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3236673 
Journal Article 
Bias and data assimilation 
Dee, DP 
2005 
1 
Quarterly Journal of the Royal Meteorological Society
ISSN: 0035-9009
EISSN: 1477-870X 
131 
613 
3323-3343 
WOS:000239896000010 
All data assimilation systems are affected by biases, caused by problems with the data, by approximations in the observation operators used to simulate the data, by limitations of the assimilating model, or by the assimilation methodology itself. A clear symptom of bias in the assimilation is the presence of systematic features in the analysis increments, such as large persistent mean values or regularly recurring spatial structures. Bias can also be detected by monitoring statistics of observed-minus-background residuals for different instruments. Bias-aware assimilation methods are designed to estimate and correct systematic errors jointly with the model state variables. Such methods require attribution of a bias to a particular source, and its characterization in terms of some well-defined set of parameters. They can be formulated either in a variational or sequential estimation framework by augmenting the system state with the bias parameters. 
adaptive bias correction; bias-aware; bias-blind; Kalman filter; model bias; observation bias; parameter estimation; systematic errors; weak-constraint 4D-Var