Abstract
Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).
Highlights
A large number of satellite radiance observations from different sensors have been assimilated directly into the operational or research numerical weather prediction (NWP)models since the fast development of radiative transfer models and their linearized versions, such as Community Radiative Transfer Model (CRTM) or Radiative Transfer forTiros Operational Vertical Sounder (RTTOV) [1,2,3,4,5,6,7,8]
After assimilating the geostationary microwave sounder (GEOMS) radiance observations, the analysis results have much closer agreements with the observed radiances than the background does; both the root mean squared error (RMSE) and standard deviation (STDV) are reduced by about 25% and 30%, respectively (Figure 4c), indicating that the variational bias correction applied in this study is effective for directly assimilating the GEOMS radiances
The single observation test shows that the typhoon structure in terms of humidity, temperature, and wind can be flow-dependently adjusted by the GEOMS humidity radiance through the spatial and multivariate correlations in the hybrid EnVar data assimilation method
Summary
A large number of satellite radiance observations from different sensors have been assimilated directly into the operational or research numerical weather prediction (NWP). In the past two decades, microwave radiance data assimilation has greatly contributed to large reductions in the error of typhoon track and intensity forecasts by providing valuable observations over oceans that can partially penetrate clouds around a typhoon [29,30,31]. It is believed that microwave radiances from geostationary satellites will contribute to the improvement in forecast skill scores for rapidly developing severe storm systems, such as typhoons, through a data assimilation technique with a regional model utilizing frequent cycling setting.
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