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The use of geostationary satellites for monitoring the development of deep convective clouds has been recently well documented. One such approach, the University of Wisconsin Cloud-Top Cooling Rate (CTC) algorithm, utilizes frequent Geostationary Operational Environmental Satellite (GOES) observations to diagnose the vigor of developing convective clouds through monitoring cooling rates of infrared window brightness temperature imagery. The CTC algorithm was modified to include GOES visible optical depth retrievals for the purpose of identifying growing convective clouds in regions of thin cirrus clouds. An automated objective skill analysis of the two CTC versions (with and without the GOES visible optical depth) versus a variety of Next Generation Weather Radar (NEXRAD) fields was performed using a cloud-object tracking system developed at the University of Wisconsin Cooperative Institute for Meteorological Satellite Studies. The skill analysis was performed in a manner consistent with a recent study employing the same cloud-object tracking system. The analysis indicates that the inclusion of GOES visible optical depth retrievals in the CTC algorithm increases probability of detection and critical success index scores for all NEXRAD fields studied and slightly decreases false alarm ratios for most NEXRAD thresholds. In addition to better identifying vertically growing storms in regions of thin cirrus clouds, the analysis further demonstrates that the strongest cooling rates associated with developing convection are more reliably detected with the inclusion of visible optical depth and that storms that achieve intense reflectivity and large radar-estimated hail exhibit strong cloud-top cooling rates in much higher proportions than they do without the inclusion of visible optical depth.

Improving Satellite-Based Convective Cloud Growth Monitoring with Visible Optical Depth Retrievals