Abstract
Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.
The National Center for Atmospheric Research is sponsored by the National Science Foundation.
