• Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430, https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE, 98, 704716, https://doi.org/10.1109/JPROC.2010.2043918.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilburn, K., F. Wentz, D. Smith, and P. Ashcroft, 2006: Correcting active scatterometer data for the effects of rain using passive radiometer data. J. Appl. Meteor. Climatol., 45, 382398, https://doi.org/10.1175/JAM2357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., C. S. Velden, and A. J. Wimmers, 2018: A global climatology of tropical cyclone eyes. Mon. Wea. Rev., 146, 20892101, https://doi.org/10.1175/MWR-D-17-0343.1.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, W., M. Portabella, A. Stoffelen, and A. Verhoef, 2013: On the characteristics of ASCAT wind direction ambiguities. Atmos. Meas. Tech., 6, 10531060. https://doi.org/10.5194/amt-6-1053-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mayers, D., and C. Ruf, 2019: Tropical cyclone center fix using CYGNSS winds. J. Appl. Meteor. Climatol., 58, 1993–2003, https://doi.org/10.1175/JAMC-D-19-0054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meissner, T., L. Ricciardulli, and F. J. Wentz, 2017: Capability of the SMAP mission to measure ocean surface winds in storms. Bull. Amer. Meteor. Soc., 98, 16601677, https://doi.org/10.1175/BAMS-D-16-0052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morris, M., and C. S. Ruf, 2017: Determining tropical cyclone surface wind speed structure and intensity with the CYGNSS satellite constellation. J. Appl. Meteor. Climatol., 56, 18471865, https://doi.org/10.1175/JAMC-D-16-0375.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298, https://doi.org/10.1175/WAF975.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Portabella, M., A. Stoffelen, W. Lin, A. Turiel, A. Verhoef, J. Verspeek, and J. Ballabrera-Poy, 2012: Rain effects on ASCAT-retrieved winds: Toward an improved quality control. IEEE Trans. Geosci. Remote Sens., 50, 24952506, https://doi.org/10.1109/TGRS.2012.2185933.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rappaport, E. N., and Coauthors, 2009: Advances and challenges at the National Hurricane Center. Wea. Forecasting, 24, 395419, https://doi.org/10.1175/2008WAF2222128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ruf, C. S., and Coauthors, 2016: New ocean winds satellite mission to probe hurricanes and tropical convection. Bull. Amer. Meteor. Soc., 97, 385395, https://doi.org/10.1175/BAMS-D-14-00218.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trahan, S., and L. Sparling, 2012: An analysis of NCEP tropical cyclone vitals and potential effects on forecasting models. Wea. Forecasting, 27, 744756, https://doi.org/10.1175/WAF-D-11-00063.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wimmers, A. J., and C. S. Velden, 2016: Advancements in objective multisatellite tropical cyclone center fixing. J. Appl. Meteor. Climatol., 55, 197212, https://doi.org/10.1175/JAMC-D-15-0098.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 2 0 0
Full Text Views 740 0 0
PDF Downloads 497 0 0

MTrack: Improved Center Fix of Tropical Cyclones from SMAP Wind Observations

View More View Less
  • 1 University of Michigan, Ann Arbor, Michigan
  • | 2 University of Michigan, Ann Arbor, Michigan
Restricted access

Abstract

MTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.

© 2021 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: David Mayers, drmayers@umich.edu

Abstract

MTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.

© 2021 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: David Mayers, drmayers@umich.edu
Save