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Forecasting the weather is an increasingly data intensive exercise. NumericalWeather Prediction (NWP) models are becoming more complex, with higherresolutions, and there are increasing numbers of different models in operation.While the forecasting skill of NWP models continues to improve, the number andcomplexity of these models poses a new challenge for the operationalmeteorologist: how should the information from all available models, each withtheir own unique biases and limitations, be combined in order to providestakeholders with well-calibrated probabilistic forecasts to use in decisionmaking? In this paper, we use a road surface temperature example to demonstratea three-stage framework that uses machine learning to bridge the gap betweensets of separate forecasts from NWP models and the 'ideal' forecast fordecision support: probabilities of future weather outcomes. First, we useQuantile Regression Forests to learn the error profile of each numerical model,and use these to apply empirically-derived probability distributions toforecasts. Second, we combine these probabilistic forecasts using quantileaveraging. Third, we interpolate between the aggregate quantiles in order togenerate a full predictive distribution, which we demonstrate has propertiessuitable for decision support. Our results suggest that this approach providesan effective and operationally viable framework for the cohesivepost-processing of weather forecasts across multiple models and lead times toproduce a well-calibrated probabilistic output.

philosophical transactions - royal society. mathematical, physical and engineering sciences/philosophical transactions - royal society. mathematical, physical and engineering sciences

A framework for probabilistic weather forecast post-processing across models and lead times using machine learning