Abstract
For more than 35 years, the Met Office has been generating and delivering forecasts of road weather hazards, using a physics-based surface-exchange-scheme model. Currently producing forecasts at a new location requires a long initialisation period. However, this can be reduced by providing the model with accurate estimates of initial road surface temperatures. In this paper, we describe a neural network model we have developed to quickly translate readily available atmospheric forecast information into initial road surface temperature estimates. In this way, we combine the advantages of a traditional physics-based approach with the speed of a machine learning approach.
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