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Universities","award":["61802158"],"award-info":[{"award-number":["61802158"]}]},{"name":"Fundamental Research Funds for Central Universities","award":["2019YFA0706200"],"award-info":[{"award-number":["2019YFA0706200"]}]},{"name":"Fundamental Research Funds for Central Universities","award":["lzujbky-2019-26"],"award-info":[{"award-number":["lzujbky-2019-26"]}]},{"name":"Fundamental Research Funds for Central Universities","award":["lzujbky-2021-kb26"],"award-info":[{"award-number":["lzujbky-2021-kb26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Global Precipitation Measurement (GPM) IMERG V06 product showed excellent performance in detecting precipitation, but still have room to improve. This study proposed an event-based bias correction strategy through random forest (RF) method to improve accuracy of the GPM IMERG V06 product over mainland China. Results showed that, over mainland China, biases caused by \u2018hits\u2019 events are most responsible for the errors of the GPM product, and \u2018falseAlarms\u2019 events took the least responsibility for that because of the small GPM values for \u2018falseAlarms\u2019 events. Compared with the raw GPM product, the bias-corrected GPM product showed better performance in both fitting observed precipitation values and in detecting precipitation events, thus the event-based bias-strategy in this study is credible. After bias correction, the ability of the bias-corrected GPM product was significantly improved for \u2018hits\u2019 events, but showed slight deterioration in RMSE and MAE and significant improvements in FAR and CSI for \u2018falseAlarms\u2019 events. This is because the established RF classification model tends to learn characteristics of the event with larger proportion, and then performed better in correctly identifying the event with larger proportion in the subregion.<\/jats:p>","DOI":"10.3390\/rs14163859","type":"journal-article","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T04:20:32Z","timestamp":1660105232000},"page":"3859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Event-Based Bias Correction of the GPM IMERG V06 Product by Random Forest Method over Mainland China"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhenyu","family":"Liu","sequence":"first","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Haowen","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2592-1987","authenticated-orcid":false,"given":"Lanhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of West China\u2019s Environmental System (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}]},{"given":"Bin","family":"Hu","sequence":"additional","affiliation":[{"name":"Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.atmosres.2009.08.017","article-title":"Precipitation: Measurement, remote sensing, climatology and modeling","volume":"94","author":"Michaelides","year":"2009","journal-title":"Atmos. 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