Automatic
Optimization of Weather Research and Forecasting (WRF) Model Parameters
Qingyun
Duan
Beijing Normal University
Noon April 1 in Room 2155
Abstract: Weather Research and Forecasting (WRF) model is a
community numerical weather prediction (NWP) model widely used by many
researchers and practitioners in the world. There are three ways
to improve the performance of the WRF model. The first way is to
improve the parameterization schemes for different physical processes,
including radiation, cumulous cloud, microphysics, surface and boundary
layers and land surface. The second way is to obtain the most
accurate and reliable boundary and initial conditions with new
observational technologies and data assimilation techniques. The
third way is to enhance the estimation of parameters present in the WRF
model. This study concerns with the estimation of the parametric
uncertainty of the WRF model. We identified a list of over 20
parameters which are thought to be influential in the performance of
the WRF model in forecasting precipitation and temperature. A
global sensitivity analysis is first used to screen out the most
sensitive parameters. Then we explore the use of surrogate
modeling approach to identify the optimal parameters for the screened
parameters. The numerical case study is set for the North China
domain surrounding the Beijing city area. The WRF model will be
run at 9km spatial resolution. The forecast lead time is set to 5
days. We have selected 9 different storms over the summer months
(June/July/August) over the 2008-2010 period to study the effect of
parametric uncertainty on predictive skill of precipitation and surface
temperature forecasts. This presentation will show the
preliminary results from this study.