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Probabilistic Forecasting of El Niño Using Neural Network Models
Author(s) -
Petersik Paul Johannes,
Dijkstra Henk A.
Publication year - 2020
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl086423
Subject(s) - artificial neural network , probabilistic logic , quantile , gaussian , probabilistic forecasting , quantile regression , statistics , prediction interval , statistical model , econometrics , computer science , mathematics , artificial intelligence , physics , quantum mechanics
Abstract We apply Gaussian density neural network and quantile regression neural network ensembles to predict the El Niño–Southern Oscillation. Both models are able to assess the predictive uncertainty of the forecast by predicting a Gaussian distribution and the quantiles of the forecasts, respectively. This direct estimation of the predictive uncertainty for each given forecast is a novel feature in the prediction of the El Niño–Southern Oscillation by statistical models. The predicted mean and median, respectively, show a high‐correlation skill for long lead times ( r =0.5, 12 months) for the 1963–2017 evaluation period. For the 1982–2017 evaluation period, the probabilistic forecasts by the Gaussian density neural network can better estimate the predictive uncertainty than a standard method to assess the predictive uncertainty of statistical models.