WindRiskTech, L.L.C., was founded in 2005 to provide estimates of tropical cyclone wind risk for any point on the globe, using the most advanced techniques available. WindRiskTech operates on the philosophy that physical science can be brought to bear on natural hazard risk assessment to bring far more information to the problem than is available merely from the statistics of historical hazard events, given that such histories are usually short, incomplete, and often biased. We use deterministic models of tropical cyclone track and intensity that have been extensively evaluated in the context of real-time forecasting of actual storms. In contrast to most existing risk models, our methods are completely open and published in peer-reviewed literature. We have no trade secrets.
Since the company's founding, we have expanded our techniques to include assessment of risk from tropical cyclone-related storm surges and rainfall, again using deterministic models.
A professor at the Massachusetts Institute of Technology, Chief Scientific Officer Kerry Emanuel has researched tropical cyclone physics for 30 years, developing and applying advanced theory, numerical models, observational analyses, field experiments and laboratory work to advance our understanding and prediction of tropical cyclones. His coupled ocean-atmosphere numerical models of tropical cyclone intensity are used by the U.S. Navy's Joint Typhoon Warning Center as aids to forecasting tropical cyclone intensity. He has taught courses in climate, atmospheric convection, large-scale atmospheric dynamics, and tropical meteorology for over 35 years and is the author of numerous peer-reviewed scientific articles and Divine Wind, a popular account of hurricanes.
Sai Ravela Chief technical Officer, is an expert in stochastic systems science and engineering. In over 20 years of research in Computational Intelligence for Earth Science he has developed nonlinear data assimilation methods, a statistical inference theory for coherent fluids, statistical models of hurricane activity, statistical-physical approaches to treat deterministic estimation errors, uncertainty quantification methods in fluid modeling, autonomous sampling of natural hazard environments by umanned aircraft systems, fluid imaging systems and, most recently, reinforcement learning for mitigation and planning under climate change. He is the author of several publications and patents, and teaches both uncertainty quantification and statistical methods for atmospheric science. Dr. Ravela is also a Principal Research Scientist at MIT.