Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia
Abstract Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), as two state‐of‐the‐art approaches, were applied to improve the prediction skills of 24‐h accumulated precipitation over East Asia with lead days of 1–7 days. The multimodel ensemble precipitation probabilistic fore...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Meteorological Applications |
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| Online Access: | https://doi.org/10.1002/met.70035 |
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| author | Luying Ji Xiefei Zhi Qixiang Luo Yan Ji |
| author_facet | Luying Ji Xiefei Zhi Qixiang Luo Yan Ji |
| author_sort | Luying Ji |
| collection | DOAJ |
| description | Abstract Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), as two state‐of‐the‐art approaches, were applied to improve the prediction skills of 24‐h accumulated precipitation over East Asia with lead days of 1–7 days. The multimodel ensemble precipitation probabilistic forecast experiments were constructed using ensemble forecasts from multiple ensemble prediction systems, revealing that the standard BMA (s‐BMA) and the standard EMOS (s‐EMOS) outperformed the raw ensemble forecasts. In comparison with the raw ensembles, the improvement by the s‐BMA model increases as lead days increase, while the s‐EMOS model consistently enhances prediction accuracy by around 30% for all lead days. Overall, the s‐EMOS model demonstrates superior performance compared with the s‐BMA model, which struggles with forecasting heavy daily precipitation exceeding 25 mm. Accordingly, the hierarchical BMA (h‐BMA) model is introduced in this study, designed for different precipitation classifications. Compared with the s‐BMA model, the h‐BMA model notably improves the probabilistic forecast skill for all precipitation thresholds throughout East Asia, particularly for heavy precipitation events. Moreover, the h‐BMA model also improves the forecast reliability across various precipitation thresholds. A hierarchical EMOS (h‐EMOS) model is also developed to validate the benefits of the precipitation classifications and further improves the forecast accuracy as expected. The prediction probability density functions of the hierarchical models are much sharper and more concentrated than those of the standard models. In general, the improvement in precipitation probabilistic forecast skill of the h‐BMA model relative to the s‐BMA model surpasses that of the h‐EMOS model compared with the s‐EMOS model. |
| format | Article |
| id | doaj-art-f4eb09fe754b49af9caf2f7db233d2e7 |
| institution | DOAJ |
| issn | 1350-4827 1469-8080 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Meteorological Applications |
| spelling | doaj-art-f4eb09fe754b49af9caf2f7db233d2e72025-08-20T03:15:05ZengWileyMeteorological Applications1350-48271469-80802025-03-01322n/an/a10.1002/met.70035Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East AsiaLuying Ji0Xiefei Zhi1Qixiang Luo2Yan Ji3Nanjing Innovation Institute for Atmospheric Sciences Chinese Academy of Meteorological Sciences–Jiangsu Meteorological Service Nanjing ChinaKey Laboratory of Meteorological Disasters, Ministry of Education (KLME) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC‐FEMD) Nanjing University of Information Science and Technology Nanjing ChinaUnit No. 31153 of PLA Nanjing ChinaSchool of Atmosphere and Remote Sensing Wuxi University Wuxi ChinaAbstract Bayesian model averaging (BMA) and ensemble model output statistics (EMOS), as two state‐of‐the‐art approaches, were applied to improve the prediction skills of 24‐h accumulated precipitation over East Asia with lead days of 1–7 days. The multimodel ensemble precipitation probabilistic forecast experiments were constructed using ensemble forecasts from multiple ensemble prediction systems, revealing that the standard BMA (s‐BMA) and the standard EMOS (s‐EMOS) outperformed the raw ensemble forecasts. In comparison with the raw ensembles, the improvement by the s‐BMA model increases as lead days increase, while the s‐EMOS model consistently enhances prediction accuracy by around 30% for all lead days. Overall, the s‐EMOS model demonstrates superior performance compared with the s‐BMA model, which struggles with forecasting heavy daily precipitation exceeding 25 mm. Accordingly, the hierarchical BMA (h‐BMA) model is introduced in this study, designed for different precipitation classifications. Compared with the s‐BMA model, the h‐BMA model notably improves the probabilistic forecast skill for all precipitation thresholds throughout East Asia, particularly for heavy precipitation events. Moreover, the h‐BMA model also improves the forecast reliability across various precipitation thresholds. A hierarchical EMOS (h‐EMOS) model is also developed to validate the benefits of the precipitation classifications and further improves the forecast accuracy as expected. The prediction probability density functions of the hierarchical models are much sharper and more concentrated than those of the standard models. In general, the improvement in precipitation probabilistic forecast skill of the h‐BMA model relative to the s‐BMA model surpasses that of the h‐EMOS model compared with the s‐EMOS model.https://doi.org/10.1002/met.70035Bayesian model averagingensemble model output statisticshierarchical multimodel ensemble forecastsprobabilistic forecastsshort‐to‐medium‐range precipitation forecasts |
| spellingShingle | Luying Ji Xiefei Zhi Qixiang Luo Yan Ji Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia Meteorological Applications Bayesian model averaging ensemble model output statistics hierarchical multimodel ensemble forecasts probabilistic forecasts short‐to‐medium‐range precipitation forecasts |
| title | Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia |
| title_full | Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia |
| title_fullStr | Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia |
| title_full_unstemmed | Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia |
| title_short | Hierarchical multimodel ensemble probabilistic forecasts for precipitation over East Asia |
| title_sort | hierarchical multimodel ensemble probabilistic forecasts for precipitation over east asia |
| topic | Bayesian model averaging ensemble model output statistics hierarchical multimodel ensemble forecasts probabilistic forecasts short‐to‐medium‐range precipitation forecasts |
| url | https://doi.org/10.1002/met.70035 |
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