University of Maine
Data assimilation, chaos, and weather forecasting
Monday, April 23, starting at 4:00 pm, Davis 301
Refreshments at 3:30 pm, outside of Davis 216
The atmosphere behaves as a nonlinear, chaotic dynamical system, where small differences in initial conditions can lead to great differences in future outcomes. Thus, accurately determining the current atmospheric state is a key step in short-term numerical weather forecasting. Data assimilation is a technique that combines model forecasts with observational data to produce a best guess of the current state. This talk will introduce Kalman filter data assimilation techniques under the context of weather forecasting and describe results on a low-dimensional dynamical system.