Comprehensive analysis of GEO-KOMPSAT-2A and FengYun satellite-based precipitation estimates across Northeast
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Date
2022-05-03
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Citation of Original Publication
Gaohong Yin, Jongjin Baik & Jongmin Park (2022) Comprehensive analysis of GEO-KOMPSAT-2A and FengYun satellite-based precipitation estimates across Northeast Asia, GIScience & Remote Sensing, 59:1, 782-800, DOI: 10.1080/15481603.2022.2067970
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Attribution 4.0 International (CC BY 4.0)
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Abstract
Geostationary meteorological satellites provide precipitation estimates with a high spatio-temporal resolution, which is important for near real-time precipitation monitoring. This study systematically evaluated geostationary orbit (GEO) satellite-based quantitative precipitation estimates (QPEs) from Chinese Fengyun-2 G (FY-2 G), Fengyun-4A (FY-4A), and South Korean Geo-KOMPSAT-2A (GK-2A) across Northeast Asia in 2020. Compared against ground-based rainfall gauges at a 6-hourly scale, FY-2 G provided the highest accuracy in the China region with a high correlation coefficient (R = 0.53) and a low bias (−0.26 mm) due to the ground calibration process in FY-2 G. Conversely, GK-2A provided more accurate precipitation estimates for South Korea and Japan stations. FY-4A QPE generally showed a large positive bias throughout different seasons, although it provided satisfactory R and categorical statistics. FY-based QPEs slightly overestimated summer precipitation, especially over South Korea and Japan region, while GK-2A tended to underestimate summer precipitation. All examined QPEs showed poor accuracy during the winter season due to the frozen particles and ice clouds. Intensity analysis revealed that FY-based QPEs tended to overestimate the occurrence of no rain and heavy rain cases, whereas GK-2A underestimated no rain and heavy rain cases and overestimated light rain occurrence. It is also found that all examined QPEs captured the temporal variation of precipitation during storm events, while FY-based products overestimated heavy precipitation peaks and GK-2A underestimated peak precipitation. The findings in the study provided valuable information to further improve current infrared precipitation retrieval algorithms.