Model Training, Data Assimilation, and Forecast Experiments with a Hybrid Atmospheric Model

Abstract

This paper investigates the performance of a unique proof-of-concept hybrid model in a cycling data assimilation scheme. This previously published model combines the Simplified Parameterization, primitive-Equation Dynamics model (SPEEDY) with an ML-based component that itself is capable of modeling the global atmospheric dynamics. Analysis and forecast experiments are carried out assuming that ERA5 reanalyses, interpolated to the model grid, represent the “true” spatiotemporal evolution of the atmosphere. Six-hourly simulated observations are generated for a 30-year training period and a one-year testing period by randomly perturbing the “true” states. To investigate the effect of the training data on the model performance, the model is trained on different data sets in the different experiments: the training data are either ERA5 reanalyses, analyses prepared using SPEEDY for cycling, or analyses prepared using the hybrid model for cycling. The simulated observations are assimilated with a Local Ensemble Transform Kalman Filter (LETKF) and the length of the ensuing forecasts is 10 days in all experiments. The cycled LETKF remains stable for the entire testing period in all experiments. When the hybrid model is trained on ERA5 reanalyses, the biases of the analyses are negligible and the variance of the analysis error is greatly reduced compared to the experiment in which SPEEDY rather than the hybrid model is used for cycling. The gains in analysis accuracy are more modest when the hybrid model is trained on analyses obtained with SPEEDY or a prior trained version of the model. All forecasts with the hybrid model are more accurate than with SPEEDY.