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source of uncertainty in GCM studies of future climate. Part of the historical problem has been that, in the face of these complexity and scale mismatch problems, simple empirical cloud parameterizations have been devised and then just tuned to give reasonable top-of-atmosphere radiative forcing in a globally or zonally averaged sense. Sufficient attention has not generally been given to the validation of the predicted cloud properties. In the NWP community even less attention has historically been
source of uncertainty in GCM studies of future climate. Part of the historical problem has been that, in the face of these complexity and scale mismatch problems, simple empirical cloud parameterizations have been devised and then just tuned to give reasonable top-of-atmosphere radiative forcing in a globally or zonally averaged sense. Sufficient attention has not generally been given to the validation of the predicted cloud properties. In the NWP community even less attention has historically been
for data assimilation have varying degrees of reliability. Atmospheric dynamics at horizontal scales larger than 100 km or so are typically handled quite well both in terms of analysis and short-term forecast skill. Moreover, operational NWP models are able to predict the location in space and time of clouds associated with large-scale organized systems, but their skill degrades as the strength of synoptic forcing or the degree of larger-scale organization decreases. Large uncertainties remain in
for data assimilation have varying degrees of reliability. Atmospheric dynamics at horizontal scales larger than 100 km or so are typically handled quite well both in terms of analysis and short-term forecast skill. Moreover, operational NWP models are able to predict the location in space and time of clouds associated with large-scale organized systems, but their skill degrades as the strength of synoptic forcing or the degree of larger-scale organization decreases. Large uncertainties remain in
1. Introduction It has long been recognized that clouds play a dominant role in the earth’s climate and its changes. Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level boundary layer clouds have the most significant influence on cloud radiative forcing because of their areal extent and frequency ( Harrison et al. 1990 ; Hartmann et al. 1992 ). Radiation absorbed by boundary layer clouds also plays an important role in the
1. Introduction It has long been recognized that clouds play a dominant role in the earth’s climate and its changes. Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level boundary layer clouds have the most significant influence on cloud radiative forcing because of their areal extent and frequency ( Harrison et al. 1990 ; Hartmann et al. 1992 ). Radiation absorbed by boundary layer clouds also plays an important role in the
∂ y j /∂ x i is the Jacobian of the forward model. The adjoint is the transpose of the Jacobian dotted into the radiance perturbation “forcing” δy j : The adjoint is a linearized expression of how much sensitivity each element of the input state vector has to giving a particular radiance pattern. The tangent linear and adjoint models of SHDOMPPDA were developed using the Transformation of Algorithms in FORTRAN (TAF) software operated by FastOpt ( Giering and Kaminski 1998 ). Adjoint compilers
∂ y j /∂ x i is the Jacobian of the forward model. The adjoint is the transpose of the Jacobian dotted into the radiance perturbation “forcing” δy j : The adjoint is a linearized expression of how much sensitivity each element of the input state vector has to giving a particular radiance pattern. The tangent linear and adjoint models of SHDOMPPDA were developed using the Transformation of Algorithms in FORTRAN (TAF) software operated by FastOpt ( Giering and Kaminski 1998 ). Adjoint compilers
errors in precipitation assimilation can be illustrated with a simple experiment using a 1 + 1D (column and time) variational assimilation system. The model in this case consists of a column model of GEOS moist physics with dynamic forcing prescribed from a full GEOS GCM simulation. The initial moisture field is slightly drier in terms of the observed total column water vapor (TCWV). The 6-h forecast by the column model produces excessive rain compared with the TMI retrieval over the analysis window
errors in precipitation assimilation can be illustrated with a simple experiment using a 1 + 1D (column and time) variational assimilation system. The model in this case consists of a column model of GEOS moist physics with dynamic forcing prescribed from a full GEOS GCM simulation. The initial moisture field is slightly drier in terms of the observed total column water vapor (TCWV). The 6-h forecast by the column model produces excessive rain compared with the TMI retrieval over the analysis window
condition errors have that are responsible for determining the effects of those errors on forecasts. So, if real initial condition errors are dominated by particular spatial scales and are approximately geostrophic, then the simulated errors should be designed to have these same properties. Not doing so can yield gross misinterpretations of results ( Errico and Baumhefner 1987 ). Predictability experiments can also be performed using either an ensemble of models or stochastic forcing representative of
condition errors have that are responsible for determining the effects of those errors on forecasts. So, if real initial condition errors are dominated by particular spatial scales and are approximately geostrophic, then the simulated errors should be designed to have these same properties. Not doing so can yield gross misinterpretations of results ( Errico and Baumhefner 1987 ). Predictability experiments can also be performed using either an ensemble of models or stochastic forcing representative of
turbulence. Before discussing the ingredients that are needed to build efficient moist physical parameterizations, it should be recalled that an accurate modeling of the dynamical forcings is, of course, an absolute prerequisite to any realistic simulation of clouds and precipitation. b. Resolved moist processes 1) Existing parameterizations Many prognostic large-scale cloud schemes suitable either for NWPMs or GCMs have been proposed in the literature during the last 30 yr; these can be divided into
turbulence. Before discussing the ingredients that are needed to build efficient moist physical parameterizations, it should be recalled that an accurate modeling of the dynamical forcings is, of course, an absolute prerequisite to any realistic simulation of clouds and precipitation. b. Resolved moist processes 1) Existing parameterizations Many prognostic large-scale cloud schemes suitable either for NWPMs or GCMs have been proposed in the literature during the last 30 yr; these can be divided into
. Macke , A. , J. Mueller , and E. Raschke , 1996 : Single scattering properties of atmospheric ice crystals. J. Atmos. Sci. , 53 , 2813 – 2825 . McClatchey , R. A. , R. W. Fenn , J. E. A. Selby , P. E. Volz , and J. S. Garing , 1972 : Optical properties of the atmosphere. 3d ed. Environmental Research Papers 411, AFCRL-72-0497, Air Force Cambridge Research Papers, 103 pp . McFarquhar , G. M. , and A. J. Heymsfield , 1996 : Microphysical characteristics of three
. Macke , A. , J. Mueller , and E. Raschke , 1996 : Single scattering properties of atmospheric ice crystals. J. Atmos. Sci. , 53 , 2813 – 2825 . McClatchey , R. A. , R. W. Fenn , J. E. A. Selby , P. E. Volz , and J. S. Garing , 1972 : Optical properties of the atmosphere. 3d ed. Environmental Research Papers 411, AFCRL-72-0497, Air Force Cambridge Research Papers, 103 pp . McFarquhar , G. M. , and A. J. Heymsfield , 1996 : Microphysical characteristics of three
