Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach
Abstract Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv...
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crwiley:10.1002/joc.7812 2024-06-23T07:54:27+00:00 Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach Chen, Ping Sun, Bo Wang, Huijun Yang, Lianmei National Natural Science Foundation of China 2022 http://dx.doi.org/10.1002/joc.7812 https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7812 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.7812 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7812 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor International Journal of Climatology volume 42, issue 16, page 9200-9214 ISSN 0899-8418 1097-0088 journal-article 2022 crwiley https://doi.org/10.1002/joc.7812 2024-06-04T06:46:42Z Abstract Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982–2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1–2 months. To improve the forecast of spring IOD events, a physical‐empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April–May–June (AMJ) is taken as the predictand, and the CFSv2‐predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2‐predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE‐model‐predicted (CNN‐model‐predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future. Article in Journal/Newspaper laptev Laptev Sea Sea ice Wiley Online Library Indian Laptev Sea International Journal of Climatology 42 16 9200 9214 |
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Open Polar |
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Wiley Online Library |
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crwiley |
language |
English |
description |
Abstract Despite significant advances in seasonal climate forecasts, the reliability of both dynamical and empirical models for the Indian Ocean Dipole (IOD) prediction is still limited to a lead time of one season or less. In this study, the skill of the NCEP Climate Forecast System version 2 (CFSv2) for the IOD prediction during the period 1982–2014 is evaluated. The results indicate that the model performance for the IOD prediction is the worst in spring among the four seasons, which is manifested in the fact that a skilful prediction of spring IOD event is limited to a lead time of only about 1–2 months. To improve the forecast of spring IOD events, a physical‐empirical (PE) model and a convolutional neural network (CNN) model are established in the present study. The IOD in April–May–June (AMJ) is taken as the predictand, and the CFSv2‐predicted sea surface height (SSH) in AMJ and the observed Laptev sea ice in the preceding December are used as the two predictors. The original CFSv2‐predicted IOD time series has an insignificant correlation with the observed IOD time series with a temporal correlation coefficient (TCC) of 0.03; the PE model (CNN model) can largely improve the IOD prediction with a TCC of 0.74 (0.77) between the PE‐model‐predicted (CNN‐model‐predicted) IOD and the observed IOD during AMJ. Thus, the PE model and the CNN model developed in the present study can be applied to improve the IOD predictions from numerical models in the future. |
author2 |
National Natural Science Foundation of China |
format |
Article in Journal/Newspaper |
author |
Chen, Ping Sun, Bo Wang, Huijun Yang, Lianmei |
spellingShingle |
Chen, Ping Sun, Bo Wang, Huijun Yang, Lianmei Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
author_facet |
Chen, Ping Sun, Bo Wang, Huijun Yang, Lianmei |
author_sort |
Chen, Ping |
title |
Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
title_short |
Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
title_full |
Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
title_fullStr |
Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
title_full_unstemmed |
Improving the CFSv2 prediction of the Indian Ocean Dipole based on a physical‐empirical model and a deep‐learning approach |
title_sort |
improving the cfsv2 prediction of the indian ocean dipole based on a physical‐empirical model and a deep‐learning approach |
publisher |
Wiley |
publishDate |
2022 |
url |
http://dx.doi.org/10.1002/joc.7812 https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7812 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/joc.7812 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7812 |
geographic |
Indian Laptev Sea |
geographic_facet |
Indian Laptev Sea |
genre |
laptev Laptev Sea Sea ice |
genre_facet |
laptev Laptev Sea Sea ice |
op_source |
International Journal of Climatology volume 42, issue 16, page 9200-9214 ISSN 0899-8418 1097-0088 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/joc.7812 |
container_title |
International Journal of Climatology |
container_volume |
42 |
container_issue |
16 |
container_start_page |
9200 |
op_container_end_page |
9214 |
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1802646622359781376 |