Another unique aspect of the CRTM is that it also provides the tangent-linear, adjoint, and Jacobian outputs needed for satellite data assimilation applications. The ability to compute a Jacobian for various geophysical input parameters significantly expands the capabilities beyond traditional forward RT models, such as those used in remote sensing retrieval algorithms and other "Bayesian" or "1D-VAR" applications.
The present talk will focus on recent advances in the ability of the CRTM to simulate satellite radiances in the presence of cloudy and precipitating scenes, with a particular emphasis on ice-phase microphysics. We'll explore the radiance sensitivity to cloud microphysical parameters through a series of experiments that will form the basis of the next generation of operational satellite data assimilation and numerical weather prediction. This represents a significant and necessary expansion of the CRTM capabilities to perform in an all-weather, all-surface, all-sensor environment.