Predicting Onsets and Dry Spells of the West African Monsoon Season Using Machine Learning Methods

TL;DR

This study develops machine learning models to predict the onset and dry spells of the West African monsoon season using sea surface temperature data, improving prediction accuracy and efficiency.

Contribution

It introduces a framework for applying ML to predict monsoon onsets and dry spells, utilizing novel variable definitions and statistical techniques.

Findings

Significant skill in dry spell prediction using ML models.

Mixed results for onset prediction with spatial but limited temporal accuracy.

Models are less computationally intensive and require less bias correction.

Abstract

The beginning of the rainy season and the occurrence of dry spells in West Africa is notoriously difficult to predict, however these are the key indicators farmers use to decide when to plant crops, having a major influence on their overall yield. While many studies have shown correlations between global sea surface temperatures and characteristics of the West African monsoon season, there are few that effectively implement this information into machine learning (ML) prediction models. In this study we investigated the best ways to define our target variables, onset and dry spell, and produced methods to predict them for upcoming seasons using sea surface temperature teleconnections. Defining our target variables required the use of a combination of two well known definitions of onset. We then applied custom statistical techniques -- like total variation regularization and predictor selection -- to the two models we constructed, the first being a linear model and the other an adaptive-threshold logistic regression model. We found mixed results for onset prediction, with spatial verification showing signs of significant skill, while temporal verification showed little to none. For dry spell though, we found significant accuracy through the analysis of multiple binary classification metrics. These models overcome some limitations that current approaches have, such as being computationally intensive and needing bias correction. We also introduce this study as a framework to use ML methods for targeted prediction of certain weather phenomenon using climatologically relevant variables. As we apply ML techniques to more problems, we see clear benefits for fields like meteorology and lay out a few new directions for further research.