ECOLE POLYTECHNIQUE FÉDÉRALE DE
LAUSANNE Institut de Mathématiques - Chaires de Statistique Prof. A. Davison - Prof. S. Morgenthaler
Abstract Several aspects of numerical weather prediction make forecasting and
data assimilation particularly challenging: very high-dimensional systems, strongly non-linear (possibly
chaotic) dynamics, and real-time requirements for assimilating data and physical models. One
technique to update the observational data to forecasts is to use a Kalman filter (KF). As the observation and state vectors are of very
high dimension --- usually of order of 105 to 106 --- direct
implementation of KF recursions cannot be implemented.
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