A comprehensive evaluation of the satellite precipitation products across Iran

Study region: Iran Study focus: This study evaluated three satellite precipitation products, including PERSIANN-CCS and TRMM-3B42RT V7 (near real-time satellites) and CMOROH (post real-time satellite), on hourly, daily, monthly, and annual time scales in 52 synoptic stations over Iran. This study ai...

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Main Authors: Asieh Mousavi Dehaghani, Alireza Gohari, Mohammad Javad Zareian, Ali Torabi Haghighi
Format: Article
Language:English
Published: Elsevier 2023-04-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581823000472
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author Asieh Mousavi Dehaghani
Alireza Gohari
Mohammad Javad Zareian
Ali Torabi Haghighi
author_facet Asieh Mousavi Dehaghani
Alireza Gohari
Mohammad Javad Zareian
Ali Torabi Haghighi
author_sort Asieh Mousavi Dehaghani
collection DOAJ
description Study region: Iran Study focus: This study evaluated three satellite precipitation products, including PERSIANN-CCS and TRMM-3B42RT V7 (near real-time satellites) and CMOROH (post real-time satellite), on hourly, daily, monthly, and annual time scales in 52 synoptic stations over Iran. This study aims to evaluate the accuracy of satellite- precipitation products over different return periods and durations as well as their performance in different climates and altitudes. The Pearson’s correlation coefficient (Rp), Relative Root Mean Square Error (RRMSE), and Relative Bias (RBias) as well as three probability indices including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were applied to evaluate the accuracy of these products. In addition, the sub-daily IDF curves (6, 12, 18, and 24 h) were derived to evaluate the studied satellites precipitation for 5, 10-, 20-, 50-, and 100-year return periods. New hydrological insights for the region: The results showed that the modification method could significantly improve all satellites' performance in precipitation estimation. The IDF curves derived from the CMORPH satellite as post real-time satellite precipitation product, showed the highest accuracy compared to synoptic stations. The TRMM and CMORPH underestimated precipitation by 56% and 8%, respectively, while PERSIANN-CCS overestimated precipitation by 143%. However, PERSIANN-CCS performed better in detecting the occurrence and non-occurrence of precipitation than other products. For Northeastern and Southwestern Iran, all products showed the same performance in accurately detecting occurrence and precipitation.
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spelling doaj.art-4f6ad6c71c13418ba5161ba15d221f912023-03-18T04:41:21ZengElsevierJournal of Hydrology: Regional Studies2214-58182023-04-0146101360A comprehensive evaluation of the satellite precipitation products across IranAsieh Mousavi Dehaghani0Alireza Gohari1Mohammad Javad Zareian2Ali Torabi Haghighi3Department of Irrigation, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, IranDepartment of Irrigation, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, IranDepartment of Water Resources Study and Research, Water Research Institute (WRI), Ministry of Energy, Tehran, IranWater, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN-90014 Oulu, Finland; Corresponding author.Study region: Iran Study focus: This study evaluated three satellite precipitation products, including PERSIANN-CCS and TRMM-3B42RT V7 (near real-time satellites) and CMOROH (post real-time satellite), on hourly, daily, monthly, and annual time scales in 52 synoptic stations over Iran. This study aims to evaluate the accuracy of satellite- precipitation products over different return periods and durations as well as their performance in different climates and altitudes. The Pearson’s correlation coefficient (Rp), Relative Root Mean Square Error (RRMSE), and Relative Bias (RBias) as well as three probability indices including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were applied to evaluate the accuracy of these products. In addition, the sub-daily IDF curves (6, 12, 18, and 24 h) were derived to evaluate the studied satellites precipitation for 5, 10-, 20-, 50-, and 100-year return periods. New hydrological insights for the region: The results showed that the modification method could significantly improve all satellites' performance in precipitation estimation. The IDF curves derived from the CMORPH satellite as post real-time satellite precipitation product, showed the highest accuracy compared to synoptic stations. The TRMM and CMORPH underestimated precipitation by 56% and 8%, respectively, while PERSIANN-CCS overestimated precipitation by 143%. However, PERSIANN-CCS performed better in detecting the occurrence and non-occurrence of precipitation than other products. For Northeastern and Southwestern Iran, all products showed the same performance in accurately detecting occurrence and precipitation.http://www.sciencedirect.com/science/article/pii/S2214581823000472Satellite precipitationPERSIANN-CCSTRMM-3B42RT V7CMORPHIDF curveIran
spellingShingle Asieh Mousavi Dehaghani
Alireza Gohari
Mohammad Javad Zareian
Ali Torabi Haghighi
A comprehensive evaluation of the satellite precipitation products across Iran
Journal of Hydrology: Regional Studies
Satellite precipitation
PERSIANN-CCS
TRMM-3B42RT V7
CMORPH
IDF curve
Iran
title A comprehensive evaluation of the satellite precipitation products across Iran
title_full A comprehensive evaluation of the satellite precipitation products across Iran
title_fullStr A comprehensive evaluation of the satellite precipitation products across Iran
title_full_unstemmed A comprehensive evaluation of the satellite precipitation products across Iran
title_short A comprehensive evaluation of the satellite precipitation products across Iran
title_sort comprehensive evaluation of the satellite precipitation products across iran
topic Satellite precipitation
PERSIANN-CCS
TRMM-3B42RT V7
CMORPH
IDF curve
Iran
url http://www.sciencedirect.com/science/article/pii/S2214581823000472
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