Evaluation of geostationary satellite observations and the development of a 1–2h prediction model for future storm intensity

摘要

A study was conducted to gain insights into the use of geostationary satellite-based indicators for characterizing and identifying growing cumulus clouds that evolve into severe weather producing convective storms. Eleven convective initiation (CI), 41 cloud top temperature–effective radius (T-r_e), and 9 additional fields were formed for 340 growing cumulus clouds that were manually tracked for 2 h and checked for association with severe weather to 2–3 h into the future. The geostationary satellite data were at 5 min resolution from Meteosat-8 on six convectively active days in 2010, 2012, and 2013. The study’s goals were to determine which satellite fields are useful to forecasting severe storms and to form a simple model for predicting future storm intensity. The CI fields were applied on 3 × 3 pixel regions, and the T-r_e fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T-r_e vertical profiles). Of the 340 growing cumulus clouds examined, 34 were later associated with severe weather (using European Severe Weather Database reports), with the remaining being nonsevere storms. Using a multivariate analysis, transforming predictors into their empirical posterior probability, and maximizing the Peirce skill score, the best predictors were T_(1451) (51 × 51 pixel T, where r_e exceeds 14 μm), T_(G9) (9× 9 pixel glaciation T surrounding a growing cloud), and Re_(BRTG51) (51 × 51 pixel r_e at the breakpoint T in the T-r_e profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 μmparticles, and lower T_G values.