Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
  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

Item is

Dateien

einblenden: Dateien
ausblenden: Dateien
:
25756oa.pdf (Verlagsversion), 3MB
Name:
25756oa.pdf
Beschreibung:
-
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Copyright Datum:
-
Copyright Info:
-

Externe Referenzen

einblenden:

Urheber

einblenden:
ausblenden:
 Urheber:
Mitsui, Takahito1, Autor              
Boers, Niklas1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: 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.

Details

einblenden:
ausblenden:
Sprache(n):
 Datum: 2021-06-112021-07-012021-07-01
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

Veranstaltung

einblenden:

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Environmental Research Letters
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 16 (7) Artikelnummer: 074024 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/150326
Publisher: IOP Publishing