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Correcting bias of satellite rainfall data using physical empirical model

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...

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Main Authors:	Ziarh, Ghaith Falah, Shahid, Shamsuddin, Ismail, Tarmizi, Md. Asaduzzaman, Md. Asaduzzaman, Dewan, Ashraf
Format:	Article
Published:	Elsevier Ltd 2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/96055/ http://dx.doi.org/10.1016/j.atmosres.2020.105430
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spelling	my.utm.960552022-07-03T06:41:17Z http://eprints.utm.my/id/eprint/96055/ Correcting bias of satellite rainfall data using physical empirical model Ziarh, Ghaith Falah Shahid, Shamsuddin Ismail, Tarmizi Md. Asaduzzaman, Md. Asaduzzaman Dewan, Ashraf TA Engineering (General). Civil engineering (General) 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. Elsevier Ltd 2021 Article PeerReviewed Ziarh, Ghaith Falah and Shahid, Shamsuddin and Ismail, Tarmizi and Md. Asaduzzaman, Md. Asaduzzaman and Dewan, Ashraf (2021) Correcting bias of satellite rainfall data using physical empirical model. Atmospheric Research, 251 . p. 105430. ISSN 0169-8095 http://dx.doi.org/10.1016/j.atmosres.2020.105430
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Ziarh, Ghaith Falah Shahid, Shamsuddin Ismail, Tarmizi Md. Asaduzzaman, Md. Asaduzzaman Dewan, Ashraf Correcting bias of satellite rainfall data using physical empirical model
description	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.
format	Article
author	Ziarh, Ghaith Falah Shahid, Shamsuddin Ismail, Tarmizi Md. Asaduzzaman, Md. Asaduzzaman Dewan, Ashraf
author_facet	Ziarh, Ghaith Falah Shahid, Shamsuddin Ismail, Tarmizi Md. Asaduzzaman, Md. Asaduzzaman Dewan, Ashraf
author_sort	Ziarh, Ghaith Falah
title	Correcting bias of satellite rainfall data using physical empirical model
title_short	Correcting bias of satellite rainfall data using physical empirical model
title_full	Correcting bias of satellite rainfall data using physical empirical model
title_fullStr	Correcting bias of satellite rainfall data using physical empirical model
title_full_unstemmed	Correcting bias of satellite rainfall data using physical empirical model
title_sort	correcting bias of satellite rainfall data using physical empirical model
publisher	Elsevier Ltd
publishDate	2021
url	http://eprints.utm.my/id/eprint/96055/ http://dx.doi.org/10.1016/j.atmosres.2020.105430
_version_	1738510317605879808
score	13.211869

Correcting bias of satellite rainfall data using physical empirical model

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