An Intercomparison of UW Cloud-Top Cooling Rates with WSR-88D Radar Data

Abstract

The University of Wisconsin Convective Initiation (UWCI) algorithm utilizes geostationary IR satellite data to compute cloud-top cooling (UW-CTC) rates and assign CI nowcasts to vertically growing clouds. This study is motivated by National Weather Service (NWS) forecaster reviews of the algorithm output, which hypothesized that more intense cloud-top cooling corresponds to more vigorous short-term (0–60 min) convective development. An objective validation of UW-CTC rates using a satellite-based object-tracking methodology is presented, along with a prognostic evaluation of such cloud-top cooling rates for use in forecasting the growth and development of deep convection. In general, both a cloud object’s instantaneous and maximum cooling rate(s) are shown to be useful prognostic tools in predicting future radar intensification. UW-CTC rates are shown to be most skillful in detecting convective clouds that achieved intense radar signatures. The UW-CTC rate lead time ahead of the various radar fields is also shown, along with an illustration of the benefit of UW-CTC rates in operational forecasting. The results of this study suggest that convective clouds with the strongest UW-CTC rates are more likely to achieve significant near-term (0–60 min) radar signatures in such fields as composite reflectivity, vertically integrated liquid (VIL), and maximum estimated size of hail (MESH) compared to clouds that exhibit only weak UW-CTC rates.

A Satellite-Based Convective Cloud Object Tracking and Multipurpose Data Fusion Tool with Application to Developing Convection

“…In fact, any georeferenced dataset, including nonmeteorological data, can be fused with the cloud objects through the statistical postprocessing system with the ability to monitor temporal trends in all data fields. Two research projects at UW-CIMSS are utilizing the convective cloud object tracking system and publications from those projects will further demonstrate the utility of the system (e.g., Hartung et al 2013).…”

“…In addition, a lead-time analysis of cloud-top cooling rates as a function of the same NEXRAD fields was generated to highlight the prognostic value of satellitebased cooling rates in operational forecasting. Such an in-depth analysis is not presented in this text, but the reader is referred to Hartung et al (2013) for complete details. A second research project leverages the two systems to compute the time rate of change of an array of satellite cloud-retrieved fields (e.g., « tot , cloud phase, visible optical depth), combined with NWP data and NEXRAD data to probabilistically nowcast the likelihood that a developing convective cloud will produce surface severe weather reports in the subsequent 0-2-h time frame.…”

Environmental Analysis of GOES-R Proving Ground Convection-Initiation Forecasting Algorithms

“…Spatial correlation problems also exist when comparing satellite brightness-temperature values with reflectivity. Objects identified objectively as convection by satellite would need to be appropriately backtracked to the correct preconvective cloud object, which would introduce several unwanted tracking errors at a low temporal resolution (Sieglaff et al 2013). Parallax is resolved using a method similar to that in Sieglaff et al (2011) in which indications are assumed to be at a height of 7 km.…”

“…Previous studies have done basic validations to test the skill of CI products (Mecikalski et al 2008;Hartung et al 2013), but only a few have speculated on the impact of the preconvective environment on CTC-based predictions of CI (Mecikalski et al 2008;Sieglaff et al 2011;Walker et al 2012). Validations range from radar-based CI detection (Mecikalski and Bedka 2006;Mecikalski et al 2008;Walker et al 2012) to occurrence of lightning near CTC indications (Sieglaff et al 2011).…”

“…To answer these questions, an objective validation method similar to that of Sieglaff et al (2013) is developed to examine large samples of both SATCAST and UWCTC products. This study is designed to answer three questions: Do preconvective environments have an impact on satellitebased CI forecasting algorithms?…”