Exploring Machine Learning Approaches to Precipitation Prediction: Post Processing of Daily Accumulated North American forecasts
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Goomer, Rushil
Date
2023-11-22Citation
Goomer, Rushil. Exploring Machine Learning Approaches to Precipitation Prediction: Post Processing of Daily Accumulated North American forecasts; A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Department of Applied Computer Science. Winnipeg, Manitoba, Canada: University of Winnipeg, 2023. DOI: 10.36939/ir.202311221657.
Abstract
This thesis presents recent work on exploring machine learning (ML) and deep learning (DL) models to improve the accuracy of 24 hour precipitation forecasts. Leveraging a comprehensive North American dataset of precipitation values from Numerical Weather Prediction (NWP) models and secondary meteorological features, the research showcases the need of ML techniques in post-processing NWP precipitation predictions. The evaluation reveals remarkable performance improvements over baseline model, with certain ML models achieving a 15% reduction in Mean Absolute Error (MAE), a 5% decrease in Root Mean Squared Error (RMSE), a 45% reduction in Median Absolute Error (MdAE), and a 50% decrease in Relative Bias (RB). Convolutional Neural Networks (CNN) and Gradient Boosting Regressor (GBR) emerged as top performers, demonstrating their proficiency in accurately predicting daily precipitation.