bcm4rcm: Bayesian committee machines for regional climate models
An ensemble learning method to combine different regional climate model outputs and produce principled uncertainty estimates of precipitation under different climate scenarios
I’ve been lucky enough to work with some amazing people on a wide variety of topics.
An ensemble learning method to combine different regional climate model outputs and produce principled uncertainty estimates of precipitation under different climate scenarios
Large-scale circulation patterns are used to make precipitation projections while contrasting flexible non-stationary covariance functions with methods incorporating domain knowledge
Formalising the decision-making process of experienced Gaussian Processes users with an emphasis on kernel design and computational scalability
A pipeline for the identification, forecasting and causal prediction of pyrocumulonimbus clouds generated by extreme wildfires
Downscaling precipitation using multi-fidelity Gaussian processes by combining data from multiple sources to increase prediction accuracy and provide uncertainty distributions over ungauged areas
White paper on investment opportunities to support locally in community infrastructure and nature-based projects that reduce carbon emissions at their source or actively sequester carbon
Deep learning model to identify clouds over polar regions using satellite data from the Sentinel 3 SLSTR instrument