Assessment of FY‐2G Atmospheric Motion Vector Data and Assimilating Impacts on Typhoon Forecasts

Abstract

Atmospheric motion vectors (AMVs) have produced positive impacts on global weather forecasts, but few studies have evaluated the impacts of AMV data from the Fengyun (FY) geostationary satellite series, especially from FY‐2G and FY‐4, on typhoon forecasts in a regional model. In this study, the qualities of FY‐2G and Himawari‐8 AMV data are comparatively evaluated with several preprocesses (e.g., height assignment, quality control, channel merging, thinning, and observation error assignment) employed, and a super typhoon (Typhoon Hato, which occurred in 2017) is selected to assess the forecasting performances with the AMV data assimilated in the Weather Research and Forecasting data assimilation (WRFDA) system. The results show that the AMV data from Himawari‐8 are better overall than those from FY‐2G, with smaller RMSEs and biases for full wind speeds, and the preprocesses improve the qualities of these two geostationary satellite AMV data sets, as they both depict the same general atmospheric circulations. On the other hand, assimilation of AMV data improves the forecasts of environmental fields, resulting in the simulated track and intensity being closer to the observations. Generally, assimilating FY‐2G AMV data has comparably positive impacts with Himawari‐8 AMVs, and the AMV data retrieved from multiple channels provide even better forecasts. Experiments involving another typhoon case are also performed to further provide evidence that the derived AMV data from the Fengyun geostationary satellite series have value in the operational applications.

The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts

“…Liu et al (2012) used Weather Research and Forecasting Model Data Assimilation (WRFDA) system to assimilate the AMVs data retrieved from FY-2C infrared and water vapor channels, and showed that the reasonable selection of AMVs data added to the numerical weather prediction (NWP) model was beneficial to supply the meso-scale information not included in the initial fields, as well as improving the prediction ability of the model. In order to assess the qualities and assimilating impacts of the AMVs retrieved from the first generation of the Chinese Fengyun geostationary satellite, the AMVs data of FY-2G and Himawari-8 were evaluated by Liang et al (2021) in typhoon forecasts with the conclusions that both AMVs data have showed comparably positive forecasting impacts even though the AMVs quality of Himawari-8 were overall better.…”