Assessment of machine learning-based approaches to improve sub-seasonal to seasonal forecasting of precipitation in Senegal
In one sentence. Ridge regression on atmospheric and oceanic predictors outperforms GCM-based S2S precipitation forecasts for Senegal's monsoon season, offering a cheaper complement to dynamical systems.
Abstract
Abstract. In Senegal, the West African monsoon (WAM) season is characterized by pronounced subseasonal to seasonal (S2S) rainfall fluctuations in response to complex interactions between large-scale atmospheric and oceanic variability patterns and mesoscale convective systems. Indeed, the general circulation models (GCMs) used in the development of S2S forecasting systems often struggle to represent the mechanisms yielding WAM predictability. This study explores the potential of machine learning (ML) approaches to improve S2S precipitation forecasting in Senegal. We evaluate a set of ML models, including ridge regression, linear regression, random forest, support vector machine, Adaboost, and multilayer perceptron for S2S forecasting of precipitation during the monsoon season. To this aim, we use a combination of high-resolution global precipitation estimates from ground and satellite observations, along with atmospheric and oceanic reanalysis products. Our methodology relies on a non-filtering approach to extract significant S2S signals as predictors, enabling real-time application. We demonstrate that integrating different predictor variables from a range of atmospheric and oceanic fields significantly enhances prediction skill. Notably, the ridge regression model outperforms state-of-the-art GCM-derived S2S predictions. The study highlights the potential for developing operational S2S forecasting systems for West African precipitation using ML techniques to complement GCM-based forecast systems, offering valuable tools for climate risk anticipation and water resource management. Such ML-based systems not only provide skillful predictions but are also computationally more efficient compared to GCMs, and can be extended to diverse climatic zones.
Labels
- Topic
- Sub-seasonal to seasonal, Benchmarks, datasets & evaluation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- ERA5 / reanalysis, Satellite, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.5194/egusphere-2024-4040 OpenAlex:W4408043976
- Added
- 2026-10-22
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