TY - JOUR
T1 - Evaluation of geostationary satellite observations and the development of a 1-2h prediction model for future storm intensity
AU - Mecikalski, John R.
AU - Rosenfeld, Daniel
AU - Manzato, Agostino
N1 - Publisher Copyright:
© 2016. American Geophysical Union. All Rights Reserved.
PY - 2016
Y1 - 2016
N2 - 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-re), 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-re fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T-re 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 T1451 (51 × 51 pixel T, where re exceeds 14 µm), TG9 (9× 9 pixel glaciation T surrounding a growing cloud), and ReBRTG51 (51 × 51 pixel re at the breakpoint T in the T-re profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 µmparticles, and lower TG values.
AB - 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-re), 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-re fields were analyzed on 9 × 9 and 51 × 51 pixel domains (needed when forming T-re 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 T1451 (51 × 51 pixel T, where re exceeds 14 µm), TG9 (9× 9 pixel glaciation T surrounding a growing cloud), and ReBRTG51 (51 × 51 pixel re at the breakpoint T in the T-re profile). Rapid cloud growth prior to severe storm formation leads to delayed particle growth, colder temperatures of the first 14 µmparticles, and lower TG values.
UR - http://www.scopus.com/inward/record.url?scp=84977590091&partnerID=8YFLogxK
U2 - 10.1002/2016JD024768
DO - 10.1002/2016JD024768
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AN - SCOPUS:84977590091
SN - 0148-0227
VL - 121
SP - 6374
EP - 6392
JO - Journal of Geophysical Research
JF - Journal of Geophysical Research
IS - 11
ER -