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Timothy R. Whitcomb, Daniel J. Arevalo, Jonathan R. Moskaitis, William A. Komaromi, James D. Doyle, Andrew Huang, Jay Wodka, Frederick Noon, Margaret M. May, and Fred Williamson


Despite improvements in predicting the track and intensity of tropical cyclones (TCs), these storms with major societal and economic impacts continue to pose challenges for statically-provisioned computational resources. The number of active storms varies from day to day, leading to regular bursts of irregular computational loads atop an already busy production schedule for weather prediction centers. The emergence of high-resolution ensemble TC prediction to quantify the uncertainty in track and intensity exacerbates this problem by requiring multiple forecasts run for each storm, each representing a possible outcome. With more than a decade of progress in the literature describing research and real-time numerical weather prediction in the cloud, we set out to evaluate if the commercial cloud environment could cope with the unique demands of TC ensemble forecasts. We describe a demonstration using a high-performance computing environment within the Microsoft Azure cloud to test dynamic resource provisioning to address time-varying resource challenges. We deployed existing operational models, implemented a combination of vendor-provided and open-source tools to orchestrate the cycling production workflows, and developed techniques for automatic error handling to keep production on schedule with minimal operator intervention. Despite challenges, our production pipeline from data ingest, forecast integration, graphics generation, and dissemination via social media was able to produce real-time forecasts of storm track and intensity with product latencies commensurate with existing operational forecasting systems.

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James D. Doyle, Jonathan R. Moskaitis, Joel W. Feldmeier, Ronald J. Ferek, Mark Beaubien, Michael M. Bell, Daniel L. Cecil, Robert L. Creasey, Patrick Duran, Russell L. Elsberry, William A. Komaromi, John Molinari, David R. Ryglicki, Daniel P. Stern, Christopher S. Velden, Xuguang Wang, Todd Allen, Bradford S. Barrett, Peter G. Black, Jason P. Dunion, Kerry A. Emanuel, Patrick A. Harr, Lee Harrison, Eric A. Hendricks, Derrick Herndon, William Q. Jeffries, Sharanya J. Majumdar, James A. Moore, Zhaoxia Pu, Robert F. Rogers, Elizabeth R. Sanabia, Gregory J. Tripoli, and Da-Lin Zhang


Tropical cyclone (TC) outflow and its relationship to TC intensity change and structure were investigated in the Office of Naval Research Tropical Cyclone Intensity (TCI) field program during 2015 using dropsondes deployed from the innovative new High-Definition Sounding System (HDSS) and remotely sensed observations from the Hurricane Imaging Radiometer (HIRAD), both on board the NASA WB-57 that flew in the lower stratosphere. Three noteworthy hurricanes were intensively observed with unprecedented horizontal resolution: Joaquin in the Atlantic and Marty and Patricia in the eastern North Pacific. Nearly 800 dropsondes were deployed from the WB-57 flight level of ∼60,000 ft (∼18 km), recording atmospheric conditions from the lower stratosphere to the surface, while HIRAD measured the surface winds in a 50-km-wide swath with a horizontal resolution of 2 km. Dropsonde transects with 4–10-km spacing through the inner cores of Hurricanes Patricia, Joaquin, and Marty depict the large horizontal and vertical gradients in winds and thermodynamic properties. An innovative technique utilizing GPS positions of the HDSS reveals the vortex tilt in detail not possible before. In four TCI flights over Joaquin, systematic measurements of a major hurricane’s outflow layer were made at high spatial resolution for the first time. Dropsondes deployed at 4-km intervals as the WB-57 flew over the center of Hurricane Patricia reveal in unprecedented detail the inner-core structure and upper-tropospheric outflow associated with this historic hurricane. Analyses and numerical modeling studies are in progress to understand and predict the complex factors that influenced Joaquin’s and Patricia’s unusual intensity changes.

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