Efficient moist physics schemes for data assimilation. I: Large-scale clouds and condensation

A cost-effective, easily maintained linear Cloud scheme is presented, which introduces the major effects of condensation and latent heat release and precipitation into the linear model of a four-dimensional variational assimilation (4D-Var) system. The condensation scheme is derived within the general framework of statistical cloud schemes and the approximations used make it applicable to a large variety of nonlinear schemes. Forecast trials indicated that the introduction of this scheme into the Met Office numerical weather prediction (NWP) model led to clear improvements of the forecast skill. Linearization test results are presented which show that the new scheme improves the match between linear and nonlinear models. An example of particularly large local improvements is found near the core of a low-pressure system over the north Atlantic. Improvements are generally larger in the extratropics than in the tropics, where convection plays a dominant role in the nonlinear model (while it is not represented in the linear model). Experimental linearization tests (where the convection scheme of the nonlinear model has been switched off) show a particularly strong beneficial impact of the new scheme and enable the identification of some convection-related compensating errors, which partially mask some of the new scheme's benefits when compared with a simpler linear scheme. The precipitation increments front the linear scheme are found to be broader and show less detail but capture many features of the corresponding increments obtained from the nonlinear model. A method of tuning and post-processing the precipitation strength is presented and discussed. (C) Crown Copyright 2009. Reproduced with the permission of HMSO. Published by John Wiley & Sons, Ltd.