Generalization of OMG-HD beyond CONUS-trained setting

Determine whether OMG-HD, which is trained using Real-Time Mesoscale Analysis (RTMA) labels limited to surface variables over the contiguous United States (CONUS), can generalize to produce accurate forecasts in other geographic regions, particularly in areas at different latitudes and with distinct surface conditions such as oceanic regions.

Background

OMG-HD is trained using RTMA as supervisory labels. RTMA focuses on surface variables and is restricted to the CONUS domain, which inherently constrains the diversity of training targets and regional conditions the model experiences during training.

Because training is limited to CONUS, the authors explicitly flag uncertainty about whether the learned representations and forecasting skill of OMG-HD will transfer to other regions, especially those at different latitude ranges or with substantially different surface characteristics, such as oceans. Establishing this generalization property is essential for expanding OMG-HD from a regional to a broader or global operational context.