Applying CloudSat/A-Train and ECMWF analysis data sets to constrain and evaluate cloud, convection and radiation parameterizations in climate models

How to represent clouds, convections and their radiative effects in numerical weather and global climate models remains a challenge. To help resolve these issues, a CloudSat-centric, multi-parameter A-Train and high-resolution ECMWF analyses data set is being developed to characterize dynamic, radiative and micro-physical processes associated with clouds and convection. The data set include parameters from CloudSat, Calipso, AIRS, AMSR, MODIS, CERES etc and the ECMWF analyses.

We apply the data to constrain model key physical parameters/processes associated with clouds and convection such as cloud hydrometers, cloud top, cloud fraction, precipitation, instability, structure, and cloud radiative properties etc. The goal is to improve in climate prediction by providing collocated observation and analysis data against which model representations of clouds and convections, including PBL stratocumulus, trade-wind shallow, middle-level and deep cumulus and their radiative properties, can be more effectively developed, constrained and evaluated.

In this presentation, results from the comparisons between cloud, convection, and precipitation statistics derived from using a subset of the above data set and the ECMWF IFS, GEOS5/GMAO, and fvMMF models will be presented. In addition, the errors in the estimated outgoing TOA radiative fluxes associated with ignoring convective/precipitating ice (something commonly done in GCMs) relative to the standard CloudSat radiation product will also be discussed.