Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling
Abstract Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2021-06-01
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| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2019EA000933 |
| _version_ | 1819106019767222272 |
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| author | Sippora Stellingwerf Emily Riddle Thomas M. Hopson Jason C. Knievel Barbara Brown Mekonnen Gebremichael |
| author_facet | Sippora Stellingwerf Emily Riddle Thomas M. Hopson Jason C. Knievel Barbara Brown Mekonnen Gebremichael |
| author_sort | Sippora Stellingwerf |
| collection | DOAJ |
| description | Abstract Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times. |
| first_indexed | 2024-12-22T02:31:29Z |
| format | Article |
| id | doaj.art-ea5f190af89746a6a6a303720974b5fd |
| institution | Directory Open Access Journal |
| issn | 2333-5084 |
| language | English |
| last_indexed | 2024-12-22T02:31:29Z |
| publishDate | 2021-06-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj.art-ea5f190af89746a6a6a303720974b5fd2022-12-21T18:41:52ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842021-06-0186n/an/a10.1029/2019EA000933Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐ModelingSippora Stellingwerf0Emily Riddle1Thomas M. Hopson2Jason C. Knievel3Barbara Brown4Mekonnen Gebremichael5Wageningen University and Research Amersfoort NetherlandsNational Center for Atmospheric Research Boulder CO USANational Center for Atmospheric Research Boulder CO USANational Center for Atmospheric Research Boulder CO USANational Center for Atmospheric Research Boulder CO USAUniversity of California Los Angeles CA USAAbstract Accurate rainfall forecasts on timescales ranging from a few hours to several weeks are needed for many hydrological applications. This study examines bias, skill and reliability of four ensemble forecast systems (from Canada, UK, Europe, and the United States) and a multi‐model ensemble as applied to Ethiopian catchments. By verifying these forecasts on hydrological catchments, we focus on spatial scales that are relevant to many actual water forecasting applications, such as flood forecasting and reservoir optimization. By most verification metrics tested, the bias corrected European model is the best individual model at predicting daily rainfall variations, while the Canadian model shows the most realistic ensemble spread and thus the most reliable forecast probabilities, including those of extreme events. The skill of the multi‐model ensemble outperforms individual models by most metrics, and is skillful up to 9 days ahead. Skill is higher for the 0–5 day model accumulation than for the first 24 h, suggesting that timing errors strongly penalize the skill of forecasts with shorter accumulation periods. Due to seasonality in the model biases, bias correction is best applied to each month individually. Forecasting extreme rainfall is a challenge for Ethiopia, especially over mountainous regions where positive skill is only reached after bias correction. Compared to individual models, the multi‐model ensemble has a higher probability of detecting extreme rainfall and a lower false alarm rate, with usable skill at 24 h lead times.https://doi.org/10.1029/2019EA000933calibrationEast Africaensembleforecastshydrometeorologyprecipitation |
| spellingShingle | Sippora Stellingwerf Emily Riddle Thomas M. Hopson Jason C. Knievel Barbara Brown Mekonnen Gebremichael Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling Earth and Space Science calibration East Africa ensemble forecasts hydrometeorology precipitation |
| title | Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling |
| title_full | Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling |
| title_fullStr | Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling |
| title_full_unstemmed | Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling |
| title_short | Optimizing Precipitation Forecasts for Hydrological Catchments in Ethiopia Using Statistical Bias Correction and Multi‐Modeling |
| title_sort | optimizing precipitation forecasts for hydrological catchments in ethiopia using statistical bias correction and multi modeling |
| topic | calibration East Africa ensemble forecasts hydrometeorology precipitation |
| url | https://doi.org/10.1029/2019EA000933 |
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