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  Seasonal prediction of Indian summer monsoon onset with echo state networks

Mitsui, T., Boers, N. (2021): Seasonal prediction of Indian summer monsoon onset with echo state networks. - Environmental Research Letters, 16, 7, 074024.
https://doi.org/10.1088/1748-9326/ac0acb

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_25756 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_25756_2
Genre: Journal Article

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 Creators:
Mitsui, Takahito1, Author              
Boers, Niklas1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: 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.

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 Dates: 2021-06-112021-07-012021-07-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1088/1748-9326/ac0acb
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: FutureLab - Artificial Intelligence in the Anthropocene
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Monsoon
Regional keyword: Asia
Model / method: Machine Learning
MDB-ID: No data to archive
OATYPE: Gold Open Access
 Degree: -

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Title: Environmental Research Letters
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 16 (7) Sequence Number: 074024 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150326
Publisher: IOP Publishing