Exploration of Cross-Seasonal Forecasting Methods for Summer Precipitation in the Yangtze River Basin.
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| Title: | Exploration of Cross-Seasonal Forecasting Methods for Summer Precipitation in the Yangtze River Basin. |
|---|---|
| Authors: | Yang, Yawei1 (AUTHOR), Chen, Lijuan2,3 (AUTHOR) chenlj@cma.gov.cn, Liang, Ping1 (AUTHOR), Zhang, Daquan2,3 (AUTHOR) |
| Source: | Journal of Hydrometeorology. Feb2026, Vol. 27 Issue 2, p183-199. 17p. |
| Subjects: | Multiple scale method, Iterative methods (Mathematics), Box-Jenkins forecasting, Hilbert-Huang transform, Forecasting methodology, Meteorological precipitation, Long-range weather forecasting |
| Geographic Terms: | Yangtze River (China), China |
| Abstract: | This study develops a cross-seasonal summer precipitation prediction model for the Yangtze River basin of China by integrating a time-scale separation method. The proposed EEAE method combines the empirical cumulative distribution function (ECDF), ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and empirical orthogonal function (EOF) iteration techniques. First, the ECDF corrects the summer precipitation predictions initialized in August by the BCC_CSM1.1m model, merging them with historical observational data to construct a new time series. The EEMD method then decomposes this series into multiple intrinsic mode functions (IMFs) exhibiting distinct periodic characteristics. For each IMF component, tailored prediction models are developed. IMF1, characterized by quasi-biennial oscillations, is predicted by summing the interannual increment with previous year values. IMF2, displaying 6–8-yr periodicities, is forecasted using the ARIMA method. Slow-varying IMFs with periods exceeding 15 years are corrected through EOF iteration of the model's geopotential height field. The synthesized predictions by the EEAE model demonstrate superior skill compared to ECDF-only corrected forecasts, with consistent results across experiments with varying initialization times. This work highlights that integrating historical data with high-skill dynamical model outputs through time-scale separation can effectively enhance prediction accuracy and extend forecast lead time. Significance Statement: This study advances seasonal precipitation forecasting by developing a novel multiscale prediction framework for the Yangtze River basin. Our key innovation lies in decomposing precipitation variability into distinct cyclical components and constructing tailored prediction models for each scale, effectively combining climate model outputs with historical observations. The approach enables skillful summer precipitation forecasts with a lead time of up to 10 months, significantly earlier than conventional methods, while improving prediction accuracy. This work demonstrates the potential of scale-aware modeling to extend forecast horizons and improve accuracy, offering a valuable reference for water resource management and climate adaptation strategies. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Hydrometeorology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192126177 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploration of Cross-Seasonal Forecasting Methods for Summer Precipitation in the Yangtze River Basin. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yawei%22">Yang, Yawei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Lijuan%22">Chen, Lijuan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> chenlj@cma.gov.cn</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Ping%22">Liang, Ping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Daquan%22">Zhang, Daquan</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Hydrometeorology%22">Journal of Hydrometeorology</searchLink>. Feb2026, Vol. 27 Issue 2, p183-199. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+scale+method%22">Multiple scale method</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Box-Jenkins+forecasting%22">Box-Jenkins forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Hilbert-Huang+transform%22">Hilbert-Huang transform</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Meteorological+precipitation%22">Meteorological precipitation</searchLink><br /><searchLink fieldCode="DE" term="%22Long-range+weather+forecasting%22">Long-range weather forecasting</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Yangtze+River+%28China%29%22">Yangtze River (China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study develops a cross-seasonal summer precipitation prediction model for the Yangtze River basin of China by integrating a time-scale separation method. The proposed EEAE method combines the empirical cumulative distribution function (ECDF), ensemble empirical mode decomposition (EEMD), autoregressive integrated moving average (ARIMA), and empirical orthogonal function (EOF) iteration techniques. First, the ECDF corrects the summer precipitation predictions initialized in August by the BCC_CSM1.1m model, merging them with historical observational data to construct a new time series. The EEMD method then decomposes this series into multiple intrinsic mode functions (IMFs) exhibiting distinct periodic characteristics. For each IMF component, tailored prediction models are developed. IMF1, characterized by quasi-biennial oscillations, is predicted by summing the interannual increment with previous year values. IMF2, displaying 6–8-yr periodicities, is forecasted using the ARIMA method. Slow-varying IMFs with periods exceeding 15 years are corrected through EOF iteration of the model's geopotential height field. The synthesized predictions by the EEAE model demonstrate superior skill compared to ECDF-only corrected forecasts, with consistent results across experiments with varying initialization times. This work highlights that integrating historical data with high-skill dynamical model outputs through time-scale separation can effectively enhance prediction accuracy and extend forecast lead time. Significance Statement: This study advances seasonal precipitation forecasting by developing a novel multiscale prediction framework for the Yangtze River basin. Our key innovation lies in decomposing precipitation variability into distinct cyclical components and constructing tailored prediction models for each scale, effectively combining climate model outputs with historical observations. The approach enables skillful summer precipitation forecasts with a lead time of up to 10 months, significantly earlier than conventional methods, while improving prediction accuracy. This work demonstrates the potential of scale-aware modeling to extend forecast horizons and improve accuracy, offering a valuable reference for water resource management and climate adaptation strategies. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Hydrometeorology is the property of American Meteorological Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1175/JHM-D-25-0048.1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 183 Subjects: – SubjectFull: Multiple scale method Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Box-Jenkins forecasting Type: general – SubjectFull: Hilbert-Huang transform Type: general – SubjectFull: Forecasting methodology Type: general – SubjectFull: Meteorological precipitation Type: general – SubjectFull: Long-range weather forecasting Type: general – SubjectFull: Yangtze River (China) Type: general – SubjectFull: China Type: general Titles: – TitleFull: Exploration of Cross-Seasonal Forecasting Methods for Summer Precipitation in the Yangtze River Basin. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Yawei – PersonEntity: Name: NameFull: Chen, Lijuan – PersonEntity: Name: NameFull: Liang, Ping – PersonEntity: Name: NameFull: Zhang, Daquan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1525755X Numbering: – Type: volume Value: 27 – Type: issue Value: 2 Titles: – TitleFull: Journal of Hydrometeorology Type: main |
| ResultId | 1 |
