2024/12/25 by Siyu Zhu, Ziqiang Ma, Songkun Yan +2 · 1 citation
Earth and Planetary Sciences · #Remote Sensing and Land Use
paper · doi:10.1109/tgrs.2024.3522152
Multiple spectral infrared (IR) observations onboard geostationary satellites are effective and widely used in estimating precipitation with high spatiotemporal resolutions. Currently, FengYun-4A (FY-4A) and FengYun-4B (FY-4B), representing the most advanced Chinese geostationary meteorological satellites, are equipped with Advanced Geosynchronous Radiation Imager (AGRI) to continuously observe the climate and weather over vast eastern Asia region. However, geographic factors, such as viewing zenith angle (VZA) and solar zenith angle (SZA), could result in systematic errors in estimating and merging precipitation. Therefore, motivated by analyzing the influence patterns of these physical factors on precipitation estimation, the precipitation estimation using the chromatographic analysis method by merging enhanced multispectral IR observations (PECAM) is proposed to generate the merged precipitation data covering observed fields of both FY-4A and FY-4B. The main conclusions are summarized as follows: 1) latitude, view zenith angle (VZA), and elevation exert a negative influence (up to 20 K) on averaged TBBs of single IR band (asT10.8), causing precipitation overestimation; 2)Δ T7.1A-13.5AandΔ T6.95B-13.3B,Δ T3.75H-13.5AandΔ T3.75H-13.3B, andΔ T3.75H-3.75Lare mainly influenced by ecliptic obliquity angle (EOA), SZA, and shadow effect, causing seasonal patterns, diurnal fluctuations, and shadow effects, respectively; 3) compared to PERSIANN-CCS, FY4A-Official, and FY4B-Official, at hourly scale, PECAM-FY4A&B consistently outperforms them across CC, RMSE, and CSI metrics, with minimum improvements of approximately 0.046 in CC, 0.30 mm/h in RMSE, and 0.033 in CSI; and 4) meanwhile at daily scales, merged data from FY-4A&B shows overall improvements in CC, RMSE, and CSI, with at least 0.041, 0.80 mm/day, and 0.022, respectively. Foreseeably, the signal processing and merging strategy in PECAM has significant potential to serve as references for the estimation and integration of precipitation data from the FY-4 series, as well as the GOES, Meteosat, and Himawari series.