The JPL Tropical Cyclone Information System integrates observations with model forecasts, allowing interrogation of a large number of variables, to help better understand the multi-scale non-linear interactions that lead to storm development, and to evaluate models. Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multi-scale processes and non-linear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations, due to the complexity associated with finding and bringing together semi-coincident and semi-contemporaneous multi-parameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatio-temporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS) - a Data Analytic Framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and on-line analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.
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