Reviewers
Dusanka and Milija Zupanski (Colorado State University)
Road Map
The review intends to shed light on possible strategies for the specification of model error covariance matrix (Q) modeling for the convective scale data assimilation (DA) of clouds and precipitations (Errico etal., 2007). A big unknown is the degree of flexibility required to represent the model error in a variational DA scheme. The review will synthesize the different attempts made in Q estimation and modeling in either idealized or quasi-operational contexts (Trémolet, 2007; Zupanski, 2006). A second part of the review may be devoted to the representation of model error in alternative DA schemes, such as ensemble approaches (Mitchell et al, 2002). Some stress can be put on the depiction of model uncertainty estimation through stochastic physics (Palmer et al, 2005).
Non-exhaustive list of publications and earlier reviews
Errico, R.M., G. Ohring, P. Bauer, B. Ferrier, J.F. Mahfouf, J. Turk, and F. Weng, 2007: Assimilation of Satellite Cloud and Precipitation Observations in Numerical Weather Prediction Models: Introduction to the JAS Special Collection. J. Atmos. Sci., 64, 3737-3741.
Mitchell, H.L., P.L. Houtekamer, and G. Pellerin, 2002: Ensemble Size, Balance, and Model-Error Representation in an Ensemble Kalman Filter. Mon. Wea. Rev., 130, 2791-2808.
T.N. Palmer, G.J. Shutts, R. Hagedorn, F.J. Doblas-Reyes, T. Jung, and M. Leutbecher, 2005: Representing model uncertainty in weather and climate prediction.Annual Review of Earth and Planetary Sciences, 33: 163-193
Yannick Trémolet (2007): Model-error estimation in 4D-Var. Quarterly Journal of the Royal Meteorological Society, 133, 1267-1280
Zupanski, D., and M. Zupanski, 2006: Model Error Estimation Employing an Ensemble Data Assimilation Approach. Mon. Wea. Rev., 134, 1337-1354.