Reviewer
Thibaut Montmerle (Meteo France)
Road Map
The review intends to shed light on possible strategies for the specification of background error covariance matrix 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 covariances between hydrometeors. This implies the review of non-linear mesoscale balance equations (Pagé etal., 2007), in conjunction with regression-based balances that could make use of cloud or precipitation-dependent geographical masks. Discussion about the use of flexible inhomogeneous representations of vertical and horizontal correlations may be also of great help. In particular, the use of empirical orthogonal functions, recursive filters, direct convolutions, implicit convolutions through the diffusion equation, spectral transforms and wavelet methods could be reviewed (Bannister, 2008) in the optic of cloud and precipitation DA. The focus will also be put on the choice of the control variable, especially the inclusion of hydrometeors into the control variables, and on strategies to compare those different choices.
Non-exhaustive list of publications and earlier reviews
Bannister R.N., 2008 A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics., Quarterly Journal of the Royal Meteorological Society, 134, 1971-1996 .
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.
Pagé, C., L. Fillion, and P. Zwack, 2007: Diagnosing Summertime Mesoscale Vertical Motion: Implications for Atmospheric Data Assimilation. Mon. Wea. Rev., 135, 2076-2094.