Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

Satellite data play an irreplaceable role in global observation data systems. Effective comprehensive application of satellite products will inevitably improve numerical weather prediction. FengYun-4 (FY-4) series satellites can provide not only radiance data but also retrieval data with high temporal and spatial resolutions. To evaluate the potential benefits of the combination application of FY-4 Advanced Geostationary Radiance Imager (AGRI) products on Typhoon Saola analysis and forecast, two group of experiments are set up with the Weather Research and Forecasting model (WRF). Compared with the benchmark experiment, whose sea surface temperature (SST) is from the National Centers for Environmental Prediction (NCEP) reanalysis data, the SST replacement experiments with FY-4 A/B SST products significantly improve the track and precipitation forecast, especially with the FY-4B SST product. Based on the above results, AGRI clear-sky and all-sky assimilations with FY-4B SST are implemented with a self-constructed AGRI assimilation module. The results show that the AGRI all-sky assimilation experiment can obtain better analyses and forecasts. Furthermore, it is proven that the combination application of AGRI radiance and SST products is beneficial for typhoon prediction.

The infrared satellite data have become an important source of assimilated data in numerical weather prediction (NWP) models. With the self-constructed assimilated module in the Weather Research and Forecasting model’s Data Assimilation (WRFDA) system, a set of cycling assimilation experiments is conducted to evaluate the added value of assimilating the Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water vapor channels clear-sky data on analyses and forecasts for “23 · 7” heavy rainfall. The results show a notable reduction (50~60%) in the root mean square error (RMSE) of observed and simulated brightness temperature after assimilating AGRI and the positive analysis increments in temperature and humidity fields, which are conducive to precipitation formation. Furthermore, changes in humidity analysis caused by AGRI assimilation propagate from the upper to lower levels with assimilation cycling. Compared to the benchmark experiment, the AGRI assimilation experiments produce higher humidity conditions and more pronounced ascending motion, resulting in more realistic rainfall predictions at both location and intensity with higher rainfall scores, especially with the two-step assimilation scheme. Moreover, based on the results from sensitivity experiments, it is proven that the addition of a new channel 11 can further improve humidity and enhance rainfall location and intensity predictions. Overall, the clear-sky assimilation of the FY-4B AGRI water vapor channel data brings notable improvements to “23 · 7” heavy rainfall prediction.

The sensitivity of all-sky infrared radiances to both hydrometeor content and cloud height leads to a very non-Gaussian distribution of first-guess departures. This non-Gaussianity can be mitigated by the application of cloud-dependent error models that normalize departures by an estimate of the cloud-height effect via assigning increased errors in situations with high clouds that can lead to very large departures. In the current study, we systematically evaluate existing error models and propose a revised approach that leads to a better fit to a Gaussian distribution at no additional cost. Furthermore, the revised approach is physically better justified as the cloud effect is estimated by the maximum cloud effect of model and observations, which determines the largest possible departure. Preceding studies, in contrast, used the mean cloud effect of model and observations. Our study is based on a one-month data set of infrared observations in two water vapor channels (6.2 and 7.3 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation satellite and corresponding simulations from the weather forecast model AROME (Application of Research to Operations at Mesoscale) over central Europe. The evaluation of different approaches revealed that near-Gaussian departures can be achieved with three different approaches for the estimation of the cloud effect: (1) deviation from a climatologically estimated value; (2) deviation from the clear-sky brightness temperature of a window channel; and (3) deviation from the clear-sky brightness temperature of the channel that is assimilated. For the lower-peaking channel (7.3 μm) with a larger cloud effect, best results were achieved with the first two options. For the 6.2 μm channel, the third option led to a slightly more Gaussian distribution. The third option, however, requires a quality control that eliminates about 10 % of the observations.