Abstract
This work describes a process for efficiently fine-tuning the GraphCast data-driven numerical weather prediction model to be consistent with another analysis system, here the Global Deterministic Prediction System (GDPS) of Environment and Climate Change Canada (ECCC). The GDPS system was significantly upgraded in 2019, so there is only a limited period of operational data to use for model training. This work considers the effect of using two years of training data (July 2019–December 2021) and a restricted computational budget to tune the 37-level, quarter-degree version of GraphCast with an empirically-determined vertical weighting of model error. The GDPS-tuned model significantly outperforms both the operational (traditional) forecast and the unmodified GraphCast model when initialized with the GDPS analysis, showing significant forecast skill in the troposphere over 1 to 10-day lead times. This fine-tuning is accomplished through an abbreviation of the original training curriculum, relying on a shorter single-step forecast stage to complete most of the adaptation, followed by a consolidation of the forecast-lengthening stages into separate 12h, 1d, 2d, and 3d stages. In addition, a “control” run trained with ERA5 data shows that fine-tuning on recent data improves forecast skill on a going-forward basis.
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