Seasonal prediction of Indian summer monsoon onset with echo state networks
Bibliographic Data
| ID | 15547682 |
|---|---|
| Authors | T Mitsui (0000-0002-2825-3996, Potsdam Institute for Climate Impact Research, corresponding author), Niklas Boers (0000-0002-1239-9034, University of Exeter) |
| Year | 2021 |
| Volume | 16 |
| Issue | 7 |
| Pages | 074024-074024 |
| Publication date | 2021-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac0acb |
| OpenAlex | W3179606698 |
| Language | EN |
| References cited | 42 |
Although the prediction of the Indian Summer Monsoon (ISM) onset is of crucial importance for water-resource management and agricultural planning on the Indian sub-continent, the long-term predictability—especially at seasonal time scales—is little explored and remains challenging. We propose a method based on artificial neural networks that provides skilful long-term forecasts (beyond 3 months) of the ISM onset, although only trained on short and noisy data. It is shown that the meridional tropospheric temperature gradient in the boreal winter season already contains the signals needed for predicting the ISM onset in the subsequent summer season. Our study demonstrates that machine-learning-based approaches can be simultaneously helpful for both data-driven prediction and enhancing the process understanding of climate phenomena
Boreal · Climatology · Geography · Meteorology · Monsoon · Monsoon of South Asia · Predictability · Statistics · Term (time · Zonal and meridional · Environmental Science · Hydrological Forecasting Using AI · Mathematics · Meteorological Phenomena and Simulations · Neural Networks and Reservoir Computing · Geology
| Highly cited | No |
|---|