Toward Fully Non-Parametric Data Assimilation for the UFS

Operational forecast centers routinely use numerical models to predict various components of the Earth system, ranging from the geosphere to the atmosphere. Despite the diversity in applications, these models share two common characteristics: (1) they generally rely on physical laws to govern the time-rate-of change of prognostic state variables and (2) “data assimilation” guides how environmental measurements inform estimates of initial conditions, boundary conditions, or unknown model parameters. Often, the relative skill of operational models (e.g., comparisons of two or more global weather prediction systems) is dominated by algorithmic choices made during data assimilation, which can be further traced back to assumptions made in priors and likelihoods used when formulating such methods from Bayes’ theorem. This presentation will discuss new data assimilation innovations that target outstanding challenges for developing accurate ensemble-based Earth-system prediction models, primarily targeting future versions of the NOAA Global Forecast System. The new methods are "non-parametric" in the sense that they do not assume specific shapes for error distributions that go into the specification of prior uncertainty and likelihoods needed for data assimilation. This research marks a major trend away from our current operational data assimilation methodology, which prescribes Gaussian error distributions for model variables and observations.