Analysis and Prediction of Cirrus-Top Altitude and Ice Water Path in a Mesoscale Area

Abstract

Vertical distributions of clouds have been a focus of many studies, motivated by their importance in radiative transfer processes in climate models. This study examines the horizontal distribution of cirrus clouds by means of satellite imagery analyses and numerical weather prediction model forecasts. A ground-truth dataset based on two aircraft mission periods flying particle probes through cirrus over a ground-based cloud radar is developed. Particle probe measurements in the cirrus clouds are used to compute ice water content and radar reflectivity averages in short time periods (25?30 s). Relationships for ice water content as a function of reflectivity are developed for 6-K ambient temperature categories. These relationships are applied to the radar-measured short-term-averaged reflectivities to compute vertical profiles of ice water content, which are vertically integrated over the depth of the observed cirrus clouds to form ice water path estimates. These and cloud-top height are compared with the same quantities as retrieved by the Geostationary Operational Environmental Satellite (GOES) level-2B algorithm applied to four channels of GOES-8 imagery measurements. The agreement in cloud-top height is reasonable (generally less than 2-km difference). The ice water path retrievals are smaller in magnitude than the radar estimates, and this difference grows with increasing cirrus thickness. Comparisons of a sequence of the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model (MM5) predictions and GOES level-2B retrievals of ice cloud tops for the convectively active second mission period showed that the MM5 cirrus areal extent was somewhat greater than the GOES depictions. Cloud-top height ranges were similar. MM5 is capable of producing ice water path magnitudes similar to the radar estimates, but the GOES retrievals are much more limited. Ninety-eight percent of the GOES grid points had ice water paths no greater than 60 g m?2, as compared with 74% for MM5. Ten percent of MM5 points had ice water content >200 g m?2, as compared with 0.07% for GOES retrievals. Based on this study, we conclude that GOES level-2B cloud-top retrievals are a reliable tool for prediction evaluations but the algorithm's retrievals of ice water path are not.