A Machine Learning Augmented Data Assimilation Method for High-Resolution Observation | CU Experts

The accuracy of initial conditions is an important driver of the; forecast skill of numerical weather prediction models. Increases in the; quantity of available measurements, in particular high-resolution remote; sensing observational data products from satellites, are valuable inputs; for improving those initial condition estimates. However, the data; assimilation methods used for integrating observations into forecast; models are computationally expensive. This makes incorporating dense; observations into operational forecast systems challenging, and it is; often prohibitively time-consuming. As a result, large quantities of; data are discarded and not used for state initialization. We; demonstrate, using the Lorenz-96 system for testing, that a simple; machine learning method can be trained to assimilate high-resolution; data. Using it to do so improves both initial conditions and forecast; accuracy. Compared to using the Ensemble Kalman Filter with; high-resolution observations ignored, our augmented method has an; average root-mean-squared error reduced by 15%. Ensemble forecasts; using initial conditions generated by the augmented method are more; accurate and reliable at up to 10 days of forecast lead time.

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