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DOI	10.1016/j.atmosres.2020.105430
	Correcting bias of satellite rainfall data using physical empirical model
	Ziarh G.F.; Shahid S.; Ismail T.B.; Asaduzzaman M.; Dewan A.
发表日期	2021
ISSN	0169-8095
卷号	251
英文摘要	The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000–2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and − 3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2–0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards. © 2020 Elsevier B.V.
英文关键词	Bias correction; Ensemble learning algorithm; Near-real-time rainfall; Physical-empirical model; Satellite precipitation
语种	英语
scopus关键词	Decision trees; Hazards; Learning algorithms; Mean square error; Rain gages; Regression analysis; Satellites; Soil moisture; Topography; Weather forecasting; Accuracy evaluation; Rain gauge networks; Root mean square errors; Satellite mapping; Satellite rainfall data; Satellite rainfalls; Spatial correlations; Two stage procedure; Rain; algorithm; correction; data set; empirical analysis; ensemble forecasting; precipitation assessment; precipitation intensity; real time; satellite imagery
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/162348
作者单位	Department of Water and Environmental Engineering, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia; Department of Engineering, School of Digital, Technologies and Arts, Staffordshire University, Stoke-on-Trent, ST4 2DE, United Kingdom; Spatial Sciences Discipline, School of Earth and Planetary Sciences, Curtin University, Perth, WA 6102, Australia
推荐引用方式 GB/T 7714	Ziarh G.F.,Shahid S.,Ismail T.B.,et al. Correcting bias of satellite rainfall data using physical empirical model[J],2021,251.
APA	Ziarh G.F.,Shahid S.,Ismail T.B.,Asaduzzaman M.,&Dewan A..(2021).Correcting bias of satellite rainfall data using physical empirical model.Atmospheric Research,251.
MLA	Ziarh G.F.,et al."Correcting bias of satellite rainfall data using physical empirical model".Atmospheric Research 251(2021).