Impacts of Radiance Data Assimilation on Weather Forecasts by the WRF-ARW Model in the Complex Terrain Areas of Southwest Asia

First, this paper describes the forecasting errors of the National Center for Atmospheric Research (NCAR) mesoscale model (WRF-ARW) applied in the complex terrain of Southwest Asia from May 1 through 31 2006. The subsequent statistical evaluation is designed to assess the model's surface and upper-air forecast accuracy. Results show that the model biases caused by inadequate parameterization of physical processes, except for the 2-m temperature, are relatively small compared to the nonsystematic errors resulting in part from the uncertainty in initial conditions. The total model forecast errors at surface show a substantial spatial heterogeneity, there is a relative larger error in higher mountain areas. And, the performance of 2-m temperature forecasts is different from the other variables forecasts, the model forecast errors in 2-m temperature forecasts are closely related to the terrain configuration. Its diurnal cycle variation in model is much smaller than the observation.

Secondly, in order to understand the role of initial conditions in the accuracy of the model forecasts, this study assimilated a form of satellite radiance data into this model through the Joint Center for Satellite Data Assimilation (JCSDA) analysis system called the Global Statistical Interpolation (GSI). The results indicate over a 30-day experiment that on average for the 24- and 48-h (second 24-h) forecasts, the satellite data provides beneficial information for improving the initial conditions and the model errors are reduced to some degree over some of study locations. The diurnal cycle for some forecasting variables can be improved after satellite data assimilation, however, the improvement is very limited.