Speaker: Professor Eugenia Kalnay, University of Maryland
Recent Advances in Data Assimilation
The improvement in numerical weather prediction in the last 3 decades is due
to improvements in atmospheric models, in observations, and in data
assimilation (the science of combining forecasts and observations to create
model initial conditions). In recent years, Ensemble Kalman Filter has become
the most advanced approach for data assimilation. I will introduce data
assimilation, Ensemble Kalman Filters, and new advances that extend their
utility.
In the second part of the talk I will show applications of these algorithms
to real and simulated observation examples showing the potential of these new
approaches. The results include 7 years of global ocean data assimilation
(Penny, 2011), LETKF-RIP applied to typhoon spin-up (Yang et al, 2011),
estimation of surface carbon, heat and moisture fluxes from atmospheric
data assimilation (Kang et al., 2011), and a comparison of 4D-Var and EnKF
for a simple 'coupled ocean-atmosphere model' (Singleton et al, 2012).