Assessment of FY-2G, FY-4A, and Himawari-8 Atmospheric Motion Vectors over Southeast Asia and Their Assimilating Impact on the Forecasts of Tropical Cyclone PABUK

Abstract

The spatial coverage of atmospheric motion vectors (AMVs) over Southeast Asia (SEA) is mainly covered by the Himawari-8 (HIMA-8) and FengYun-2 (FY-2) series satellites in the Global Telecommunication System (GTS). With the launch of FengYun-4A (FY-4A), a new Chinese geostationary satellite, AMVs have enhanced the spatial and temporal resolution data along with allowing for more options of the spectral channels than the FY-2G. This study focuses on the preliminary quality assessments of the FY-2G, FY-4A and HIMA-8 AMVs during a three-month monsoon period, as well as the impact of assimilating AMVs on the numerical weather prediction (NWP) model over SEA. The results show that the qualities of the AMVs from the FY-2G and FY-4A are sensitive to different quality indicator (QI) values, but this is not the case for the HIMA-8. For QI values at 85%, FY-2G and FY-4A AMVs had a monthly mean feature in the monsoon period that were quite comparable to HIMA-8 AMVs, with a few exceptions in this area when three sets of AMVs were validated against NCEP/FNL operational global analysis data; however, the qualities of the AMVs from HIMA-8 were better overall than those from FY-2G and FY-4A. In addition, four experiments were conducted with and without an assimilation of AMVs with a QI at 85% available from FY-2G, FY-4A, and HIMA-8 to assess their impact on tropical cyclone (TC) PABUK from 1 to 4 January 2019. The findings demonstrate that the assimilation of three sets of AMVs diminishes the average initial position error and track forecast error after 42 h when compared to the control experiment. Nevertheless, none of the experiments’ analyses or forecasts of the TC intensity showed a statistically significant development. The findings for FY-2G and FY-4A AMVs may offer a direction forward for the FY AMVs series dataset for future implementation in the global data assimilation system of NWP models, similar to HIMA-8 AMVs, which shows a favourable performance in assimilating AMVs from assorted satellites for SEA forecasts.