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

Abstract

nhanced temporal and spatial resolution of the Geostationary Operational Environmental Satellite?R Series (GOES-R) 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: the University of Wisconsin cloud-top-cooling algorithm (UWCTC) and the University of Alabama in Huntsville?s satellite convection analysis and tracking algorithm (SATCAST). Preliminary analyses of algorithm products have led to speculation over preconvective environmental influences on algorithm performance. An objective validation approach is developed to separate algorithm products into positive and false indications. Seventeen preconvective environmental variables are examined for the positive and false indications to improve algorithm output. The total dataset consists of two time periods in the late convective season of 2012 and the early convective season of 2013. Data are examined for environmental relationships using principal component analysis (PCA) and quadratic discriminant analysis (QDA). Data fusion by QDA is tested for SATCAST and UWCTC on five separate case-study days to determine whether application of environmental variables improves satellite-based CI forecasting. PCA and significance testing revealed that positive indications favored environments with greater vertically integrated instability (CAPE), less stability (CIN), and more low-level convergence. QDA improved both algorithms on all five case studies using significantly different variables. This study provides an examination of environmental influences on the performance of GOES-R Proving Ground CI forecasting algorithms and shows that integration of QDA in the cloud-top-cooling-based algorithms using environmental variables will ultimately generate a more skillful product.