175 761008 50 9700001 PercentErrorInGlobal = 13.4 ... SolarAltInDegrees = 60 ... NL00102414 165 761007 5610700001 PercentErrorInGlobal = 13.4 ... SolarAltInDegrees = 50 ... NL00102415 158 731007 5610700001 PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 39 ... NL00102611 180 871011 40 9700001 PercentErrorInGlobal = 13 ... SolarAltInDegrees = 65 ... NL00102614 201 821010 1610700001 PercentErrorInGlobal = 12.7 ... SolarAltInDegrees = 51 ...
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NL001027 9 195 921012 21 5400000
+61 2 6260 6173
513256 27547 682
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 47 ... NL00102711 195 961012 28 5400001
250223
065 242
416357
0402832
422320
2558 522
441357
866 252
419372
2402832
438388
2623142
361317
2522942
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 65 ... NL00102715 191 921010 54 3400001 PercentErrorInGlobal = 14.2 ... SolarAltInDegrees = 40 ... NL00102810 1741091009 36 3700000 PercentErrorInGlobal = 19.1 ... SolarAltInDegrees = 58 ... NL00102811 1901111009 44 3700001 PercentErrorInGlobal = 17.4 ... SolarAltInDegrees = 66 ... NL00102815 197 981007 54 3700001 PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 40 ... NL96110113 153 621006 5210700011 PercentErrorInGlobal = 11 ... SolarAltInDegrees = 62 ... NL96110114 151 631006 4910600011 PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 52 ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 26.3 TheMaxErrorLine=NL02012216 224 981013 3214200001
250183
2372702
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NM_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.1 TheMaxErrorLine=NM02092614 108 741009 56 1700000
47 46
0403142
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_NP_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.8 TheMaxErrorLine=NP03092810 140 98 996 61 3700000
36 35
045 392
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_QL_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760
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+61 2 6260 6173
TheMaxPercentError= 2.1 TheMaxErrorLine=QL04031011 150103 951 1511700000
47 46
044 302
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_RO_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 1.9 TheMaxErrorLine=RO94110215 210 28 981 25 9400001
616 78 875372812
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_TP_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.3 TheMaxErrorLine=TP00101112
93 72 960 66 3700001
44 43
0573472
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WC_AR.txt ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 2.8 TheMaxErrorLine=WC02121011 150 981000 92 1700001
36 35
066 362
reading in from the data file C:\Documents and Settings\Brett Stokes\Desktop\ACDB - NZ2008TMYar\TMY_WN_AR.txt ... WN94010111 190 941002 8715600000
633339 23668 362
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 68 ... WN94010112 180 931002 6615600000
516403
58723552
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 72 ... WN94010113 183 951002 6114600000
563397 113673162
PercentErrorInGlobal = 11 ... SolarAltInDegrees = 67 ... WN94010114 1901001002 7214500000
761495 216582942
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 58 ... WN94010115 1801001001 8215500000
547357 172472802
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 47 ... WN940102 8 170100100210314600000
347253
8638 882
PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 38 ... WN940102 9 180100100210314500000 PercentErrorInGlobal =
455262 18349 772
12.1 ...
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SolarAltInDegrees =
Peer Review of New Zealand Climate Data: Interim Report
+61 2 6260 6173
49 ...
WN94010211 1801001001 9714600000
608363 19768 362
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 68 ... WN94010213 188 94100111314500000
691333 280673172
PercentErrorInGlobal = 14.5 ... SolarAltInDegrees = 67 ... WN94010313 177 741006 7215700000
655415 150673172
PercentErrorInGlobal = 15.6 ... SolarAltInDegrees = 67 ... WN94010414 220122100312816700000
636436 127582952
PercentErrorInGlobal = 14.5 ... SolarAltInDegrees = 58 ... WN94010511 180 87100011315100000
688271 37568 362
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 68 ... WN94010512 190 87100111815400000
805259 469713562
PercentErrorInGlobal = 12.7 ... SolarAltInDegrees = 71 ... WN94010513 180 871003 8215500000
611339 216673182
PercentErrorInGlobal = 12 ... SolarAltInDegrees = 67 ... WN94010515 190 941004 9215200000
713268 502472802
PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 47 ... WN940106 8 170 801014 5116500000
486186 41137 882
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 37 ... WN940107 8 1901121014 56 1700000
44 39
037 882
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 37 ... WN94010811 2101371015 56 1600000
733531 12267 362
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 67 ... WN94010812 2101371015 51 1600000
733580
75713572
550446
5267 362
605464
80713572
69 61
048 762
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 71 ... WN94010911 2101201014 6115700000 PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 67 ... WN94010912 2101131014 7715700000 PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 71 ... WN940110 9 2001291012108 1700000 PercentErrorInGlobal =
11.6 ...
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48 ...
WN940111 9 190 801018 56 9600000
483342 11648 762
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 48 ... WN94011412 2101371017 61 1400000
794540 172703592
PercentErrorInGlobal = 11.6 ... SolarAltInDegrees = 70 ... WN940115 9 180 861010 6616700000
497363 11147 762
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 47 ... WN94011514 190 931008 7715700000
469339
97572982
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 57 ... WN94011811 190 741019 8216600000
697461 17766 362
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 66 ... WN94011814 190 741018 5114500000
816183 655572992
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 57 ... WN94011815 180 801018 6614600000
405247 141472842
PercentErrorInGlobal = 13.6 ... SolarAltInDegrees = 47 ... WN94011912 210113101410314600000
572421
7769
02
455391
19573002
405299
8846 752
630488
6665 362
602507
27653252
619507
3856 592
591488
2568
438354
4155 592
PercentErrorInGlobal = 13.8 ... SolarAltInDegrees = 69 ... WN94012114 2101291016 36 2700000 PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 57 ... WN940122 9 2001281018 7716700000 PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 46 ... WN94012211 2201371018 7715700000 PercentErrorInGlobal = 13 ... SolarAltInDegrees = 65 ... WN94012213 2321171018 8214700000 PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 65 ... WN94012310 2331071017 56 1600000 PercentErrorInGlobal = 13 ... SolarAltInDegrees = 56 ... WN94012412 2001131010 5110600000
12
PercentErrorInGlobal = 13.5 ... SolarAltInDegrees = 68 ... WN94012610 172 681013 92 8600000 PercentErrorInGlobal =
11.5 ...
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SolarAltInDegrees =
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55 ...
WN94012611 190 611013103 8400000
850268 55064 352
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 64 ... WN94012615 190 651015103 8300000
766192 658462862
PercentErrorInGlobal = 13.1 ... SolarAltInDegrees = 46 ... WN94012711 150 461022 82 9500000
813403 35063 352
PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 63 ... WN94013010 185 911015 82 2700000
561436
8054 582
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 54 ... WN94013012 200 991015 87 2700000
780568 13066
12
PercentErrorInGlobal = 12 ... SolarAltInDegrees = 66 ... WN94013013 194 851015 77 1700000
672501 102633282
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 63 ... WN94013110 190 971014 41 2700000
344268
5054 572
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 54 ... WN94013112 200 991014 36 1700000
708381 28066
22
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 66 ... WN94013113 1801001014 6110600000
552394 113633282
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 63 ... WN960203 9 2101301006 51 1200000
583186 48043 722
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 43 ... WN96020310 2101301006 51 3400000
625333 25554 572
PercentErrorInGlobal = 13.7 ... SolarAltInDegrees = 54 ... WN96020311 2101301006 46 2400000
505375
7762 342
344290
19452892
477360
7243 712
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 62 ... WN96020315 2101381005 46 1700000 PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 45 ... WN960205 9 210 711014 3614700000 PercentErrorInGlobal = 14.2 ... SolarAltInDegrees = 43 ... WN96020612 2001121019 46 1400000 PercentErrorInGlobal =
416278 10064
22
11.6 ...
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SolarAltInDegrees =
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+61 2 6260 6173
64 ...
WN96021113 2301121017 41 1700000
744507 186603312
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 60 ... WN96021114 2201121017 36 2700000
494406
47533082
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 53 ... WN96021414 2201291012 4616700000
611293 294523092
PercentErrorInGlobal = 14.1 ... SolarAltInDegrees = 52 ... WN96021415 2201291011 46 1400000
486204 325422922
PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 42 ... WN96021610 190 861014 3612400000
825174 73650 522
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 50 ... WN96022112 160 571020 56 8700000
597351 18359
12
PercentErrorInGlobal = 14.9 ... SolarAltInDegrees = 59 ... WN96022115 170 481021 87 7400000
433363
27412952
430345
47503112
PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 41 ... WN96022214 190 851019 4111700000 PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 50 ... WN960224 9 160 661024 15 6400000
405256 13339 652
PercentErrorInGlobal = 16.1 ... SolarAltInDegrees = 39 ... WN97031214 180 531020 72 7400000
702177 647433162
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 43 ... WN97031612 204 831004 7715200001
811 76 850503592
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 50 ... WN97031613 210 941004 6115200000
741 73 791483362
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 48 ... WN05040311 184 931017 61 2700000
375314
542 182
419360
5393562
250216
2433332
PercentErrorInGlobal = 15.4 ... SolarAltInDegrees = 42 ... WN05041512 168 751022 2516700000 PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 39 ... WN95091713 143 801024 51 1700000 PercentErrorInGlobal =
13.1 ...
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SolarAltInDegrees =
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43 ...
WN95091812 125 651022 46 8500000
383290
52463542
PercentErrorInGlobal = 14.5 ... SolarAltInDegrees = 46 ... WN95092210 130 811013 61 1300000
250152 10242 362
PercentErrorInGlobal = 11.9 ... SolarAltInDegrees = 42 ... WN95092411 154 761017 77 2700000
577372 19748 162
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 48 ... WN95092810 170 751017 82 2500000
550265 30844 362
PercentErrorInGlobal = 12.9 ... SolarAltInDegrees = 44 ... WN95093012 136 801014 6111600000
525430
50513522
413311
72403112
302232
1651 152
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 51 ... WN95093014 160 811012 5111600000 PercentErrorInGlobal = 13.5 ... SolarAltInDegrees = 40 ... WN95100211 160 991011 30 9400000 PercentErrorInGlobal = 19.1 ... SolarAltInDegrees = 51 ... WN95101814 140 611019 87 1500000
500354 133453042
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 45 ... WN95101910 130 571017 66 9700000
558345 19752 402
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 52 ... WN95102212 130 701012 46 1500000
630464 119593472
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 59 ... WN951026 9 160 811017 41 1400000
552418
9445 602
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 45 ... WN95102610 170 751017 36 1300000
736424 28654 422
PercentErrorInGlobal = 11 ... SolarAltInDegrees = 54 ... WN95103112 150 991005 72 2500000
105 79
0623462
PercentErrorInGlobal = 24.8 ... SolarAltInDegrees = 62 ... WN03110113 165 741002 56 1600001
663385 252573182
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 57 ... WN031103 8 141 63 998 6615200000 PercentErrorInGlobal =
458143 42237 762
13.3 ...
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SolarAltInDegrees =
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+61 2 6260 6173
37 ...
WN031103 9 144 63 998 6615300001
602250 37247 632
PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 47 ... WN03110310 151 67 998 7215500001
650287 32756 442
PercentErrorInGlobal = 14.1 ... SolarAltInDegrees = 56 ... WN03110311 151 64 998 5615700000
763330 39162 182
PercentErrorInGlobal = 11.5 ... SolarAltInDegrees = 62 ... WN03110312 152 63 998 6115400000
644378 216633452
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 63 ... WN03110313 148 61 998 6115700000
494348 105573172
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 57 ... WN031104 8 130 601004 51 9400000
419119 38037 762
PercentErrorInGlobal = 17 ... SolarAltInDegrees = 37 ... WN031104 9 134 601005 51 8200000
491189 29147 632
PercentErrorInGlobal = 18.2 ... SolarAltInDegrees = 47 ... WN03110411 134 571007 66 9700000
675275 36363 182
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 63 ... WN03111010 160 741017 82 1700000
583403 14158 472
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 58 ... WN03111214 173 92100410315700000
491366
91502952
508388
72603142
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 50 ... WN03111513 163 86100410814200000 PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 60 ... WN03111514 162 86100411814400000
588397 161512952
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 51 ... WN03111515 163 90100411815700000
636440 202402812
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 40 ... WN03111611 163 741011 7216700000
613446 11366 202
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 66 ... WN03111612 161 751011 77 1400000 PercentErrorInGlobal =
647473 113673442
10.8 ...
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SolarAltInDegrees =
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+61 2 6260 6173
67 ...
WN03111613 165 781011 72 1700000
602464
86613142
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 61 ... WN031118 8 120 501030 66 9700001
550146 49739 812
PercentErrorInGlobal = 16.6 ... SolarAltInDegrees = 39 ... WN03112110 166 851012 92 2700000
600427 12760 512
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 60 ... WN03112112 178 881011 82 1700000
713366 277683442
PercentErrorInGlobal = 12.6 ... SolarAltInDegrees = 68 ... WN03112510 149 661012 4112700000
644491 10061 522
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 61 ... WN03112514 159 711010 3613700000
697467 186532922
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 53 ... WN03112715 143 69 995 25 9700000
422284 136432792
PercentErrorInGlobal = 10.7 ... SolarAltInDegrees = 43 ... WN971201 8 169 601008 6116500001
472281 21940 842
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 40 ... WN97120114 164 631011 66 1200001
594381 177542912
PercentErrorInGlobal = 11.8 ... SolarAltInDegrees = 54 ... WN971202 8 146 661007 5616700000
77 67
040 842
427369
1361 552
366320
2703452
158137
0643112
363317
11542912
147119
0432782
PercentErrorInGlobal = 13 ... SolarAltInDegrees = 40 ... WN97120210 151 721006 7716500000 PercentErrorInGlobal = 10.9 ... SolarAltInDegrees = 61 ... WN97120212 157 751005 72 1700000 PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 70 ... WN97120213 156 781004 66 1700000 PercentErrorInGlobal = 13.3 ... SolarAltInDegrees = 64 ... WN97120214 161 821004 8716700000 PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 54 ... WN97120215 160 861003 7716700000 PercentErrorInGlobal =
19 ...
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SolarAltInDegrees =
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43 ...
WN97120313 183104 998 6615700000
111 94
0643112
PercentErrorInGlobal = 15.3 ... SolarAltInDegrees = 64 ... WN971208 8 154 711018 36 1400000
500232 33840 862
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 40 ... WN97121011 174 781009 72 1700000
561415
9469 292
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 69 ... WN97121012 184 791008 8216500000
775446 255713472
PercentErrorInGlobal = 11.3 ... SolarAltInDegrees = 71 ... WN97121013 174 771008 8216700000
658403 177653122
PercentErrorInGlobal = 14.4 ... SolarAltInDegrees = 65 ... WN97121215 172 681018 8216600000
613296 352452782
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 45 ... WN97121311 175 691021 6616300001
769433 27769 302
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 69 ... WN97121314 182 781021 6116100000
905342 566562912
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 56 ... WN97121315 182 771021 6116200000
580333 250452782
PercentErrorInGlobal = 12.1 ... SolarAltInDegrees = 45 ... WN971214 8 175 801022 61 2700000
325256
5540 872
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 40 ... WN971214 9 181 801022 66 1200000
786314 50051 752
PercentErrorInGlobal = 10.6 ... SolarAltInDegrees = 51 ... WN97121410 189 811022 72 1200001 1002201 80061 592 PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 61 ... WN97121710 187 871015 66 1700000
669388 22761 592
PercentErrorInGlobal = 12.3 ... SolarAltInDegrees = 61 ... WN97121711 193 821016 66 1500000 1050247 74469 322 PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 69 ... WN97121712 194 781015 66 1700000 PercentErrorInGlobal =
838473 277723492
12.1 ...
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SolarAltInDegrees =
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72 ...
WN97121714 191 811014 61 1100000
775354 380562912
PercentErrorInGlobal = 13.7 ... SolarAltInDegrees = 56 ... WN97121715 188 821014 46 2700000
488348 108462782
PercentErrorInGlobal = 12.8 ... SolarAltInDegrees = 46 ... WN97121812 198 891013 66 1300000
708330 319723492
PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 72 ... WN97121815 188 891011 8215100000
466290 150462782
PercentErrorInGlobal = 14.6 ... SolarAltInDegrees = 46 ... WN97121912 207 991013 41 1300000
830488 263723502
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 72 ... WN97121913 204 971014 41 1400001
658476 119663132
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 66 ... WN97122011 1981141015 46 2700000
69 61
069 332
PercentErrorInGlobal = 11.6 ... SolarAltInDegrees = 69 ... WN97122013 2071101013 61 1500000
716461 191663132
PercentErrorInGlobal = 11.2 ... SolarAltInDegrees = 66 ... WN97122015 2071071011 51 2500000
497284 191462782
PercentErrorInGlobal = 15.2 ... SolarAltInDegrees = 46 ... WN97122111 177 831017 30 9600000
858397 40069 332
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 69 ... WN97122112 182 831018 41 9700000
775608
80723502
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 72 ... WN97122113 186 831018 41 9700000
808516 219663132
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 66 ... WN971222 8 176 871021 1513700000
325271
3339 882
316268
22462782
469372
4761 612
PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 39 ... WN97122315 2121141011 61 1700000 PercentErrorInGlobal = 10.2 ... SolarAltInDegrees = 46 ... WN97122410 219 881011 41 2400000 PercentErrorInGlobal =
11.9 ...
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SolarAltInDegrees =
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61 ...
WN97122511 197 971002 7215300001
780433 26369 342
PercentErrorInGlobal = 13 ... SolarAltInDegrees = 69 ... WN97122513 194 941002 8715200001
969210 716673142
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 67 ... WN97122514 196 871002 5616300001
938168 791572922
PercentErrorInGlobal = 11.4 ... SolarAltInDegrees = 57 ... WN97122515 191 811001 6116400001
733149 688462782
PercentErrorInGlobal = 12.2 ... SolarAltInDegrees = 46 ... WN97122715 184 711014 5615700000
475287 177462792
PercentErrorInGlobal = 12.8 ... SolarAltInDegrees = 46 ... WN971228 8 178 851009 77 2700000
277226
3638 882
PercentErrorInGlobal = 10.4 ... SolarAltInDegrees = 38 ... WN971229 8 135 801006 5610600000
433296 13838 882
PercentErrorInGlobal = 12 ... SolarAltInDegrees = 38 ... WN97122912 144 791008 61 9600000
844608 150723532
PercentErrorInGlobal = 11.1 ... SolarAltInDegrees = 72 ... WN97122913 149 771009 56 9700000
613519
30673152
522449
22572932
PercentErrorInGlobal = 10.8 ... SolarAltInDegrees = 67 ... WN97122914 149 751009 46 9600000 PercentErrorInGlobal = 10.5 ... SolarAltInDegrees = 57 ... WN97122915 154 761009 51 9400001
602394 200472792
PercentErrorInGlobal = 10.3 ... SolarAltInDegrees = 47 ... WN97123113 184 801017 2512700000
647360 241673162
PercentErrorInGlobal = 10.1 ... SolarAltInDegrees = 67 ... WN97123115 181 841017 2511700000
430305 102472792
PercentErrorInGlobal = 11.7 ... SolarAltInDegrees = 47 ... finished reading in from the data file okay... NumberOfLinesReadIn= 8760 TheMaxPercentError= 24.8 TheMaxErrorLine=WN95103112 150 991005 72 2500000
105 79
0623462
=====================================================================
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Appendix G. Results of Consistency Checks Examples of the errors discovered in ad-hoc checks for consistency are described below. Further examples exhibiting similar errors are likely to be found in the data set. Error: Wind Direction in error by 1 260 degrees is West ... should be 12 not 13 ... Whoops = wind direction AK07012419 2091301010 3613700000 Whoops = wind direction 1962 20070124:0800 260 3.603 Whoops = wind direction 13 versus 12 diff = 1 Error: Whoops Whoops Whoops
11
Wind Speed incorrectly rounded down = windspeed AK070124 0 2001291013 3012700000 0 0 = windspeed 1962 20070123:1300 250 3.089 = windspeed 3 versus 3.089 diff = -.089
Cloud Cover Whoops = cloudcover Whoops = cloudcover Whoops = cloudcover
AK020919 9 160105 993 7216700000 94 94 1962 20020918:2200 2743 3 8 7 versus 8
Error: Air Temperature Flag (actual/estimated) is all these are estimated air temp ... but relevant IN060116 0 86 501010 6115700010 IN060116 1 75 511010 5515700010 IN060116 2 80 531010 5216700010
9
57 02462
0 01802
035 542
incorrect flag is "0=actual" 0 0 0 01862 0 0 0 01712 0 0 0 01562
Inconsistent Wind (Direction and Speed) for both NM and NP 4271 20050130:1300 130 1.03 = 130degrees=SE=6 vs 9 and 4271 20050130:1400 0 0 = 0 vs 0 4271 20050130:1500 0 0 = 0 vs 10 4271 20050130:1600 990 .5148 = 990 undefined? vs 8 4271 20050130:1700 170 1.544 = 170degrees=S=8 vs 0 versus NM050131 0 170 981012 5 9700001 0 0 0 01822 NM050131 1 167 971011 0 0700001 0 0 0 01662 NM050131 2 170 951011 510700001 0 0 0 01502 NM050131 3 160 931010 10 8700001 0 0 0 01372 NM050131 4 146 891011 0 0700001 0 0 0 01252
1.03 vs .5 0 vs 0 0 vs .5 .5 vs 1.0 1.5 vs 0
Error: Missing Data identifier (should be 999, not 990) 4271 20050130:1600 990 .5148 Inconsistent Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air Whoops = Air
Air Temperature Temp IN060116 3 82 521010 5116400000 0 0 0 01432 Temp 12444 20060115:1600 0.00 7.0 81 1011.00 Temp 82 versus 70 diff = 12 Temp IN060116 4 82 521011 6116700000 0 0 0 01302 Temp 12444 20060115:1700 0.00 7.0 81 1011.00 Temp 82 versus 70 diff = 12 Temp IN060116 5 80 541012 6616700000 2 3 0 11192 Temp 12444 20060115:1800 0.01 7.0 81 1012.00 Temp 80 versus 70 diff = 10 Temp IN060116 6 91 571012 7715700000 33 30 0101092 Temp 12444 20060115:1900 0.12 8.0 81 1012.00 Temp 91 versus 80 diff = 11
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Appendix H. Notes on the Report Sections i
The Australian HERS began with only 28 climates (nominally), then expanded to 64, then 69, then 71 and is about to be expanded to 80. While it can be argued that many of these are redundant for rank ordering and even rating of dwellings reliably, the apparent attention to nuance implied in the extra climate zones was found to be important for credibility and acceptance as well as to reduce the chance that the "advice" given by the software could be misleading in the case of high performance design. (TRL) ii
The Australian HERS will soon have, out of a total of 80 zones, 4 zones for Melbourne, 3 zones each for Adelaide, Perth and Sydney (not counting the Blue Mountains) and 2 zones for Brisbane for essentially these reasons. (TRL) iii
The Australian Bureau of Meteorology recommends a minimum of 14 years to establish a reliable climatic mean (assuming no systematic change to climate). (TRL) iv
Three of the selected two-letter acronyms coincide with the pre-existing Australian ones: CH = Coffs Harbour, CO = Cobar, RO = Rockhampton. Replacements should be found to avoid confusion within the ANZHERS. (TRL) v
Australia has adopted the term Reference Meteorological Year (RMY) for this same file format to avoid confusion with the international usage of the acronym TMY to describe a particular and different format. (TRL) vi
In Australia we have two Alpine climate files: one for a valley (Thredbo Valley) and one for open plateau (Cabramurra). (TRL)
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