Verifying the spatial structure of precipitation fields from a foundation model of the atmosphere

Data driven weather models have proven their ability to learn various aspects of the weather prediction problem. While their point-to-point skill has been proven, the precise nature of their errors is not yet fully understood.

This contribution takes a first look at the spatial precipitation patterns simulated by the Weather Generator – a foundation model trained on diverse data sources with the goal of learning the underlying behavior of the atmosphere as a whole.  We analyze the correlation structure of the simulated precipitation fields using spatial verification techniques including two-dimensional wavelet transforms. Some attention is paid to the problem of applying these methods to global data on an irregular grid. The results can be compared to observations, reanalysis and potentially other data-driven forecast models.