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• W4210975547 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Typhoon-related precipitation over land can result in severe disasters such as floods and landslides, and satellites are a valuable tool for estimating surface precipitation with high spatial-temporal resolutions. Accordingly, this study develops a surface precipitation integration framework to combine high-resolution observations from the radiometers of two geostationary satellites, Fengyun-4A (F4) and Himawari-8 (H8), with high-density rain-gauge observations or IMERG data and atmospheric reanalysis data based on a random forest (RF) algorithm. The RF algorithm integrates cloud and atmospheric features from radiometric observations and reanalysis information, and the intensity and spatial distribution of precipitation can be revealed by high-density rain-gauge or IMERG data. We take three typhoons that made landfall in South China during 2018 as examples. The F4-based and H8-based results using rain-gauge data as the predictand both show excellent results, yielding correlation coefficients (<span class=inline-formula><i>R</i></span>) of <span class=inline-formula>∼</span> 0.75 and probabilities of detection (POD) of <span class=inline-formula>∼</span> 0.95. In contrast, when IMERG data are used as the predictand, the corresponding <span class=inline-formula><i>R</i></span> and POD drop to <span class=inline-formula>∼</span> 0.5 and 0.93, respectively, due to the uncertainties related to IMERG retrievals. By carefully choosing the predictor, our RF algorithm successfully integrates the information from satellite observations, surface measurements and atmospheric reanalyses, resulting in precipitation estimates that are highly consistent with actual ground observations. Consequently, our proposed integration framework can reconstruct hourly surface precipitation estimates at high spatial-temporal resolutions for historical typhoon studies." @default.
• W4210975547 created "2022-02-13" @default.
• W4210975547 date "2022-02-11" @default.
• W4210975547 modified "2023-09-25" @default.
• W4210975547 title "Comment on amt-2021-411" @default.
• W4210975547 doi "https://doi.org/10.5194/amt-2021-411-rc2" @default.
• W4210975547 hasPublicationYear "2022" @default.
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