Multiscale Processes of Hurricane Sandy (2012) as Revealed by the CAMVis-MAP

In late October 2012, Storm Sandy made landfall near Brigantine, New Jersey, devastating surrounding areas and causing tremendous economic loss and hundreds of fatalities (Blake et al., 2013). An estimated damage of $50 billion made Sandy become the second costliest tropical cyclone (TC) in US history, surpassed only by Hurricane Katrina (2005). Central questions to be addressed include (1) to what extent the lead time of severe storm prediction such as Sandy can be extended (e.g., Emanuel 2012); and (2) whether and how advanced global model, supercomputing technology and numerical algorithm can help effectively illustrate the complicated physical processes that are associated with the evolution of the storms. In this study, the predictability of Sandy is addressed with a focus on short-term (or extended-range) genesis prediction as the first step toward the goal of understanding the relationship between extreme events, such as Sandy, and the current climate. The newly deployed Coupled Advanced global mesoscale Modeling (GMM) and concurrent Visualization (CAMVis) system is used for this study. We will show remarkable simulations of Hurricane Sandy with the GMM, including realistic 7-day track and intensity forecast and genesis predictions with a lead time of up to 6 days (e.g., Shen et al., 2013, GRL, submitted). We then discuss the enabling role of the high-resolution 4-D (time-X-Y-Z) visualizations in illustrating TC's transient dynamics and its interaction with tropical waves. In addition, we have finished the parallel implementation of the ensemble empirical mode decomposition (PEEMD, Cheung et al., 2013, AGU13, submitted) method that will be soon integrated into the multiscale analysis package (MAP) for the analysis of tropical weather systems such as TCs and tropical waves. While the original EEMD has previously shown superior performance in decomposition of nonlinear (local) and non-stationary data into different intrinsic modes which stay within the natural filter period windows, the PEEMD achieves a speedup of over 100 times as compared to the original EEMD. The advanced GMM, 4D visualizations and PEEMD method are being used to examine the multiscale processes of Sandy and its environmental flows that may contribute to the extended lead-time predictability of Hurricane Sandy. Figure 1: Evolution of Hurricane Sandy (2012) as revealed by the advanced visualization.