National Weather Center Colloquium

An efficient non-parametric data assimilation method for atmospheric research and ensemble forecasting

Dr. Jonathan Poterjoy

Institute for Mathematics Applied to Geosciences Mesoscale and Microscale Meteorology Laboratory

08 March 2016, 4:00 PM

National Weather Center, Room 1313
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK

Ensemble data assimilation strategies are now common practice in geoscience; examples include the ensemble Kalman filter (EnKF) and ensemble/variational hybrids. These methods provide Monte Carlo estimates of a system’s probability density conditioned on observations, assuming errors for the model state and observations are independent and Gaussian. A major benefit of Gaussian-based approaches is that they can be constructed to work effectively using relatively small ensembles by treating sampling errors in ensemble-estimated covariances. Nevertheless, these methods may not be the best option as computational resources allow for increasingly larger ensembles that better resolve non-Gaussian errors.

In the presentation, a brief overview of data assimilation strategies used routinely in atmospheric science will be provided. Using examples from an idealized convective-scale weather application, several limitations of these techniques will be demonstrated, and a new strategy for avoiding them will be introduced. The talk will summarize recent work towards the development and testing of a non-parametric filter for data assimilation in weather models, and discuss future implications for weather research and forecasting

Speaker bio

National Weather Center Colloquium Seminar Series website