Impacts of Radar Reflectivity and Surface Observation Data Assimilation on the Precipitation and Land Surface Temperature

The high quality of road weather forecast crucially depends on the accuracy of some meteorological factors, especially temperature and precipitation forecast. Besides, as vital input elements for road meteorological model, high spatiotemporal resolution weather forecast data plays a key role in the diagnosis of the road status.To get better high quality meteorological factors, we are developing data assimilation and prediction system which mainly combines WRF and GSI model. After testing in different native regions, the system shows good performance in predicting atmospheric conditions. Based on this system, kinds of data assimilation can be achieved, conventional data and Chinese Doppler radar at present. Several experiments have been done and show promising result. It indicates that lower level meteorological factors are improved effectively when assimilating surface observation data, while the amount of hydrometeors in cloud is adjusted when assimilating Doppler radar reflectivity data in a cloud analysis procedure. In addition, this system runs steadily and engenders better results than general interpolation schemes, which outputs hourly forecast data with regional resolution 3 km and ultimately feeds the road model.