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Found 2 papers, 1 papers with code
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data
However, as the number of available observation sites increases, so too does the opportunity for data quality issues to emerge, particularly given that many of these sensors do not have the benefit of official maintenance teams.
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes.
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