Development and Optimization of Mesoscale Atmospheric Motion Vectors Using Novel GOES-R Processing Algorithms on 1-5 Minute SRSO Proxy Data, and Demonstration of Readiness for GOES-R Applications via Impact Studies in Mesoscale/Hurricane Data Assimilation and NWP Systems

One of the principle benefits expected from GOES-R is the improvement in temporal sampling of images from the ABI. The rapid refresh (1-5 min.) should allow for quantitative improvements in derived products such as AMVs, which have long stood as an important contributor of tropospheric wind information to analyses on the global scale (Velden et al. 2005). GOES-R will allow superior cloud-tracking and AMV generation on time scales not only useful for global applications, but for mesoscale applications (e.g., severe storms, hurricanes) as well. The reasons we are optimistic that GOES-R AMVs can be an important contributor to mesoscale analyses are a result from recent and ongoing studies using GOES-R proxy datasets (Bedka and Mecikalski, 2005; Velden et al. 2013; Rabin et al. 2013; Wu and Velden, 2013, 2015). In this study, we build on these pioneering efforts and take advantage of expected GOES-R capabilities and new AMV derivation methods (Bresky et al. 2012). Then apply these to the production of mesoscale AMV datasets with the goal of extracting wind information that benefits short-term forecasts and operational NWP. First results will be presented here from model impact studies of cases involving hurricanes and severe weather events.