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The cloud object approach identifies a cloud object as a contiguous patch of cloudy regions with similar cloud physical properties. Tropical deep convective (DC) cloud objects are composed of optically thick and high cloud-top footprints. The optically thin and/or low cloud-top footprints adjacent to a DC cloud object but bounded by the most easterly, westerly, southerly and northerly footprints of the cloud object are included to form a “gridded” cloud object. The latter data allow a more straightforward evaluation of cloud parameterizations used in large-scale models and a better understanding of physical processes associated with tropical convective systems than the cloud object data alone. It is well documented that progress in improving cloud parameterizations in numerical weather prediction (NWP) and climate models has been very slow. In this investigation, we propose to evaluate the recently-improved cloud parameterizations used in ECMWF, GMAO and NCAR models, with an emphasis on the performance of cloud parameterizations with respect to changes in surface and atmospheric conditions, using observations of about 80,000 cloud objects that are matched with meteorological data from these models. For the DC cloud type, both the cloud object and gridded cloud object data from Aqua CERES are used while only cloud object data are used for boundary-layer cloud types. This investigation will be built upon an evaluation of earlier ECMWF cloud parameterizations used in the operational (1998) and ERA-40 (2002) cycles using the TRMM CERES cloud object data. In this investigation, the recently-released ECMWF ERA Interim data and a short period of ECMWF operational analyses will be examined to identify deficiencies in the ECMWF cloud parameterization, and suggest further improvements to the cloud parameterization. A similar approach will be used to examine and evaluate the GMAO MERRA (Modern ERA Retrospective-Analysis for Research and Applications) data. The NCAR CAM climate model, which is run in NWP mode, may be evaluated using a slightly different strategy in addition to that used for other two models, due to its potential inability to temporally match the occurrence of cloud objects. Regional differences in cloud physical and radiative properties will be contrasted between the CAM forecasts and the cloud object observations. The outcomes of this investigation will be a better understanding of the strengths and deficiencies in each of three diverse cloud parameterizations used in NWP and climate models for deep convection and boundary-layer cloud types and directions for critical improvements in each of the three parameterizations. This investigation will thus fit to Research Theme 1 of this MAP NRA: Integrated Studies of Weather and Climate.
Project PI: Kuan-Man Xu/NASA Langley Research Center
Kuan-Man Xu NASA Langley Research Center Hampton, VA 23681-0001
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