Abstract
A possibility of the early prediction of El Niño and La Niña phenomena taking into account their two types (Central Pacific and East Pacific ones) is studied. The prognostic model is based on the method of artificial neural networks. A set of global climate indices for the period of 1950–2019 is used as the input parameters of the model. The indices are calculated using monthly mean NCEP/NCAR reanalysis data on 500 hPa geopotential height and sea-level pressure. The model is verified for the period of 2010–2019. A possibility of the successful forecast of Niño 3.4, Niño 4, and Niño 3 indices with a lead time of 3 to 9 months is shown. The quality of the forecast decreases as its lead time increases, but remains generally high. It is found that the model can predict well the El Niño beginning and evolution and, a bit worse, La Niña events; in this case, the number of La Niña events is overestimated as the forecast lead time increases.
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Funding
A.S. Lubkov is the winner of the contest of young scientists’ reports at the International Youth School and Conference on Computing and Information Technologies for Environmental Sciences “CITES-2019” on May 27–June 6, 2019, Moscow, Russia (http://www.scert.ru/ru/conference/cites2019/). The research was funded by Russian Foundation for Basic Research (projects No. 18-45-920063 and No. 20-45-920015) and by the state assignment of the Institute of Natural Technical Systems within the research theme No. 0012-2019-0007.
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Russian Text ©The Author(s), 2020, published in Meteorologiya i Gidrologiya, 2020, No. 11, pp. 111-121.
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Lubkov, A.S., Voskresenskaya, E.N. & Marchukova, O.V. Forecasting El Niño/La Niña and Their Types Using Neural Networks. Russ. Meteorol. Hydrol. 45, 806–813 (2020). https://doi.org/10.3103/S1068373920110084
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DOI: https://doi.org/10.3103/S1068373920110084