Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin
For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates. Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs.
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Format: | Journal Article biblioteca |
Language: | English |
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Informa UK Limited
2019-10-18
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Subjects: | rain, precipitation, satellites, weather forecasting, performance evaluation, river basins, meteorological stations, observation, hydrology, deltas, |
Online Access: | https://hdl.handle.net/10568/106467 https://doi.org/10.1080/01431161.2019.1602791 |
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dig-cgspace-10568-1064672023-06-13T06:32:12Z Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin Gumindoga, W. Rientjes, T.H.M. Haile, Alemseged Tamiru rain precipitation satellites weather forecasting performance evaluation river basins meteorological stations observation hydrology deltas For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates. Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs. 2019-10-18 2020-01-08T05:11:54Z 2020-01-08T05:11:54Z Journal Article Gumindoga, W.; Rientjes, T. H. M.; Haile, Alemseged Tamiru; Makurira, H.; Reggiani, P. 2019. Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin. International Journal of Remote Sensing, 40(20):7730-7749. doi: 10.1080/01431161.2019.1602791 0143-1161 https://hdl.handle.net/10568/106467 https://doi.org/10.1080/01431161.2019.1602791 en Copyrighted; all rights reserved Limited Access 7730-7749 Informa UK Limited International Journal of Remote Sensing |
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rain precipitation satellites weather forecasting performance evaluation river basins meteorological stations observation hydrology deltas rain precipitation satellites weather forecasting performance evaluation river basins meteorological stations observation hydrology deltas Gumindoga, W. Rientjes, T.H.M. Haile, Alemseged Tamiru Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
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For evaluation of the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product in the Zambezi Basin, daily time series (1998–2013) of 60 rain gauge stations are used. Evaluations for occurrence and rain rate are at sub-basin scale and at daily, weekly, and seasonal timescale by means of probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias (FBS). CMORPH predicts 60% of the rainfall occurrences. Rainfall detection is better for the wet season than for the dry season. Best detection is shown for rainfall rates smaller than 2.5 mm/day. Findings on error decomposition revealed sources of Hit, Missed and False rainfall bias. CMORPH performance (detection of rainfall occurrences and estimations for rainfall depth) at sub-basin scale increases when daily estimates are accumulated to weekly estimates. Findings suggest that for the Zambezi Basin, errors in CMORPH rainfall should be corrected before the product can serve applications such as in hydrological modelling that largely rely on reliable and accurate rainfall inputs. |
format |
Journal Article |
topic_facet |
rain precipitation satellites weather forecasting performance evaluation river basins meteorological stations observation hydrology deltas |
author |
Gumindoga, W. Rientjes, T.H.M. Haile, Alemseged Tamiru |
author_facet |
Gumindoga, W. Rientjes, T.H.M. Haile, Alemseged Tamiru |
author_sort |
Gumindoga, W. |
title |
Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
title_short |
Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
title_full |
Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
title_fullStr |
Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
title_full_unstemmed |
Performance evaluation of CMORPH satellite precipitation product in the Zambezi Basin |
title_sort |
performance evaluation of cmorph satellite precipitation product in the zambezi basin |
publisher |
Informa UK Limited |
publishDate |
2019-10-18 |
url |
https://hdl.handle.net/10568/106467 https://doi.org/10.1080/01431161.2019.1602791 |
work_keys_str_mv |
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_version_ |
1779065244767748096 |