Neural network forecasts of the tropical Pacific sea surface temperatures

Application of Entropy Ensemble Filter in Neural Network Forecasts of Tropical Pacific Sea Surface Temperatures

Recently, the Entropy Ensemble Filter (EEF) method was proposed to mitigate the computational cost of the Bootstrap AGGregatING (bagging) method. This method uses the most informative training data sets in the model ensemble rather than all ensemble members created by the conventional bagging. In this study, we evaluate, for the first time, the application of the EEF method in Neural Network (N...

Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pear...

The potential benefits of using artificial intelligence for monthly rainfall forecasting for the Bowen Basin, Queensland, Australia

The Bowen Basin contains the largest coal reserves in Australia. Prolonged heavy rainfall during the 2010-2011 wet-season severely affected industry operations with an estimated economic loss of A$5.7 billion (£3.8 billion). There was no explicit warning of the exceptionally wet conditions in the seasonal forecast from the Australian Bureau of Meteorology, which simply suggested a 50-55% probab...

Tropical Pacific Sea-Surface Temperatures and Preceding Sea Level Pressure Anomalies in the Subtropical North Pacific

Long-lead prediction of Pacific SSTs via Bayesian dynamic modeling

Tropical Pacific sea surface temperatures (SSTs) and the accompanying El Nino-Southern Oscillation phenomenon are recognized as significant components of climate behavior. The atmospheric and oceanic processes involved display highly complicated variability over both space and time. Researchers have applied both physically derived modeling and statistical approaches to develop long-lead predict...