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Found 4 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.
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Here we demonstrate the power of feature learning in a geostatistical context, by showing how deep neural networks can automatically learn the complex relationships between point-sampled target variables and gridded auxiliary variables (such as those provided by remote sensing), and in doing so produce detailed maps of chosen target variables.
Deep covariate-learning: optimising information extraction from terrain texture for geostatistical modelling applications
However, given the complexities of the natural world, chance dictates that the use of 'off-the-shelf' filters is unlikely to derive covariates that provide strong explanatory power to the target variable at hand, and any attempt to manually design informative covariates is likely to be a trial-and-error process -- not optimal.
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.
