Impact of the combined assimilation of GPM/IMGER precipitation and Himawari-8/AHI water vapor radiance on snowfall forecasts using WRF model and 4Dvar system

Abstract

In this study, the investigation is made to reveal the impact of multi-strategically assimilating Global Precipitation Measurement (GPM) precipitation and Himawari-8/Advanced Himawari Imager (AHI) water vapor radiances (WVR) on forecasting a heavy snowfall event in the Eastern Qinghai-Tibet Plateau (EQTP) employing the Weather Research and Forecast model (WRF) and the Four-Dimensional Variational (4DVar) assimilation system (WRF-4DVar). The multiple data assimilation (DA) strategies include control tests (CON), the individual assimilation of AHI and GPM tests (DA_AHI and DA_GPM) and the joint assimilation of GPM and AHI (DA_G&A), with different initial times. The results indicate that GPM precipitation effectively captures mesoscale atmospheric details, but its scope is confined to a limited area. AHI WVR is sensitive to upper-middle atmospheric humidity and furnishes extensive-scale environmental parameters such as water vapor transport characteristics. The joint assimilation of the two not only yields multi-dimensional atmospheric insights but also addresses the limitations of individual assimilation. Assimilation GPM and AHI are respective sensitivity to the lower layers (about 800hpa) and upper layers (about 400hpa) of model. The individual assimilation GPM has the greatest effect on near-surface humidity field, and AHI plays a dominant role in the joint assimilation. By assimilating different remote sensing products at different initial times of NWPs, the thermodynamic and dynamic structures are variously reconstructed, leading to the different snowfall scenes. In addition, we further compare the 12-hourly cumulative snowfall with in-situ meteorological station observations. The predictions of snowfall from DA_G&A perform much better with the correlation coefficient (CC) and root-mean-square error (RMSE) 0.36 and 3.14 mm, respectively. As for different initial times of NWPs, the best snowfall forecast is 0600 UTC on October 28, 2022, and the CC is 0.4. Nevertheless, accurately predicting precipitation areas, intensity, and temporal variations remains challenging, particularly for solid precipitation like snowfall. Thus, meticulous consideration of weather process characteristics, observation attributes, and relevant parameter configurations during DA are imperative to enhance the efficiency of observation data utilization.