Development of High Resolution Models and Its Applications for Weather and Climate Risk Reduction in Indonesia:
The First International Workshop on Prevention and Mitigation of Meteorological Disasters in Southeast Asia Kyoto, Japan, 3-5 March 2008
BMG
Develop...
The First International Workshop on Prevention and Mitigation of Meteorological Disasters in Southeast Asia Kyoto, Japan, 3-5 March 2008
BMG
Development of High Resolution Models and Its Applications for Weather and Climate Risk Reduction in Indonesia: Recent Development using CCAM
Mezak A. Ratag Director for Research & Development - BMG
Indonesia Meteorological & Geophysical Agency (BMG)
Climate Forecast Applications BMG
Outline
• Introduction • The needs of meteorological services at regency/district scale • Forecasting approach: Introducing CCAM • Some remarks on applications and dissemination activities Acknowledgement. The slides on CCAM are mostly based on the material prepared by Marcus Tatcher (CMAR – CSIRO). The results of CCAM presented here are all the outputs of the model run at BMG R&D Centre
It includes: The Conformal Cubic grid Using the Schmidt transform for regional forecasting Multiple nesting techniques for downscaling Topography and land-use datasets
A more detailed discussion of using CCAM for NWP and climate applications will be given in subsequent presentations
CMAR Introduction
Regional climate modelling at BMG (& LAPAN) Used DARLAM for most of 90s 1-way nested limited-area model
For last few years using the conformal-cubic atmospheric model (C-CAM), a variable-resolution global model avoids boundary reflections avoids difficulties should forcing model and driven model have different inherent cold or moist biases can enforce conservation in a proper manner
CMAR Introduction
CCAM technical notes CCAM employs a Conformal-Cubic grid Typically each face contains 48x48 grid points (i.e., a C48 grid) and 18 vertical sigma levels (total points = 48x48x6x18)
Devised by Rancic et al., QJRMS 1996 CMAR Introduction
Sigma levels
CMAR Introduction
Gnomonic-cubic grid and panels Sadourny (MWR, 1972) Semi-Lagrangian advection study by McGregor (A-O, 1996)
CMAR Introduction
CMAR Introduction
The Conformal-Cubic grid
The Conformal-Cubic (CC) grid provides CCAM with a number of advantages, including: No singular points (e.g., the north or south pole). No hard boundaries – CCAM is a global model. The grid can be stretched for high resolution forecasts (e.g., 1km). The stretched grid can be repositioned anywhere in the world.
CMAR Introduction
CCAM features 2-time-level semi-implicit hydrostatic (recently, also non-hydrostatic) semi-Lagrangian horizontal advection with bi-cubic spatial interpolation total variation diminishing (TVD) vertical advection unstaggered grid, with winds transformed to/from C-staggered positions before/after gravity wave calculations using reversible interpolation minimal horizontal diffusion needed: Smagorinsky style; zero is fine Cartesian representation of all awkward terms: calculation of departure points (McGregor, 1996, MWR) advection or diffusion of vector quantities indirect addressing keeps code simple weak off-centering (in time) used to avoid semi-Lagrangian "mountain resonances“ careful treatment of surface pressure and pressure-gradient terms near terrain a posteriori conservation of mass and moisture grid is isotropic
CMAR Introduction
CCAM physical parameterizations cumulus convection: new CSIRO mass-flux scheme, including downdrafts evaporation of rainfall GFDL parameterization for long and short wave radiation interactive cloud distributions derived prognostically from liquid water gravity-wave drag scheme stability-dependent boundary layer and vertical mixing with non-local option vegetation/canopy scheme 6 layers for soil temperatures 6 layers for soil moisture (Richard's equation) option for cumulus mixing of trace gases
CMAR Introduction
CCAM technical description
CMAR Introduction
CCAM technical notes
200km
Schmidt = 1. A uniform C48 grid. Note the (approx) uniform 200km grid spacing. CMAR Introduction
CCAM technical notes
750km
60km Schmidt = 3.33 The CCAM grid can be stretched (using a Schmidt transformation) to also provide a regional forecast. CMAR Introduction
CMAR Introduction
The Conformal-Cubic grid The ability to stretch the grid is crucial for generating high resolution forecasts (e.g., 1km). For example, to model the whole world for 1 day we would need: At 200km
