• Aberson, S. D., and J. L. Franklin, 1999: Impact on hurricane track and intensity forecasts of GPS dropwindsonde observations from the first-season flights of the NOAA Gulfstream-IV jet aircraft. Bull. Amer. Meteor. Soc., 80, 421428, https://doi.org/10.1175/1520-0477(1999)080<0421:IOHTAI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aberson, S. D., and C. Sampson, 2003: On the predictability of tropical cyclone tracks in the Northwest Pacific basin. Mon. Wea. Rev., 131, 14911497, https://doi.org/10.1175/1520-0493(2003)131<1491:OTPOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aberson, S. D., A. Aksoy, T. Vukicevic, K. J. Sellwood, and X. Zhang, 2015: Assimilation of high-resolution tropical cyclone observations with an ensemble Kalman filter using HEDAS: Evaluation of the 2008–11 HWRF forecasts. Mon. Wea. Rev., 143, 511523, https://doi.org/10.1175/MWR-D-14-00138.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aberson, S. D., K. J. Sellwood, and P. A. Leighton, 2017: Calculating dropwindsonde location and time from TEMP-DROP messages for accurate assimilation and analysis. J. Atmos. Oceanic Technol., 34, 16731678, https://doi.org/10.1175/JTECH-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aksoy, A., 2013: Storm-relative observations in tropical cyclone data assimilation with an ensemble Kalman filter. Mon. Wea. Rev., 141, 506522, https://doi.org/10.1175/MWR-D-12-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aksoy, A., D. C. Dowell, and C. Snyder, 2009: A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part II: Short-range ensemble forecasts. Mon. Wea. Rev., 138, 12731292, https://doi.org/10.1175/2009MWR3086.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Aksoy, A., S. Lorsolo, T. Vukicevic, K. J. Sellwood, S. D. Aberson, and F. Zhang, 2012: The HWRF Hurricane Ensemble Data Assimilation System (HEDAS) for high-resolution data: The impact of airborne Doppler radar observations in an OSSE. Mon. Wea. Rev., 140, 18431862, https://doi.org/10.1175/MWR-D-11-00212.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, S. A., and Coauthors, 2013: NASA’s Genesis and Rapid Intensification Processes (GRIP) field experiment. Bull. Amer. Meteor. Soc., 94, 345363, https://doi.org/10.1175/BAMS-D-11-00232.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Braun, S. A., P. A. Newman, and G. M. Heymsfield, 2016: NASA’s Hurricane and Severe Storm Sentinel (HS3) investigation. Bull. Amer. Meteor. Soc., 97, 20852102, https://doi.org/10.1175/BAMS-D-15-00186.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, S. T., B. Lambrigtsen, R. F. Denning, T. Gaier, P. Kangaslahti, B. H. Lim, J. M. Tanabe, and A. B. Tanner, 2011: The High-Altitude MMIC Sounding Radiometer for the Global Hawk unmanned aerial vehicle: Instrument description and performance. IEEE Trans. Geosci. Remote Sens., 49, 32913301, https://doi.org/10.1109/TGRS.2011.2125973.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chou, K.-H., C.-C. Wu, P.-H. Lin, S. D. Aberson, M. Weissmann, F. Harnisch, and T. Nakazawa, 2011: The impact of dropwindsonde observations on typhoon track forecasts in DOTSTAR and T-PARC. Mon. Wea. Rev., 139, 17281743, https://doi.org/10.1175/2010MWR3582.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christophersen, H., A. Aksoy, J. Dunion, and K. J. Sellwood, 2017: The impact of NASA Global Hawk unmanned aircraft dropwindsonde observations on tropical cyclone track, intensity, and structure: Case studies. Mon. Wea. Rev., 145, 18171830, https://doi.org/10.1175/MWR-D-16-0332.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cione, J. J., E. A. Kalina, E. W. Uhlhorn, A. M. Farber, and B. Damiano, 2016: Coyote unmanned aircraft system observations in Hurricane Edouard. Earth Space Sci., 3, 370380, https://doi.org/10.1002/2016EA000187.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeMaria, M., C. R. Sampson, J. A. Knaff, and K. D. Musgrave, 2014: Is tropical cyclone intensity guidance improving? Bull. Amer. Meteor. Soc., 95, 387398, https://doi.org/10.1175/BAMS-D-12-00240.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doyle, J., and Coauthors, 2017: A view of tropical cyclones from above: The Tropical Cyclone Intensity experiment. Bull. Amer. Meteor. Soc., 98, 21132134, https://doi.org/10.1175/BAMS-D-16-0055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, C., and Coauthors, 2017: The extratropical transition of tropical cyclones. Part I: Cyclone evolution and direct impacts. Mon. Wea. Rev., 145, 43174344, https://doi.org/10.1175/MWR-D-17-0027.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gamache, J. F., 2005: Real-time dissemination of hurricane wind fields determined from airborne Doppler radar. JHT Project Final Rep., 38 pp., http://www.nhc.noaa.gov/jht/2003-2005reports/DOPLRgamache_JHTfinalreport.pdf.

  • Gopalakrishnan, S. G., F. Marks, J. A. Zhang, X. Zhang, J.-W. Bao, and V. Tallapragada, 2013: A study of the impacts of vertical diffusion on the structure and intensity of the tropical cyclones using the high-resolution HWRF system. J. Atmos. Sci., 70, 524541, https://doi.org/10.1175/JAS-D-11-0340.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., J. S. Whitaker, D. T. Kleist, M. Fiorino, and S. J. Benjamin, 2011: Predictions of 2010’s tropical cyclones using the GFS and ensemble-based data assimilation methods. Mon. Wea. Rev., 139, 32433247, https://doi.org/10.1175/MWR-D-11-00079.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, G. M., L. Tian, L. Li, M. McLinden, and J. I. Cervantes, 2013: Airborne radar observations of severe hailstorms: Implications for future spaceborne radar. J. Appl. Meteor. Climatol., 52, 18511867, https://doi.org/10.1175/JAMC-D-12-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hock, T. F., and J. L. Franklin, 1999: The NCAR GPS dropwindsonde. Bull. Amer. Meteor. Soc., 80, 407420, https://doi.org/10.1175/1520-0477(1999)080<0407:TNGD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarvinen, B. R., C. J. Neumann, and M. A. S. Davis, 1984: A tropical cyclone data tape for the North Atlantic basin, 1886-1983: Contents, limitations, and uses. NOAA Tech. Memo. NWS NHC 22, 21 pp., https://www.nhc.noaa.gov/pdf/NWS-NHC-1988-22.pdf.

  • Jones, S. C., and Coauthors, 2003: The extratropical transition of tropical cyclones: Forecast challenges, current understanding, and future directions. Wea. Forecasting, 18, 10521092, https://doi.org/10.1175/1520-0434(2003)018<1052:TETOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y.-H., T. K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. A. Anthes, 2004: Inversion and error estimation of GPS radio occultation data. J. Meteor. Soc. Japan, 82, 507531, https://doi.org/10.2151/jmsj.2004.507.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Majumdar, S. J., M. J. Brennan, and K. Howard, 2013: The impact of dropwindsonde and supplemental rawinsonde observations on track forecasts for Hurricane Irene (2011). Wea. Forecasting, 28, 13851403, https://doi.org/10.1175/WAF-D-13-00018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGill, M., D. Hlavka, W. Hart, V. S. Scott, J. Spinhirne, and B. Schmid, 2002: Cloud Physics Lidar: Instrument description and initial measurement results. Appl. Opt., 41, 37253734, https://doi.org/10.1364/AO.41.003725.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powell, M. D., and T. A. Reinhold, 2007: Tropical cyclone destructive potential by integrated kinetic energy. Bull. Amer. Meteor. Soc., 88, 513526, https://doi.org/10.1175/BAMS-88-4-513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Revercomb, H. E., and J. K. Taylor, 2017: Hurricane and Severe Storm Sentinel (HS3) Scanning High-Resolution Interferometer Sounder (S-HIS). NASA Global Hydrology Center DAAC, Huntsville, AL, https://doi.org/10.5067/HS3/SHIS/DATA201.

    • Crossref
    • Export Citation
  • Rogers, R. F., and Coauthors, 2006: The Intensity Forecasting Experiment: A NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull. Amer. Meteor. Soc., 87, 15231538, https://doi.org/10.1175/BAMS-87-11-1523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., P. Reasor, and S. Lorsolo, 2013: Airborne Doppler observations of the inner-core structural differences between intensifying and steady-state tropical cyclones. Mon. Wea. Rev., 141, 29702991, https://doi.org/10.1175/MWR-D-12-00357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, R. F., J. A. Zhang, J. Zawislak, H. Jiang, G. R. Alvey III, E. J. Zipser, and S. N. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part II: Kinematic structure and the distribution of deep convection. Mon. Wea. Rev., 144, 33553376, https://doi.org/10.1175/MWR-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Susskind, J., C. D. Barnet, and J. M. Blaisdell, 2003: Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds. IEEE Trans. Geosci. Remote Sens., 41, 390409, https://doi.org/10.1109/TGRS.2002.808236.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., 2014: The impact of targeted dropwindsonde observations on tropical cyclone intensity forecasts of four weak systems during PREDICT. Mon. Wea. Rev., 142, 28602878, https://doi.org/10.1175/MWR-D-13-00284.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vömel, H., K. Young, and T. F. Hock, 2016: NCAR GPS dropsonde humidity dry bias. NCAR Tech. Note NCAR/TN-531+STR, 8 pp., https://doi.org/10.5065/D6XS5SGX.

    • Crossref
    • Export Citation
  • Watts, A. C., V. G. Ambrosia, and E. A. Hinkley, 2012: Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use. Remote Sens., 2012, 16711692, https://doi.org/10.3390/rs4061671.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. International Geophysics Series, Vol. 59, Elsevier, 467 pp.

  • Yuter, S. E., and R. A. Houze, 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123, 19411963, https://doi.org/10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zawislak, J., H. Jiang, G. R. Alvey III, E. J. Zipser, R. F. Rogers, J. A. Zhang, and S. N. Stevenson, 2016: Observations of the structure and evolution of Hurricane Edouard (2014) during intensity change. Part I: Relationship between the thermodynamic structure and precipitation. Mon. Wea. Rev., 144, 33333354, https://doi.org/10.1175/MWR-D-16-0018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 240 139 12
PDF Downloads 184 126 12

Composite Impact of Global Hawk Unmanned Aircraft Dropwindsondes on Tropical Cyclone Analyses and Forecasts

View More View Less
  • 1 Cooperative Institute for Marine and Atmospheric Studies, University of Miami, and NOAA/Atlantic Oceanographic and Meteorological Laboratory/Hurricane Research Division, Miami, Florida
  • | 2 NOAA/Atlantic Oceanographic and Meteorological Laboratory/Hurricane Research Division, Miami, Florida
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

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 (~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 ~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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hui Christophersen, hui.christophersen@noaa.gov

Abstract

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 (~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 ~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.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hui Christophersen, hui.christophersen@noaa.gov
Save