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

John A. Knaff aCenter for Satellite Applications and Research, NOAA, Fort Collins, Colorado

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Christopher J. Slocum aCenter for Satellite Applications and Research, NOAA, Fort Collins, Colorado

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Abstract

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).

Significance Statement

Many applications need estimates of 2D surface winds in tropical cyclones in real time. While real-time aircraft-based observations of the winds inside tropical cyclones have been available for several decades, there have been few automated and objective methods to analyze this information to provide estimates of the strength and distribution of the surface winds. Here, we provide details of one method that fuses these unique observations to provide useful 2D analyses of the winds in and around tropical cyclones.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John Knaff, John.Knaff@noaa.gov

Abstract

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).

Significance Statement

Many applications need estimates of 2D surface winds in tropical cyclones in real time. While real-time aircraft-based observations of the winds inside tropical cyclones have been available for several decades, there have been few automated and objective methods to analyze this information to provide estimates of the strength and distribution of the surface winds. Here, we provide details of one method that fuses these unique observations to provide useful 2D analyses of the winds in and around tropical cyclones.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John Knaff, John.Knaff@noaa.gov

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  • Barnes, S., 1964: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteor., 3, 396409, https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Beven, J. L., II, R. Berg, and A. Hagen, 2019: Tropical cyclone report: Hurricane Michael (7–11 October 2018). NHC Tech. Rep. AL142018, 86 pp., https://www.nhc.noaa.gov/data/tcr/AL142018_Michael.pdf.

  • Boose, E. R., K. E. Chamberlin, and D. R. Foster, 2001: Landscape and regional impacts of hurricanes in New England. Ecol. Monogr., 71, 2748, https://doi.org/10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Brennan, M. J., 2019: NHC’s use of aircraft data in hurricane analysis. SECART 2019 Resilience Webinar Series, 22 pp., https://www.noaa.gov/sites/default/files/legacy/document/2020/Dec/SECARTwebinar-Brennan-sm.pdf.

  • Cangialosi, J. P., and R. Berg, 2021: Tropical cyclone report: Hurricane Delta (4–10 October 2020). NHC Tech. Rep. AL262020, 46 pp., https://www.nhc.noaa.gov/data/tcr/AL262020_Delta.pdf.

  • Cha, T.‐Y., M. M. Bell, W.-C. Lee, and A. J. DesRosiers, 2020: Polygonal eyewall asymmetries during the rapid intensification of Hurricane Michael (2018). Geophys. Res. Lett., 47, e2020GL087919, https://doi.org/10.1029/2020GL087919.

    • Search Google Scholar
    • Export Citation
  • Cline, I. M., 1920: Relation of changes in storm tides on the coast of the Gulf of Mexico to the center and movement of hurricanes. Mon. Wea. Rev., 48, 127146, https://doi.org/10.1175/1520-0493(1920)48<127:ROCIST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Combot, C., A. Mouche, J. Knaff, Y. Zhao, Y. Zhao, L. Vinour, Y. Quilfen, and B. Chapron, 2020: Extensive high-resolution synthetic aperture radar (SAR) data analysis of tropical cyclones: Comparisons with SFMR flights and best track. Mon. Wea. Rev., 148, 45454563, https://doi.org/10.1175/MWR-D-20-0005.1.

    • Search Google Scholar
    • Export Citation
  • De Boor, C., 1978: A Practical Guide to Splines. rev. ed. Springer, 392 pp.

  • DeMaria, M., and R. W. Jones, 1993: Optimization of a hurricane track forecast model with the adjoint model equations. Mon. Wea. Rev., 121, 17301745, https://doi.org/10.1175/1520-0493(1993)121<1730:OOAHTF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., and J. Kaplan, 1999: An updated statistical Hurricane Intensity Prediction Scheme (SHIPS) for the Atlantic and eastern North Pacific basins. Wea. Forecasting, 14, 326337, https://doi.org/10.1175/1520-0434(1999)014<0326:AUSHIP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dong, J., and Coauthors, 2020: The evaluation of real-time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) model performance for the 2019 Atlantic hurricane season. Atmosphere, 11, 617, https://doi.org/10.3390/atmos11060617.

    • Search Google Scholar
    • Export Citation
  • Franklin, J. L., M. L. Black, and K. Valde, 2003: GPS dropwindsonde wind profiles in hurricanes and their operational implications. Wea. Forecasting, 18, 3244, https://doi.org/10.1175/1520-0434(2003)018<0032:GDWPIH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holbach, H. M., and Coauthors, 2023: Recent advancements in aircraft and in situ observations of tropical cyclones. Trop. Cyclone Res. Rev., 12, 8199, https://doi.org/10.1016/j.tcrr.2023.06.001.

    • Search Google Scholar
    • Export Citation
  • Holthuijsen, L. H., M. D. Powell, and J. D. Pietrzak, 2012: Wind and waves in extreme hurricanes. J. Geophys. Res., 117, C09003, https://doi.org/10.1029/2012JC007983.

    • Search Google Scholar
    • Export Citation
  • Kepert, J. D., 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part I: Linear theory. J. Atmos. Sci., 58, 24692484, https://doi.org/10.1175/1520-0469(2001)058<2469:TDOBLJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kepert, J. D., 2023: A parametric model of tropical cyclone surface winds for sea and land. Wea. Forecasting, 38, 17391757, https://doi.org/10.1175/WAF-D-23-0028.1.

    • Search Google Scholar
    • Export Citation
  • Kepert, J. D., and Y. Wang, 2001: The dynamics of boundary layer jets within the tropical cyclone core. Part II: Nonlinear enhancement. J. Atmos. Sci., 58, 24852501, https://doi.org/10.1175/1520-0469(2001)058<2485:TDOBLJ>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and E. W. Uhlhorn, 2014: Improved stepped frequency microwave radiometer tropical cyclone surface winds in heavy precipitation. J. Atmos. Oceanic Technol., 31, 23922408, https://doi.org/10.1175/JTECH-D-14-00028.1.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and H. Jiang, 2017: Examination of surface wind asymmetries in tropical cyclones. Part I: General structure and wind shear impacts. Mon. Wea. Rev., 145, 39894009, https://doi.org/10.1175/MWR-D-17-0019.1.

    • Search Google Scholar
    • Export Citation
  • Klotz, B. W., and D. S. Nolan, 2019: SFMR surface wind undersampling over the tropical cyclone life cycle. Mon. Wea. Rev., 147, 247268, https://doi.org/10.1175/MWR-D-18-0296.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., and C. R. Sampson, 2015: After a decade are Atlantic tropical cyclone gale force wind radii forecasts now skillful? Wea. Forecasting, 30, 702709, https://doi.org/10.1175/WAF-D-14-00149.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., M. DeMaria, D. A. Molenar, C. R. Sampson, and M. G. Seybold, 2011: An automated, objective, multiple-satellite platform tropical cyclone surface wind analysis. J. Appl. Meteor. Climatol., 50, 21492166, https://doi.org/10.1175/2011JAMC2673.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., S. P. Longmore, R. T. DeMaria, and D. A. Molenar, 2015: Improved tropical-cyclone flight-level wind estimates using routine infrared satellite reconnaissance. J. Appl. Meteor. Climatol., 54, 463478, https://doi.org/10.1175/JAMC-D-14-0112.1.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., and Coauthors, 2021: Estimating tropical cyclone surface winds: Current status, emerging technologies, historical evolution, and a look to the future. Trop. Cyclone Res. Rev., 10, 125150, https://doi.org/10.1016/j.tcrr.2021.09.002.

    • Search Google Scholar
    • Export Citation
  • Koch, S. E., M. desJardins, and P. J. Kocin, 1983: An interactive Barnes objective map analysis scheme for use with satellite and conventional data. J. Climate Appl. Meteor., 22, 14871503, https://doi.org/10.1175/1520-0450(1983)022<1487:AIBOMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 35763592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Search Google Scholar
    • Export Citation
  • Marchok, T. P., 2002: How the NCEP tropical cyclone tracker works. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., P1.13, https://ams.confex.com/ams/pdfpapers/37628.pdf.

  • Marchok, T. P., 2021: Important factors in the tracking of tropical cyclones in operational models. J. Appl. Meteor. Climatol., 60, 12651284, https://doi.org/10.1175/JAMC-D-20-0175.1.

    • Search Google Scholar
    • Export Citation
  • Merrill, R. T., 1987: An experiment in statistical prediction of tropical cyclone intensity change. NOAA Tech. Memo. NWS NHC-34, 37 pp., https://repository.library.noaa.gov/view/noaa/7212.

  • Mouche, A. A., B. Chapron, B. Zhang, and R. Husson, 2017: Combined co- and cross-polarized SAR measurements under extreme wind conditions. IEEE Trans. Geosci. Remote Sens., 55, 67466755, https://doi.org/10.1109/TGRS.2017.2732508.

    • Search Google Scholar
    • Export Citation
  • Mouche, A. A., B. Chapron, J. Knaff, Y. Zhao, B. Zhang, and C. Combot, 2019: Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: Application to Irma category 5 hurricane. J. Geophys. Res. Oceans, 124, 39053922, https://doi.org/10.1029/2019JC015056.

    • Search Google Scholar
    • Export Citation
  • Mueller, K. J., M. DeMaria, J. Knaff, J. P. Kossin, and T. H. Vonder Haar, 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, 21, 9901005, https://doi.org/10.1175/WAF955.1.

    • Search Google Scholar
    • Export Citation
  • National Hurricane Operations Plan, 2022: NOAA Federal Coordinator Meteorological Services and Supporting Research. Rep. FCM-P12-2022, 186 pp., https://www.weather.gov/media/nws/IHC2022/2022_NHOP_June_1.pdf.

  • Nolan, D. S., B. D. McNoldy, and J. Yunge, 2021: Evaluation of the surface wind field over land in WRF simulations of Hurricane Wilma (2005). Part I: Model initialization and simulation validation. Mon. Wea. Rev., 149, 679695, https://doi.org/10.1175/MWR-D-20-0199.1.

    • Search Google Scholar
    • Export Citation
  • Nordberg, W., J. Conaway, D. B. Ross, and T. Wilheit, 1971: Measurements of microwave emission from a foam-covered, wind-driven sea. J. Atmos. Sci., 28, 429435, https://doi.org/10.1175/1520-0469(1971)028%3C0429:MOMEFA%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Pasch, R. J., R. Berg, D. R. Roberts, and P. P. Papin, 2021: Tropical cyclone report: Hurricane Laura (20–29 August 2020). NHC Tech. Rep. AL132020, 75 pp., https://www.nhc.noaa.gov/data/tcr/AL132020_Laura.pdf.

  • Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn. 7778, 5364, https://doi.org/10.1016/S0167-6105(98)00131-7.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., E. W. Uhlhorn, and J. D. Kepert, 2009: Estimating maximum surface winds from hurricane reconnaissance measurements. Wea. Forecasting, 24, 868883, https://doi.org/10.1175/2008WAF2007087.1.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., and Coauthors, 2010: Reconstruction of Hurricane Katrina’s wind fields for storm surge and wave hindcasting. Ocean Eng., 37, 2636, https://doi.org/10.1016/j.oceaneng.2009.08.014.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, 1992: Numerical Recipes in FORTRAN 77: The Art of Scientific Computing. Cambridge University Press, 1010 pp.

  • Rappaport, E. N., J.-G. Jiing, C. W. Landsea, S. T. Murillo, and J. L. Franklin, 2012: The joint hurricane test bed: Its first decade of tropical cyclone research-to-operations activities reviewed. Bull. Amer. Meteor. Soc., 93, 371380, https://doi.org/10.1175/BAMS-D-11-00037.1.

    • Search Google Scholar
    • Export Citation
  • Rogers, R., S. Lorsolo, P. Reasor, J. Gamache, and F. Marks, 2012: Multiscale analysis of tropical cyclone kinematic structure from airborne Doppler radar composites. Mon. Wea. Rev., 140, 7799, https://doi.org/10.1175/MWR-D-10-05075.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and A. J. Schrader, 2000: The Automated Tropical Cyclone Forecasting System (version 3.2). Bull. Amer. Meteor. Soc., 81, 12311240, https://doi.org/10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., and J. A. Knaff, 2015: A consensus forecast for tropical cyclone gale wind radii. Wea. Forecasting, 30, 13971403, https://doi.org/10.1175/WAF-D-15-0009.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., E. M. Fukada, J. A. Knaff, B. R. Strahl, M. J. Brennan, and T. Marchok, 2017: Tropical cyclone gale wind radii estimates for the western North Pacific. Wea. Forecasting, 32, 10291040, https://doi.org/10.1175/WAF-D-16-0196.1.

    • Search Google Scholar
    • Export Citation
  • Sampson, C. R., J. S. Goerss, J. A. Knaff, B. R. Strahl, E. M. Fukada, and E. A. Serra, 2018: Tropical cyclone gale wind radii estimates, forecasts, and error forecast for the western North Pacific. Wea. Forecasting, 33, 10811092, https://doi.org/10.1175/WAF-D-17-0153.1.

    • Search Google Scholar
    • Export Citation
  • Sapp, J. W., S. O. Alsweiss, Z. Jelenak, P. S. Chang, and J. Carswell, 2019: Stepped frequency microwave radiometer wind-speed retrieval improvements. Remote Sens., 11, 214, https://doi.org/10.3390/rs11030214.

    • Search Google Scholar
    • Export Citation
  • Schwerdt, R. W., F. P. Ho, and R. W. Watkins, 1979: Meteorological criteria for standard project hurricane and probable maximum hurricane wind fields, Gulf and East Coasts of the United States. NOAA Tech. Rep. NWS 23, 356 pp., https://repository.library.noaa.gov/view/noaa/6948/noaa_6948_DS1.pdf.

  • Thacker, W. C., 1988: Fitting models to inadequate data by enforcing spatial and temporal smoothness. J. Geophys. Res., 93, 10 65510 665, https://doi.org/10.1029/JC093iC09p10655.

    • Search Google Scholar
    • Export Citation
  • Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting, 27, 715729, https://doi.org/10.1175/WAF-D-11-00085.1.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and P. G. Black, 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99116, https://doi.org/10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., and D. S. Nolan, 2012: Observational undersampling in tropical cyclones and implications for estimated intensity. Mon. Wea. Rev., 140, 825840, https://doi.org/10.1175/MWR-D-11-00073.1.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., P. G. Black, J. L. Franklin, M. Goodberlet, J. Carswell, and A. S. Goldstein, 2007: Hurricane surface wind measurements from an operational stepped frequency microwave radiometer. Mon. Wea. Rev., 135, 30703085, https://doi.org/10.1175/MWR3454.1.

    • Search Google Scholar
    • Export Citation
  • Uhlhorn, E. W., B. W. Klotz, T. Vukicevic, P. D. Reasor, and R. F. Rogers, 2014: Observed hurricane wind speed asymmetries and relationships to motion and environmental shear. Mon. Wea. Rev., 142, 12901311, https://doi.org/10.1175/MWR-D-13-00249.1.

    • Search Google Scholar
    • Export Citation
  • Willoughby, H. E., and M. E. Rahn, 2004: Parametric representation of the primary hurricane vortex. Part I: Observations and evaluation of the Holland (1980) model. Mon. Wea. Rev., 132, 30333048, https://doi.org/10.1175/MWR2831.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, J. A., and E. W. Uhlhorn, 2012: Hurricane sea surface inflow angle and an observation-based parametric model. Mon. Wea. Rev., 140, 35873605, https://doi.org/10.1175/MWR-D-11-00339.1.

    • Search Google Scholar
    • Export Citation
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