Epistemic and Aleatoric Uncertainties in Weather and Climate
Published:
Using Bayesian Neural Networks to learn epistemic and aleatoric uncertainties in atmospheric parameterisations
Posts
Uncertainties in weather and climate modelling
Published:
What uncertainties are there in weather and climate models?
What is the Lorenz-96 System?
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Why we still use the Lorenz 1996 model as a toy model of atmospheric circulation.
Using Diffusion Models for Climate Data Downscaling
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We used generative diffusion models to sharpen coarse climate data over the U.S., showing they can recover fine-scale weather features and provide useful uncertainty estimates for risk analysis.
Uncertainty Quantification of a Machine Learning Parameterization
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Machine learning parameterizations are becoming a popular technique for improving climate models. This work aims to learn the uncertainties associated with them.
Comparison of Calibration Methods for Gravity Wave Parameterization
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Work led by Rob King to compare calibration and uncertainty quantification techniques for a gravity wave parameterization
Calibration and Uncertainty Quantification of Gravity Wave Parameterization
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Visualizations of Calibrate, Emulate and Sample method used in Mansfield & Sheshadri, 2022.
Climate response emulator
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A video of the emissions - response emulator built during my PhD.