Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall
The prediction of the Indian summer monsoon rainfall (ISMR) in the June–September (JJAS) season at long-lead times is challenging. The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197425000394 |
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| author | Kalpesh R. Patil Takeshi Doi J.V. Ratnam Swadhin K. Behera |
| author_facet | Kalpesh R. Patil Takeshi Doi J.V. Ratnam Swadhin K. Behera |
| author_sort | Kalpesh R. Patil |
| collection | DOAJ |
| description | The prediction of the Indian summer monsoon rainfall (ISMR) in the June–September (JJAS) season at long-lead times is challenging. The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill of the models. We attempted to address this issue using a deep learning model based on convolutional neural networks (CNN). An ensemble of JJAS rainfall predictions using the CNN model with a unique custom function showed high skills in predicting ISMR at a long-lead time of 12 months. The predictions had an anomaly correlation coefficient (ACC) exceeding 0.5 at all the lead times from 2 to 17 months. The CNN model predictions could capture the sign and phase of the extreme rainfall events in the study period realistically. Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. The ensemble of CNN ISMR predictions can supplement the predictions of the forecasting centers. |
| format | Article |
| id | doaj-art-233caf13f7cf46638c087f9ec5fdf63c |
| institution | DOAJ |
| issn | 2590-1974 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-233caf13f7cf46638c087f9ec5fdf63c2025-08-20T03:21:43ZengElsevierApplied Computing and Geosciences2590-19742025-06-012610025710.1016/j.acags.2025.100257Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfallKalpesh R. Patil0Takeshi Doi1J.V. Ratnam2Swadhin K. Behera3Corresponding author. 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001, Japan.; Application Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanApplication Laboratory, VAiG, Japan Agency for Marine-Earth Science and Technology, Yokohama, JapanThe prediction of the Indian summer monsoon rainfall (ISMR) in the June–September (JJAS) season at long-lead times is challenging. The state-of-the-art dynamical models often fail to capture the sign and amplitude of the rainfall anomalies in the extreme rainfall seasons, limiting the overall skill of the models. We attempted to address this issue using a deep learning model based on convolutional neural networks (CNN). An ensemble of JJAS rainfall predictions using the CNN model with a unique custom function showed high skills in predicting ISMR at a long-lead time of 12 months. The predictions had an anomaly correlation coefficient (ACC) exceeding 0.5 at all the lead times from 2 to 17 months. The CNN model predictions could capture the sign and phase of the extreme rainfall events in the study period realistically. Analysis of saliency-based heatmaps indicated the high skill to be due to the model capturing the leading modes of climate variability, such as the Indian Ocean Dipole and El Niño-Southern Oscillation, realistically. The ensemble of CNN ISMR predictions can supplement the predictions of the forecasting centers.http://www.sciencedirect.com/science/article/pii/S2590197425000394Rainfed agricultureIndia meteorological departmentCost function |
| spellingShingle | Kalpesh R. Patil Takeshi Doi J.V. Ratnam Swadhin K. Behera Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall Applied Computing and Geosciences Rainfed agriculture India meteorological department Cost function |
| title | Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall |
| title_full | Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall |
| title_fullStr | Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall |
| title_full_unstemmed | Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall |
| title_short | Enhancing Indian summer monsoon prediction: Deep learning approach for skillful long-lead forecasts of rainfall |
| title_sort | enhancing indian summer monsoon prediction deep learning approach for skillful long lead forecasts of rainfall |
| topic | Rainfed agriculture India meteorological department Cost function |
| url | http://www.sciencedirect.com/science/article/pii/S2590197425000394 |
| work_keys_str_mv | AT kalpeshrpatil enhancingindiansummermonsoonpredictiondeeplearningapproachforskillfullongleadforecastsofrainfall AT takeshidoi enhancingindiansummermonsoonpredictiondeeplearningapproachforskillfullongleadforecastsofrainfall AT jvratnam enhancingindiansummermonsoonpredictiondeeplearningapproachforskillfullongleadforecastsofrainfall AT swadhinkbehera enhancingindiansummermonsoonpredictiondeeplearningapproachforskillfullongleadforecastsofrainfall |