Using network theory and machine learning to predict El Niño

Autor:	Nooteboom, P.D., Feng, Q., López, Cristóbal, Hernández-García, Emilio, Dijkstra, H.A., Sub Physical Oceanography, Sub Onderzoek- en Valorisatiebeleid, Marine and Atmospheric Research
Přispěvatelé:	Ministerio de Economía y Competitividad (España), European Commission, Sub Physical Oceanography, Sub Onderzoek- en Valorisatiebeleid, Marine and Atmospheric Research
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	010504 meteorology & atmospheric sciences lcsh:Dynamic and structural geology Computer science Lag Forecast skill 02 engineering and technology Network theory Machine learning computer.software_genre 01 natural sciences lcsh:QE500-639.5 0202 electrical engineering electronic engineering information engineering Autoregressive–moving-average model Autoregressive integrated moving average lcsh:Science 0105 earth and related environmental sciences Mathematics Artificial neural network business.industry lcsh:QE1-996.5 lcsh:Geology Physics - Atmospheric and Oceanic Physics El Niño Southern Oscillation Ensemble prediction General Earth and Planetary Sciences 020201 artificial intelligence & image processing lcsh:Q Artificial intelligence business computer Lead time
Zdroj:	Earth System Dynamics, Vol 9, Pp 969-983 (2018) Digital.CSIC. Repositorio Institucional del CSIC instname Earth System Dynamics, 9(3), 969. Copernicus GmbH
ISSN:	2190-4979 2190-4987
Popis:	The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag time of 6 months. In this paper, we aim to increase this prediction skill at lag times of up to 1 year. The new method combines a classical autoregressive integrated moving average technique with a modern machine learning approach (through an artificial neural network). The attributes in such a neural network are derived from knowledge of physical processes and topological properties of climate networks, and they are tested using a Zebiak–Cane-type model and observations. For predictions up to 6 months ahead, the results of the hybrid model give a slightly better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Interestingly, results for a 12-month lead time prediction have a similar skill as the shorter lead time predictions. Cristóbal López and Emilio Hernández-García acknowledge support from Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional through the LAOP project (CTM2015-66407-P, MINECO/FEDER).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::392db1b456b424bdf8bb6e15414f0b49 https://hdl.handle.net/1874/367881 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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