Abstract:
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 are provided for a variety of geophysical applications.
Bio:
My research career has spanned two decades and has been focused by the common theme to improve predictions of the earth’s atmosphere. I have made research contributions in theoretical geophysical fluid dynamics, seasonal prediction , predictability , ensemble prediction and ensemble data assimilation. My accomplishments in software engineering, applied mathematics and statistics have been directly in support of my goal to improve prediction.