Date of this Version
Apke, J. M., 2013: Environmental analysis of GOES-R proving ground convection initiation forecasting algorithms. Dept. of Earth and Atmo. Sciences, The University of Nebraska-Lincoln, 134 pp.
The enhanced temporal and spatial resolution of the GOES-R series will allow for the use of cloud top cooling based convection initiation (CI) forecasting algorithms. Two such algorithms have been created on the current generation of GOES satellites: theUniversityofWisconsincloud top cooling algorithm (UWCTC) and the University of Alabama-Huntsville’s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analysis of algorithm products has led to speculation over pre-convective environmental effects on algorithm performance, which this study aims to examine. CI indications are used with objective segmentation tools to identify and cluster radar objects over theGreat Plainsbased on reflectivity quantitative and spatial thresholds. The identified clusters are tracked objectively to identify points of CI. Any SATCAST or UWCTC indication that corresponds with (without) an evaluated initiation point within an hour is considered a positive (false) indication. The objective approach is compared to a small-scale hand validation for optimal results. 17 pre‑convective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods, one in the late convective season of 2012 and one in the early convective season of 2013. Data are examined for environmental relationships using principal components analysis (PCA) and quadratic discriminant analysis (QDA). Significant differences are determined for pre-convective environmental variables between positive and false indications. Data fusion by QDA is tested for SATCAST and UWCTC on five separate case study days to determine if application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater instability (CAPE), less stability (CIN) and more low-level convergence. The QDA improved both algorithms on all five case studies using significantly different variables. This study provides a preliminary examination of environmental effects on the performance of GOES-R proving ground CI forecasting algorithms, and shows that probability-based discrimination on the algorithms using environmental variables will ultimately help the situational awareness of a nowcaster.
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