Abstract

AbstractA proper consideration of cloud–radiation interactions in linearized models is required for variational assimilation of cloud properties. Therefore, both a linearized cloud scheme and a linearized radiation scheme have been developed for the European Centre for Medium‐Range Weather Forecasts (ECMWF) data assimilation system. The tangent‐linear and adjoint versions of the ECMWF short‐wave radiation scheme are prepared without a priori modifications. The complexity of the radiation scheme for the long‐wave part of the spectrum makes accurate computations expensive. To reduce its computational cost, a combination of artificial neural networks and Jacobian matrices is defined for the linearized long‐wave radiation scheme. The linearized cloud scheme is diagnostic and has been adapted for this study. The accuracy of the linearization of both radiation and diagnostic cloud schemes is examined. The inclusion of a more sophisticated radiation scheme within the existing linearized parametrizations improves the accuracy of the tangent‐linear approximation. However, the impact of the linearized diagnostic cloud scheme is small, suggesting that the linearized model will require further developments of cloud parametrization.The adjoint technique is used to investigate the sensitivity of the radiation schemes to changes in temperature, humidity and cloud properties. This study shows which variables can be adjusted when certain observations of the surface and/or of the top‐of‐atmosphere radiation fluxes are used in data assimilation. It also indicates the vertical extent of the influence of such observations. Copyright © 2002 Royal Meteorological Society.

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