| Composite Impact of Global Hawk Unmanned Aircraft Dropwindsondes on Tropical Cyclone Analyses and Forecasts - :18750 | Office of Oceanic and Atmospheric Research (OAR)
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Composite Impact of Global Hawk Unmanned Aircraft Dropwindsondes on Tropical Cyclone Analyses and Forecasts
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Composite Impact of Global Hawk Unmanned Aircraft Dropwindsondes on Tropical Cyclone Analyses and Forecasts
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  • Description:
    The impacts of Global Hawk (GH) dropwindsondes on tropical cyclone (TC) analyses and forecasts are examined over a composite sample of missions flown during the NASA Hurricane and Severe Storm Sentinel (HS3) and the NOAA Sensing Hazards with Operational Unmanned Technology (SHOUT) field campaigns. An ensemble Kalman filter is employed to assimilate the dropwindsonde observations at the vortex scale. With the assimilation of GH dropwindsondes, TCs generally exhibit fewer position and intensity errors, a better wind-pressure relationship, and improved representation of integrated kinetic energy in the analyses. The resulting track and intensity forecasts with all the cases generally show a positive impact when GH dropwindsondes are assimilated. The impact of GH dropwindsondes is further explored with cases stratified by intensity change and presence of crewed aircraft data. GH dropwindsondes demonstrate a larger impact for nonsteady-state TCs [non-SS; 24-h intensity change larger than 20 kt (similar to 10 m s(-1))] than for steady-state (SS) TCs. The relative skill from assimilating GH dropwindsondes ranges between 25% and 35% for either the position or intensity improvement in the final analyses overall, but only similar to 5%-10% for SS cases alone. The resulting forecasts for non-SS cases show higher skill for both track and intensity than SS cases. In addition, the GH dropwindsonde impact on TC forecasts varies in the presence of crewed aircraft data. An increased intensity improvement at long lead times is seen when crewed aircraft data are absent. This demonstrates the importance of strategically designing flight patterns to exploit the sampling strengths of the GH and crewed aircraft in order to maximize data impacts on TC prediction.

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