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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
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cloud-id: cloud identification over polar regions
Deep learning model to identify clouds over polar regions using satellite data from the Sentinel 3 SLSTR instrument
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Cambridgeshire decarbonisation fund: net-zero by 2050
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
Beyond intuition, a framework for applying Gaussian Processes to real-word data
Formalising the decision-making process of experienced Gaussian Processes users with an emphasis on kernel design and computational scalability
Narrowing precipitation uncertainty over High Mountain Asia
Downscaling precipitation using multi-fidelity Gaussian processes by combining data from multiple sources to increase prediction accuracy and provide uncertainty distributions over ungauged areas
Predicting future precipitation in the Upper Indus Basin using Gaussian processes
Large-scale circulation patterns are used to make precipitation projections while contrasting flexible non-stationary covariance functions with methods incorporating domain knowledge
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Seoul Subway Poems
Featured projects
Pyrocast: a machine learning framework for forcasting pyroCb clouds
A pipeline for the identification, forecasting and causal prediction of pyrocumulonimbus clouds generated by extreme wildfires