A Statistical Evaluation of GOES Cloud-Top Properties for Nowcasting Convective Initiation

Abstract

Abstract The goal of this project is to validate and extend a study by Mecikalski and Bedka that capitalized on information the Geostationary Operational Environmental Satellite (GOES) instruments provide for nowcasting (i.e., 0–1-h forecasting) convective initiation through the real-time monitoring of cloud-top properties for moving cumuli. Convective initiation (CI) is defined as the first occurrence of a ≥35-dBZ radar echo from a cumuliform cloud. Mecikalski and Bedka’s study concluded that eight infrared GOES-based “interest fields” of growing cumulus clouds should be monitored over 15–30-min intervals toward predicting CI: the transition of cloud-top brightness temperature to below 0°C, cloud-top cooling rates, and instantaneous and time trends of channel differences 6.5–10.7 and 13.3–10.7 μm. The study results are as follows: 1) measures of accuracy and uncertainty of Mecikalski and Bedka’s algorithm via commonly used skill scoring procedures, and 2) a report on the relative importance of each interest field to nowcasting CI using GOES. It is found that for nonpropagating convective events, the skill scores are dependent on which CI interest fields are considered per pixel and are optimized when three–four fields are met for a given 1-km GOES pixel in terms of probability of detection, and threat and Heidke skill scores. The lowest false-alarm rates are found when one field is used: that associated with cloud-top glaciation 30 min prior to CI. Subsequent recommendations for future research toward improving Mecikalski and Bedka’s study are suggested especially with regard to constraining CI nowcasts when inhibiting factors are present (e.g., capping inversions).