An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-Time Aircraft Reconnaissance Observations
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An Automated Method to Analyze Tropical Cyclone Surface Winds from Real-Time Aircraft Reconnaissance Observations

Public Access Version Available on: July 29, 2024, 12:00 AM
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  • Journal Title:
    Weather and Forecasting
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  • Description:
    This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within 5 h prior and 3 h after analysis time and makes use of prescribed methods to move observations to a common flight level (CFL; 700 hPa) for analysis and to reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the undersampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is <10 kt (<5 m s−1).
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    Weather and Forecasting, 39(2), 333-349
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