Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia
This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation...
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doaj-42dc376689774774a46cc8c973c873162020-11-24T22:15:20ZengMDPI AGRemote Sensing2072-42922015-06-01767181721110.3390/rs70607181rs70607181Inter-Comparison of High-Resolution Satellite Precipitation Products over Central AsiaHao Guo0Sheng Chen1Anming Bao2Jujun Hu3Abebe S. Gebregiorgis4Xianwu Xue5Xinhua Zhang6State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaSchool of Computer Science, University of Oklahoma, Norman, OK 73072, USAHydrometeorology and Remote Sensing Laboratory and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USAHydrometeorology and Remote Sensing Laboratory and School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USAState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, ChinaThis paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%).http://www.mdpi.com/2072-4292/7/6/7181satellite-based precipitation estimatesbias correctionquantitative precipitation estimationerror characteristicCentral Asia |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hao Guo Sheng Chen Anming Bao Jujun Hu Abebe S. Gebregiorgis Xianwu Xue Xinhua Zhang |
spellingShingle |
Hao Guo Sheng Chen Anming Bao Jujun Hu Abebe S. Gebregiorgis Xianwu Xue Xinhua Zhang Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia Remote Sensing satellite-based precipitation estimates bias correction quantitative precipitation estimation error characteristic Central Asia |
author_facet |
Hao Guo Sheng Chen Anming Bao Jujun Hu Abebe S. Gebregiorgis Xianwu Xue Xinhua Zhang |
author_sort |
Hao Guo |
title |
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia |
title_short |
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia |
title_full |
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia |
title_fullStr |
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia |
title_full_unstemmed |
Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia |
title_sort |
inter-comparison of high-resolution satellite precipitation products over central asia |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2015-06-01 |
description |
This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between −57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%). |
topic |
satellite-based precipitation estimates bias correction quantitative precipitation estimation error characteristic Central Asia |
url |
http://www.mdpi.com/2072-4292/7/6/7181 |
work_keys_str_mv |
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