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

Abstract

Characteristics of the West African Monsoon (WAM) season, such as its onset and dry spell occurrences, are notoriously difficult to predict. However, these characteristics are 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 WAM season, there are few that effectively implement this information into machine learning (ML) prediction models. This study is focused on predicting dry spells, that is, if there will be a period of consecutive days without rain after the onset of the WAM. We first investigated the best ways to define onset and dry spells and gathered sea surface temperature training data from both real-world observations and a climate simulation model. Then we constructed an adaptive-threshold logistic regression model for dry spell prediction, to which we applied a custom feature selection method and spatial regularization. Using Leave-One-Out cross validation testing, we found significant results in multiple binary classification metrics. These models overcome some limitations that current approaches have, such as being computationally intensive and needing bias correction. We also aim for this study to serve as a framework for ML use in the context of targeted prediction of certain weather phenomena using climatologically relevant variables.