2021 journal article

A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity

ATMOSPHERE, 12(4).

By: X. Sun n , L. Xie n, S. Shah n & X. Shen n 

author keywords: hurricane prediction; machine learning; ensemble model
TL;DR: The results show that neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias, and that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (Web of Science)
Source: Web Of Science
Added: May 17, 2021

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.