{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T10:46:25Z","timestamp":1764240385350,"version":"build-2065373602"},"reference-count":64,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,26]],"date-time":"2020-09-26T00:00:00Z","timestamp":1601078400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["No. 2017YFB0503003"],"award-info":[{"award-number":["No. 2017YFB0503003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To obtain the high-resolution multitemporal precipitation using spatial downscaling technique on a precipitation dataset may provide a better representation of the spatial variability of precipitation to be used for different purposes. In this research, a new downscaling methodology such as the global precipitation mission (GPM)-based multitemporal weighted precipitation analysis (GMWPA) at 0.05\u00b0 resolution is developed and applied in the humid region of Mainland China by employing the GPM dataset at 0.1\u00b0 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m DEM-based geospatial predictors, i.e., elevation, longitude, and latitude in empirical distribution-based framework (EDBF) algorithm. The proposed methodology is a two-stepped process in which a scale-dependent regression analysis between each individual precipitation variable and the EDBF-based weighted precipitation with geospatial predictor(s), and to downscale the predicted multitemporal weighted precipitation at a refined scale is developed for the downscaling of GMWPA. While comparing results, it shows that the weighted precipitation outperformed all precipitation variables in terms of the coefficient of determination (R2) value, whereas they outperformed the annual precipitation variables and underperformed as compared to the seasonal and the monthly variables in terms of the calculated root mean square error (RMSE) value. Based on the achieved results, the weighted precipitation at the low-resolution (e.g., at 0.75\u00b0 resolution) along-with the original resolution (e.g., at 0.1\u00b0 resolution) is employed in the downscaling process to predict the average multitemporal precipitation, the annual total precipitation for the year 2001 and 2004, and the average annual precipitation (2001\u20132015) at 0.05\u00b0 resolution, respectively. The downscaling approach resulting through proposed methodology captured the spatial patterns with greater accuracy at higher spatial resolution. This work showed that it is feasible to increase the spatial resolution of a precipitation variable(s) with greater accuracy on an annual basis or as an average from the multitemporal precipitation dataset using a geospatial predictor as the proxy of precipitation through the weighted precipitation in EDBF environment.<\/jats:p>","DOI":"10.3390\/rs12193162","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T08:02:58Z","timestamp":1601280178000},"page":"3162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["GPM-Based Multitemporal Weighted Precipitation Analysis Using GPM_IMERGDF Product and ASTER DEM in EDBF Algorithm"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4379-1426","authenticated-orcid":false,"given":"Sana","family":"Ullah","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"},{"name":"Guangxi Key Laboratory of Remote Measuring System, Guilin University of Aerospace Technology, Guilin 541004, China"}]},{"given":"Zhengkang","family":"Zuo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"}]},{"given":"Feizhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"}]},{"given":"Jianghua","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China"}]},{"given":"Shifeng","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3396-3327","authenticated-orcid":false,"given":"Yi","family":"Lin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7031-6674","authenticated-orcid":false,"given":"Imran","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1769-0285","authenticated-orcid":false,"given":"Yiyuan","family":"Sun","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"},{"name":"Key Laboratory of Mountain Resources and Environmental Remote Sensing, School of Geography and Environmental Science, Guizhou Normal University, Guiyang 550001, China"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"}]},{"given":"Lei","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Space Information Integration and 3s Application, School of Earth and Space Science, Peking University, Beijing 100871, China"},{"name":"Guangxi Key Laboratory of Remote Measuring System, Guilin University of Aerospace Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.jhydrol.2010.04.027","article-title":"High-resolution space\u2013time rainfall analysis using integrated ANN inference systems","volume":"387","author":"Langella","year":"2010","journal-title":"J. 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