Reviewer
Andrew Lorenc (Met Office)
Road Map
The review intends to shed light on possible strategies for the convective scales data assimilation (DA) of clouds and precipitations. Operational schemes are often based on quasi-linear optimal estimation theory, and use the variational technique (e.g. 3DVar/4DVar) to cope with the minimization problem. The review will try to figure out possibilities and limitations of the seamless extension of such schemes to higher resolution (Lorenc and Payne, 2007) and cloud analysis (Errico et al, 2007). An other method of solving the quasi-linear estimation problem is to discretize it with ensembles, e.g. through the various forms of Ensemble Kalman Filters (EnKF). Several reviews have already considered the EnKF vs. 4D-Var approaches (Dance, 2004), so we suggest the review may focus on hybrid strategies (1DVAR+4DVAR, VAR+EnKF, Lognormal+Gaussian DA systems)in conjunction with cloud analysis. At those scales and for the hydrometeors, non-linearity becomes a significant issue. It may first appear as a displacement error, requiring some specific DA schemes, such as Beezley and Mandel (2008). We suggest the review should also give some clues about the notion of fronts and vertical/horizontal displacement errors in clouds and the way alternative DA scheme could be designed to solve such problems.
Non-exhaustive list of publications and earlier reviews
J. D. Beezley and J. Mandel, 2008: Morphing ensemble Kalman filters. Tellus , 60A, 131-140
S.L. Dance, 2004: Issues in high resolution limited area data assimilation for quantitative precipitation forecasting. Physica D: Nonlinear Phenomena, 196, 1-27
S. K. Park, Zupanski, 2003: Four-dimensional variational data assimilation for mesoscale and storm-scale applications. Meteorol. Atmos. Phys., 82, 173-208.
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.
Andrew C. Lorenc, Tim Payne, 2007: 4D-Var and the butterfly effect: Statistical four-dimensional data assimilation for a wide range of scales_. Quarterly Journal of the Royal Meteorological Society_ , 133, 607-614