Refining Seasonal Precipitation Forecast in Brazil Using Simple Data-Driven Techniques and Climate Indices

• Published in: Revista Brasileira de Meteorologia 2024, Vol.39, p.1
• Main Authors: Torres, Francisca Lanai Ribeiro, Kuki, Cassia Akemi Castro, Reboita, Michelle Simões, Lima, Luana Medeiros Marangon, Lima, José Wanderley Marangon, de Queiroz, Anderson Rodrigo
• Format: Article
• Language: eng ; por
• Subjects: Accuracy; Climate models; Climate prediction; Climatic indexes; Daily precipitation; data-driven models; El Nino; El Nino phenomena; Hydroelectric power; Precipitation; Precipitation forecasting; Predictions; Refining; Regression analysis; Resource management; SEAS5; Seasonal forecasting; Seasonal precipitation; seasonal precipitation forecast; Southern Oscillation; Support vector machines; Teleconnection patterns; time series forecasting; Water resources; Water resources management; Weather forecasting
• ISSN: 0102-7786; 1982-4351
• DOI: 10.1590/0102-77863910052

Seasonal precipitation forecasts are essential for water resource management, agricultural activities, and the operational planning of hydropower systems. Any methodological advancement that enhances the accuracy of precipitation predictions will yield considerable societal benefits. In this context, this study proposes and evaluates two approaches for refining seasonal precipitation forecasts in Brazil, using simple data-based models, such as multiple linear regression (MLR) and nonlinear support vector machine (SVM). These models employ climate indices related to different teleconnection patterns that affect seasonal precipitation in Brazil, the unified gauge-based analysis of global daily precipitation from the Climate Prediction Center (CPC), and the precipitation forecasts from the Seasonal Forecast System 5 (SEAS5) as input variables. Both MLR and SVM models were validated from Jan-2017 to Dec-2020 using precipitation from the CPC as ground truth. The results suggest that, compared to SEAS5, MLR and SVM models enhance predictive accuracy and reduce bias in precipitation forecasts for the Southeast, Midwest, and North regions of Brazil during the austral summer. However, the performance of the models was found to be on par with the original predictions of SEAS5 in the Northeast and South regions, sectors of Brazil where the climate is significantly influenced by the El Niño-Southern Oscillation.