Correcting systematic and state-dependent errors in the NOAA FV3-GFS using neural networks | CU Experts

Weather forecasts made with imperfect models contain flow- and; state-dependent errors. Data assimilation (DA) partially corrects these; errors with new information from observations. As such, the corrections,; or “analysis increments’, produced by the DA process embed information; about model errors. An attempt is made here to extract that information; to improve numerical weather prediction. Neural networks (NNs) are; trained to predict corrections to the systematic error in the NOAA’s; FV3-GFS model based on a large set of analysis increments. A simple NN; focusing on an atmospheric column significantly improves the estimated; model error correction relative to a linear baseline. Leveraging; large-scale horizontal flow conditions using a convolutional NN, when; compared to the simple column-oriented NN, does not improve skill in; correcting model error. The sensitivity of model error correction to; forecast inputs is highly localized by vertical level and by; meteorological variable, and the error characteristics vary across; vertical levels. Once trained, the NNs are used to apply an online; correction to the forecast during model integration. Improvements are; evaluated both within a cycled DA system and across a collection of; 10-day forecasts. It is found that applying state-dependent NN-predicted; corrections to the model forecast improves the overall quality of DA and; improves the 10-day forecast skill at all lead times.

VIVO