The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China
Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administrat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhu, Zhiwei [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Climate dynamics - Berlin : Springer, 1986, 62(2023), 1 vom: 25. Juli, Seite 69-86 |
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| Übergeordnetes Werk: |
volume:62 ; year:2023 ; number:1 ; day:25 ; month:07 ; pages:69-86 |
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| DOI / URN: |
10.1007/s00382-023-06900-w |
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SPR054485681 |
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| 245 | 1 | 4 | |a The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China |
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| 520 | |a Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. | ||
| 650 | 4 | |a 10–30-day boreal summer intraseasonal oscillation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extended-range forecast |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extreme rainfall over southern China |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Wu, Junting |4 aut | |
| 700 | 1 | |a Huang, Hongjie |4 aut | |
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10.1007/s00382-023-06900-w doi (DE-627)SPR054485681 (SPR)s00382-023-06900-w-e DE-627 ger DE-627 rakwb eng Zhu, Zhiwei verfasserin aut The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 Wu, Junting aut Huang, Hongjie aut Enthalten in Climate dynamics Berlin : Springer, 1986 62(2023), 1 vom: 25. Juli, Seite 69-86 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2023 number:1 day:25 month:07 pages:69-86 https://dx.doi.org/10.1007/s00382-023-06900-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 1 25 07 69-86 |
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10.1007/s00382-023-06900-w doi (DE-627)SPR054485681 (SPR)s00382-023-06900-w-e DE-627 ger DE-627 rakwb eng Zhu, Zhiwei verfasserin aut The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 Wu, Junting aut Huang, Hongjie aut Enthalten in Climate dynamics Berlin : Springer, 1986 62(2023), 1 vom: 25. Juli, Seite 69-86 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2023 number:1 day:25 month:07 pages:69-86 https://dx.doi.org/10.1007/s00382-023-06900-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 1 25 07 69-86 |
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10.1007/s00382-023-06900-w doi (DE-627)SPR054485681 (SPR)s00382-023-06900-w-e DE-627 ger DE-627 rakwb eng Zhu, Zhiwei verfasserin aut The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 Wu, Junting aut Huang, Hongjie aut Enthalten in Climate dynamics Berlin : Springer, 1986 62(2023), 1 vom: 25. Juli, Seite 69-86 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2023 number:1 day:25 month:07 pages:69-86 https://dx.doi.org/10.1007/s00382-023-06900-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 1 25 07 69-86 |
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10.1007/s00382-023-06900-w doi (DE-627)SPR054485681 (SPR)s00382-023-06900-w-e DE-627 ger DE-627 rakwb eng Zhu, Zhiwei verfasserin aut The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 Wu, Junting aut Huang, Hongjie aut Enthalten in Climate dynamics Berlin : Springer, 1986 62(2023), 1 vom: 25. Juli, Seite 69-86 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2023 number:1 day:25 month:07 pages:69-86 https://dx.doi.org/10.1007/s00382-023-06900-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 1 25 07 69-86 |
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10.1007/s00382-023-06900-w doi (DE-627)SPR054485681 (SPR)s00382-023-06900-w-e DE-627 ger DE-627 rakwb eng Zhu, Zhiwei verfasserin aut The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 Wu, Junting aut Huang, Hongjie aut Enthalten in Climate dynamics Berlin : Springer, 1986 62(2023), 1 vom: 25. Juli, Seite 69-86 (DE-627)268128561 (DE-600)1471747-5 1432-0894 nnns volume:62 year:2023 number:1 day:25 month:07 pages:69-86 https://dx.doi.org/10.1007/s00382-023-06900-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_63 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_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_612 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 62 2023 1 25 07 69-86 |
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Enthalten in Climate dynamics 62(2023), 1 vom: 25. Juli, Seite 69-86 volume:62 year:2023 number:1 day:25 month:07 pages:69-86 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">10–30-day boreal summer intraseasonal oscillation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extended-range forecast</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extreme rainfall over southern China</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Junting</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Hongjie</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Climate dynamics</subfield><subfield code="d">Berlin : Springer, 1986</subfield><subfield code="g">62(2023), 1 vom: 25. 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Zhu, Zhiwei |
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Zhu, Zhiwei misc 10–30-day boreal summer intraseasonal oscillation misc Extended-range forecast misc Extreme rainfall over southern China The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China |
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The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China 10–30-day boreal summer intraseasonal oscillation (dpeaa)DE-He213 Extended-range forecast (dpeaa)DE-He213 Extreme rainfall over southern China (dpeaa)DE-He213 |
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misc 10–30-day boreal summer intraseasonal oscillation misc Extended-range forecast misc Extreme rainfall over southern China |
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influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern china |
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The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China |
| abstract |
Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Based on deterministic and probabilistic forecast verification, we investigated the performance of three subseasonal-to-seasonal (S2S) operational models, i.e., the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) and two models of the China Meteorological Administration (CMA1.0 and CMA2.0), in the extended-range forecast of extreme rainfall over southern China (SCER) while considering the modulation of 10–30-day boreal summer intraseasonal oscillation (BSISO2). The Heidke Skill Score (HSS) of the SCER in the ECMWF, CMA2.0, and CMA1.0 models decreased to less than 0.1 at lead times of 13, 9, and 6 days, respectively. Similarly, the useful prediction skill of the BSISO2 index in the ECMWF, CMA1.0, and CMA2.0 models was up to 15, 13, and 8 days in advance, respectively. The BSISO2’s phase error, rather than the amplitude error, determines its prediction skill. The HSS of the BSISO2 index is significantly correlated with that of SCER in all three S2S models, suggesting that the prediction skill of SCER is influenced by that of BSISO2. The ECMWF shows much higher skill than the two CMA models do in predicting the SCER probability changes under the influence of BSISO2 during Phases 5–7, with the useful prediction skill having up to a 10-day lead time. In contrast, CMA1.0 and CMA2.0 can only predict the modulation of BSISO2 on the SCER probability within a week. The prediction skill of BSISO2’s modulation on SCER largely relies on moisture convergence, rather than on moisture advection. This study highlighted the importance of model’s accurate representation of BSISO2 and its associated moisture convergence for improving extended-range forecast of SCER. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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The influence of 10–30-day boreal summer intraseasonal oscillation on the extended-range forecast skill of extreme rainfall over southern China |
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https://dx.doi.org/10.1007/s00382-023-06900-w |
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Wu, Junting Huang, Hongjie |
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10.1007/s00382-023-06900-w |
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| score |
7.4007006 |