{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:03:01Z","timestamp":1766484181123,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T00:00:00Z","timestamp":1629676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1507002"],"award-info":[{"award-number":["2018YFC1507002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The lack of accurate estimation of intense precipitation is a universal limitation in precipitation retrieval. Therefore, a new rainfall retrieval technique based on the Random Forest (RF) algorithm is presented using the Advanced Himawari Imager-8 (Himawari-8\/AHI) infrared spectrum data and the NCEP operational Global Forecast System (GFS) forecast information. And the gauge-calibrated rainfall estimates from the Global Precipitation Measurement (GPM) product served as the ground truth to train the model. The two-step RF classification model was established for (1) rain area delineation and (2) precipitation grades\u2019 estimation to improve the accuracy of moderate rain and heavy rain. In view of the imbalance categories\u2019 distribution in the datasets, the resampling technique including the Random Under-sampling algorithm and Synthetic Minority Over-sampling Technique (SMOTE) was implemented throughout the whole training process to fully learn the characteristics among the samples. Among the features used, the contributions of meteorological variables to the trained models were generally greater than those of infrared information; in particular, the contribution of precipitable water was the largest, indicating the sufficient necessity of water vapor conditions in rainfall forecasting. The simulation results by the RF model were compared with the GPM product pixel-by-pixel. To prove the universality of the model, we used independent validation sets which are not used for training and two independent testing sets with different periods from the training set. In addition, the algorithm was validated against independent rain gauge data and compared with GFS model rainfall. Consequently, the RF model identified rainfall areas with a Probability Of Detection (POD) of around 0.77 and a False-Alarm Ratio (FAR) of around 0.23 for validation, as well as a POD of 0.60\u20130.70 and a FAR of around 0.30 for testing. To estimate precipitation grades, the value of classification was 0.70 in validation and in testing the accuracy was 0.60 despite a certain overestimation. In summary, the performance on the validation and test data indicated the great adaptability and superiority of the RF algorithm in rainfall retrieval in East Asia. To a certain extent, our study provides a meaningful range division and powerful guidance for quantitative precipitation estimation.<\/jats:p>","DOI":"10.3390\/rs13163332","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T10:24:17Z","timestamp":1629714257000},"page":"3332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimating Rainfall with Multi-Resource Data over East Asia Based on Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-9173","authenticated-orcid":false,"given":"Yushan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"},{"name":"Shanghai Qi Zhi Institute, Shanghai 200232, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4373-4058","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Qi Zhi Institute, Shanghai 200232, China"},{"name":"Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixia","family":"Xiao","sequence":"additional","affiliation":[{"name":"Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210008, China"},{"name":"Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5820-1516","authenticated-orcid":false,"given":"Fuchang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Atmospheric and Oceanic Sciences, Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China"},{"name":"Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Song","sequence":"additional","affiliation":[{"name":"Meteorological Remote Sensing Application Center, Beijing Piesat Information Technology Co., Ltd., Beijing 100195, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Peng","sequence":"additional","affiliation":[{"name":"Meteorological Remote Sensing Application Center, Beijing Piesat Information Technology Co., Ltd., Beijing 100195, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4992","DOI":"10.1175\/2008JCLI2047.1","article-title":"Thermal contrast between the middle-latitude Asian continent and adjacent ocean and its connection to the East Asian summer precipitation","volume":"21","author":"Cheng","year":"2008","journal-title":"J. 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