Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning
Abstract The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden–Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the d...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-31394-1 |
| _version_ | 1827984260832690176 |
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| author | Yang Zhou Qifan Zhao |
| author_facet | Yang Zhou Qifan Zhao |
| author_sort | Yang Zhou |
| collection | DOAJ |
| description | Abstract The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden–Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the dominant S2S variability in the tropics. Besides, significantly periodic features in terms of both intensity and location are identified in 10–40 days for the concurrent variation of the subtropical and polar jet streams over Asia in this study. So far, those signals contribute less and are not fully applied to the S2S prediction. The deep learning (DL) approach, especially the long-short term memory (LSTM) networks, has the ability to take advantage of the information at the previous time to improve the prediction after then. This study presents the application of the DL in the postprocessing of S2S prediction using quasi-periodic signals predicted by the operational model to improve the prediction of minimum 2-m air temperature over Asia. With the help of deep learning, it finds the best weights for the ensemble predictions, and the quasi-periodic signals in the atmosphere can further benefit the S2S operational prediction. |
| first_indexed | 2024-04-09T22:58:06Z |
| format | Article |
| id | doaj.art-209d5680f69d4e7eb214f41874060780 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T22:58:06Z |
| publishDate | 2023-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-209d5680f69d4e7eb214f418740607802023-03-22T11:09:01ZengNature PortfolioScientific Reports2045-23222023-03-0113111510.1038/s41598-023-31394-1Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learningYang Zhou0Qifan Zhao1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and TechnologyCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and TechnologyAbstract The quasi-periodic signals in the earth system could be the predictability source for sub-seasonal to seasonal (S2S) climate prediction because of the connections among the lead-lag time of those signals. The Madden–Julian Oscillation (MJO) is a typical quasi-periodic signal, which is the dominant S2S variability in the tropics. Besides, significantly periodic features in terms of both intensity and location are identified in 10–40 days for the concurrent variation of the subtropical and polar jet streams over Asia in this study. So far, those signals contribute less and are not fully applied to the S2S prediction. The deep learning (DL) approach, especially the long-short term memory (LSTM) networks, has the ability to take advantage of the information at the previous time to improve the prediction after then. This study presents the application of the DL in the postprocessing of S2S prediction using quasi-periodic signals predicted by the operational model to improve the prediction of minimum 2-m air temperature over Asia. With the help of deep learning, it finds the best weights for the ensemble predictions, and the quasi-periodic signals in the atmosphere can further benefit the S2S operational prediction.https://doi.org/10.1038/s41598-023-31394-1 |
| spellingShingle | Yang Zhou Qifan Zhao Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning Scientific Reports |
| title | Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning |
| title_full | Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning |
| title_fullStr | Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning |
| title_full_unstemmed | Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning |
| title_short | Taking advantage of quasi-periodic signals for S2S operational forecast from a perspective of deep learning |
| title_sort | taking advantage of quasi periodic signals for s2s operational forecast from a perspective of deep learning |
| url | https://doi.org/10.1038/s41598-023-31394-1 |
| work_keys_str_mv | AT yangzhou takingadvantageofquasiperiodicsignalsfors2soperationalforecastfromaperspectiveofdeeplearning AT qifanzhao takingadvantageofquasiperiodicsignalsfors2soperationalforecastfromaperspectiveofdeeplearning |