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Despite the rapid growth of machine learning (ML) and its far-reaching applications in various fields such as healthcare, finance, and urban heat management, there are still some unresolved challenges in the field of climate change. Reliable subseasonal forecasts of summer temperatures would be a great benefit to society. Although numerical weather prediction (NWP) models are better at capturing relevant sources of predictability, such as temperatures, land, and sea surface conditions, the subseasonal potential is not fully exploited. One such challenge is accurate subseasonal temperature forecasting using cutting-edge ML technology. This study aims to assess and predict the changes in subseasonal temperature during the summer season (from March to June) in Senegal on 2-weeks time scales. Six ML techniques, including linear regression (LR), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent units (GRU), are used. The experiments utilize a multivariate approach by incorporating variables of the ERA-5 dataset from 1981 to 2022. The results compared all the performances of the methods to assess their overall effectiveness in forecasting air temperature (t2m) values over 2 weeks. Our analysis demonstrates that the GRU model outperforms the other ML models, achieving a Nash–Sutcliffe efficiency (NSE) score of 74.68% and a mean absolute percentage error (MAPE) of 2.51%. The GRU model effectively captures long-term dependencies and exhibits superior performance in temperature forecasting. Furthermore, a comparison between the observed and predicted values confirms the accuracy of the GRU model in aligning with actual temperature trends. Overall, this study contributes an impactful deep learning model to the field of subseasonal temperature forecasting in West Africa (Senegal), which offers local authorities the capability to anticipate climatic events and enact preventive measures accordingly.
Kenne et al. (Tue,) studied this question.