Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China
Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two con...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Kefeng [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Convective-permitting ensemble forecasts |
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| Anmerkung: |
© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Science in China - Heidelberg : Springer, 1997, 65(2022), 10 vom: 09. Aug., Seite 1879-1902 |
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| Übergeordnetes Werk: |
volume:65 ; year:2022 ; number:10 ; day:09 ; month:08 ; pages:1879-1902 |
| Links: |
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| DOI / URN: |
10.1007/s11430-022-9961-7 |
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SPR051039427 |
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| 520 | |a Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. | ||
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10.1007/s11430-022-9961-7 doi (DE-627)SPR051039427 (SPR)s11430-022-9961-7-e DE-627 ger DE-627 rakwb eng Zhu, Kefeng verfasserin aut Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. Convective-permitting ensemble forecasts (dpeaa)DE-He213 Neighborhood precipitation probability (dpeaa)DE-He213 Extreme rainfall (dpeaa)DE-He213 Zhang, Chenyue aut Xue, Ming aut Yang, Nan aut Enthalten in Science in China Heidelberg : Springer, 1997 65(2022), 10 vom: 09. Aug., Seite 1879-1902 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:65 year:2022 number:10 day:09 month:08 pages:1879-1902 https://dx.doi.org/10.1007/s11430-022-9961-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 65 2022 10 09 08 1879-1902 |
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10.1007/s11430-022-9961-7 doi (DE-627)SPR051039427 (SPR)s11430-022-9961-7-e DE-627 ger DE-627 rakwb eng Zhu, Kefeng verfasserin aut Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. Convective-permitting ensemble forecasts (dpeaa)DE-He213 Neighborhood precipitation probability (dpeaa)DE-He213 Extreme rainfall (dpeaa)DE-He213 Zhang, Chenyue aut Xue, Ming aut Yang, Nan aut Enthalten in Science in China Heidelberg : Springer, 1997 65(2022), 10 vom: 09. Aug., Seite 1879-1902 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:65 year:2022 number:10 day:09 month:08 pages:1879-1902 https://dx.doi.org/10.1007/s11430-022-9961-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 65 2022 10 09 08 1879-1902 |
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10.1007/s11430-022-9961-7 doi (DE-627)SPR051039427 (SPR)s11430-022-9961-7-e DE-627 ger DE-627 rakwb eng Zhu, Kefeng verfasserin aut Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. Convective-permitting ensemble forecasts (dpeaa)DE-He213 Neighborhood precipitation probability (dpeaa)DE-He213 Extreme rainfall (dpeaa)DE-He213 Zhang, Chenyue aut Xue, Ming aut Yang, Nan aut Enthalten in Science in China Heidelberg : Springer, 1997 65(2022), 10 vom: 09. Aug., Seite 1879-1902 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:65 year:2022 number:10 day:09 month:08 pages:1879-1902 https://dx.doi.org/10.1007/s11430-022-9961-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 65 2022 10 09 08 1879-1902 |
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10.1007/s11430-022-9961-7 doi (DE-627)SPR051039427 (SPR)s11430-022-9961-7-e DE-627 ger DE-627 rakwb eng Zhu, Kefeng verfasserin aut Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. Convective-permitting ensemble forecasts (dpeaa)DE-He213 Neighborhood precipitation probability (dpeaa)DE-He213 Extreme rainfall (dpeaa)DE-He213 Zhang, Chenyue aut Xue, Ming aut Yang, Nan aut Enthalten in Science in China Heidelberg : Springer, 1997 65(2022), 10 vom: 09. Aug., Seite 1879-1902 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:65 year:2022 number:10 day:09 month:08 pages:1879-1902 https://dx.doi.org/10.1007/s11430-022-9961-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 65 2022 10 09 08 1879-1902 |
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10.1007/s11430-022-9961-7 doi (DE-627)SPR051039427 (SPR)s11430-022-9961-7-e DE-627 ger DE-627 rakwb eng Zhu, Kefeng verfasserin aut Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. Convective-permitting ensemble forecasts (dpeaa)DE-He213 Neighborhood precipitation probability (dpeaa)DE-He213 Extreme rainfall (dpeaa)DE-He213 Zhang, Chenyue aut Xue, Ming aut Yang, Nan aut Enthalten in Science in China Heidelberg : Springer, 1997 65(2022), 10 vom: 09. Aug., Seite 1879-1902 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:65 year:2022 number:10 day:09 month:08 pages:1879-1902 https://dx.doi.org/10.1007/s11430-022-9961-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 AR 65 2022 10 09 08 1879-1902 |
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Predictability and skill of convection-permitting ensemble forecast systems in predicting the record-breaking “21·7” extreme rainfall event in Henan Province, China |
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Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Abstract During 19–21 July 2021, an extreme rainfall event occurred in Henan Province, China, during which a record-breaking maximum hourly rainfall of 201.9 mm was recorded in Zhengzhou at 09 UTC July 20. In this study, the predictability of this extreme rainfall event is investigated using two convection-permitting ensemble forecast systems (CEFSs): one initialized from NCEP GEFS (named CEFS_GEFS) and the other initialized from time-lagged ERA5 data (named CEFS_ERA). Both are able to reproduce the daily heavy rainfall along the Taihang Mountains, but most members have significant position biases for the extreme rainfall in Zhengzhou. For the hourly rainfall, a few members are able to capture the evolution and propagation of extreme rainfall. However, all ensemble members underestimate the extreme hourly rainfall and have position errors of a few tens to a few hundreds of kilometers. Such results suggest that the predictability of the extreme hourly rainfall at the accuracy of city scale in Zhengzhou is low, especially by deterministic forecasting models, and the occurrence of the extreme requires that many favorable conditions to happen simultaneously. In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. While the traditional POP shows almost no skill for hourly rainfall ≥ 25 mm $ h^{−1} $, the neighborhood POP significantly improves the forecast skill score, for both daily and hourly rainfall, suggesting higher predictability when spatial error among the ensemble members is allowed. © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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In terms of the Brier score, CEFS_GEFS performs better than CEFS_ERA. The latter lacks spread, especially in regions with scarce rain, resulting in less dispersion in precipitation distributions and larger probability forecast error. When a neighborhood is applied, the probability of precipitation (POP) is significantly increased over Zhengzhou. 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