Research and Operational Development of Numerical Weather Prediction in China

Numerical weather prediction(NWP) is a core technology in weather forecast and disaster mitigation. China’s NWP research and operational applications have been attached great importance by the meteorological community.Fundamental achievements have been made in the theories, methods, and NWP model development in China, which are of certain international impacts. In this paper, the scientific and technological progress of NWP in China since1949 is summarized. The current status and recent progress of the domestically developed NWP system-GRAPES(Global/Regional Assimilation and Pr Ediction System) are presented. Through independent research and development in the past 10 years, the operational GRAPES system has been established, which includes both regional and global deterministic and ensemble prediction models, with resolutions of 3-10 km for regional and 25-50 km for global forecasts. Major improvements include establishment of a new non-hydrostatic dynamic core, setup of four-dimensional variational data assimilation, and development of associated satellite application. As members of the GRAPES system, prediction models for atmospheric chemistry and air pollution, tropical cyclones, and ocean waves have also been developed and put into operational use. The GRAPES system has been an important milestone in NWP science and technology in China.

Impact of FY-3D MWRI Radiance Assimilation in GRAPES 4DVar on Forecasts of Typhoon Shanshan

In this study, Fengyun-3 D(FY-3 D) Micro Wave Radiation Imager(MWRI) radiance data were directly assimilated into the Global/Regional Assimilation and Pr Ediction System(GRAPES) four-dimensional variational(4 DVar) system. Quality control procedures were developed for MWRI applications by using algorithms from similar microwave instruments. Compared with the FY-3 C MWRI, the bias of FY-3 D MWRI observations did not show a clear node-dependent difference from the numerical weather prediction background simulation. A conventional bias correction approach can therefore be used to remove systematic biases before the assimilation of data. After assimilating the MWRI radiance data into GRAPES, the geopotential height and humidity analysis fields were improved relative to the control experiment. There was a positive impact on the location of the subtropical high, which led to improvements in forecasts of the track of Typhoon Shanshan.