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Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model. (arXiv:2203.14814v4 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
The modelling of small-scale processes is a major source of error in climate
models, hindering the accuracy of low-cost models which must approximate such
processes through parameterization. Red noise is essential to many operational
parameterization schemes, helping model temporal correlations. We show how to
build on the successes of red noise by combining the known benefits of
stochasticity with machine learning. This is done using a physically-informed
recurrent neural network within a probabilistic framework. Our model is
competitive and often …
arxiv case case study machine machine learning patterns study temporal