Group seminar on 11. May, 16:00 CET
Building State-of-the-Art Forecast Systems with the Ensemble Kalman Filter
The development of numerical weather prediction was one of the great scientific and computational achievements of the last century. Computer models that approximate solutions of the partial differential equations that govern fluid flow and a comprehensive global observing network are two components of this prediction enterprise. An essential third component is data assimilation, the computational method that combines observations with predictions from previous times to produce initial conditions for subsequent predictions. The best present-day numerical weather prediction systems have evolved over decades and feature model-specific assimilation systems built with nearly a person century of effort.
This talk describes the development of a community software facility for ensemble Kalman filter data assimilation, the Data Assimilation Research Testbed (DART). DART can produce high-quality weather predictions but can also be used to build a comprehensive forecast system for any prediction model and observations. The basic ensemble Kalman filter is described and applied to simple example problems. Heuristic extensions to the basic algorithm that are essential for large applications are presented in a historical context.
An ensemble forecast system can do much more than just make probabilistic predictions. By confronting a prediction model with observations, it can estimate model parameters and guide general model improvement. It can also evaluate the quality of existing observations and inform the design of future observing systems. Examples of these capabilities, including several with a UW heritage, are provided for a variety of geophysical applications.