Motivation
§Any data
assimilation scheme requires accurate statistics for the observation and
background errors. Unfortunately those statistics are not known
and are usually tuned or adjusted by gut feeling.
§Ensemble Kalman
filters need inflation (additive or multiplicative) of the background error covariance, but
1) Tuning the
inflation parameter is expensive especially if it is regionally dependent, and
it may depend on time
2) Miyoshi and Kalnay 2005
(MK) proposed a technique to objectively estimate the covariance
inflation parameter.
3) This
method works, but only if the observation errors are known.
§ Here we introduce a method to simultaneously estimate observation
errors and inflation.