Enhancement and Error Estimation of Neural Network Prediction of Niño-3.4 SST Anomalies

Abstract

A procedure to enhance neural network (NN) predictions of tropical Pacific sea surface temperature anomalies and calculating their estimated errors is presented. A simple linear correction enables more accurate predictions of warm and cold events but can result in introduction of larger errors in other cases. The prediction error estimates aid recognizing erroneously magnified anomalies and are used to sort the predictions into El Niño, La Niña, and neutral states. The error estimation process is based on bootstrap resamplings of the data and construction of a large number of bootstrap prediction replicas. A statistic calculated on the set of bootstrap replicas that corresponds to each of the actual predictions is used to estimate the prediction?s errors. The method is demonstrated on NN prediction of the Niño-3.4 index.