Machine Learning Approaches to Improve North American Precipitation Forecasts

Numerical weather prediction (NWP) is a challenging task which involves working with micro and macro-scale spatio-temporal parameters susceptible to biases and accuracy problems. In recent years, machine learning has grown in popularity with the increasing demand in accurate weather predictions. In this study, we adopt a multimodel (ensemble) forecasting approach by collecting precipitation data from multiple NWP models of Canadian, American and European weather agencies in an effort to deploy an optimal machine learning-based weather model for real-time precipitation forecasting that will outperform the baseline. We considered 8 NWP models as inputs and combined them to create ensemble predictors using 5 different machine learning techniques along with a baseline model (mean of eight input NWP models). We demonstrate that machine learning approaches can improve upon the results of the individual NWP models. The best results were obtained by the neural-network variants with 17% improvement in the mean absolute error, 3% in the root mean squared error, 47% in the median absolute error, 5% in the maximum error, 70% in the relative bias, 41% in the false alarm ratio and 8% in the critical score index over the baseline. Neural networks also complied with the practicality constraints, with minutes of training time and near-real time prediction time.

Automated summary

In this study, a multimodel (ensemble) forecasting approach was adopted to deploy an optimal machine learning-based weather model for real-time precipitation forecasting. Machine learning techniques were used to combine precipitation data from multiple NWP models and improve upon their individual results. The best results were achieved by neural network variants, which showed significant improvements in various error metrics compared to the baseline model.