36 citations · 38 across the 4 of their papers we have counts for
4 papers
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data
Charlie Kirkwood, Theo Economou, Henry Odbert +1
As the costs of sensors and associated IT infrastructure decreases - as exemplified by the Internet of Things - increasing volumes of observational data are becoming available for…
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Charlie Kirkwood, Theo Economou, Nicolas Pugeault
For geospatial modelling and mapping tasks, variants of kriging - the spatial interpolation technique developed by South African mining engineer Danie Krige - have long been regard…
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
Charlie Kirkwood, Theo Economou, Henry Odbert +1
Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are incr…
Deep covariate-learning: optimising information extraction from terrain texture for geostatistical modelling applications
Charlie Kirkwood
Where data is available, it is desirable in geostatistical modelling to make use of additional covariates, for example terrain data, in order to improve prediction accuracy in the…