Editorial Type:
Article
Article Type:
Research Article

A Unique Satellite-Based Sea Surface Wind Speed Algorithm and Its Application in Tropical Cyclone Intensity Analysis

Sungwook Hong Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul, South Korea

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Hwa-Jeong Seo National Meteorological Satellite Center, Korea Meteorological Administration, Gwanghyewon-myeon, South Korea

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Young-Joo Kwon Department of Environment, Energy, and Geoinfomatics, Sejong University, Seoul, South Korea

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Print Publication:
01 Jul 2016
DOI:
https://doi.org/10.1175/JTECH-D-15-0128.1
Page(s):
1363–1375
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Abstract

This study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

Corresponding author address: Dr. Sungwook Hong, Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, South Korea. E-mail: sesttiya@gmail.com

Abstract

This study proposes a sea surface wind speed retrieval algorithm (the Hong wind speed algorithm) for use in rainy and rain-free conditions. It uses a combination of satellite-observed microwave brightness temperatures, sea surface temperatures, and horizontally polarized surface reflectivities from the fast Radiative Transfer for TOVS (RTTOV), and surface and atmospheric profiles from the European Centre for Medium-Range Weather Forecasts (ECMWF). Regression relationships between satellite-observed brightness temperature and satellite-simulated brightness temperatures, satellite-simulated brightness temperatures, rough surface reflectivities, and between sea surface roughness and sea surface wind speed are derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2). Validation results of sea surface wind speed between the proposed algorithm and the Tropical Atmosphere Ocean (TAO) data show that the estimated bias and RMSE for AMSR-2 6.925- and 10.65-GHz bands are 0.09 and 1.13 m s−1, and −0.52 and 1.21 m s−1, respectively. Typhoon intensities such as the current intensity (CI) number, maximum wind speed, and minimum pressure level based on the proposed technique (the Hong technique) are compared with best-track data from the Japan Meteorological Agency (JMA), the Joint Typhoon Warning Center (JTWC), and the Cooperative Institute for Mesoscale Meteorological Studies (CIMSS) for 13 typhoons that occurred in the northeastern Pacific Ocean throughout 2012. Although the results show good agreement for low- and medium-range typhoon intensities, the discrepancy increases with typhoon intensity. Consequently, this study provides a useful retrieval algorithm for estimating sea surface wind speed, even during rainy conditions, and for analyzing characteristics of tropical cyclones.

Corresponding author address: Dr. Sungwook Hong, Department of Environment, Energy, and Geoinfomatics, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, South Korea. E-mail: sesttiya@gmail.com
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  • Brown, D. B., and Franklin J. L. , 2004: Dvorak TC wind speed biases determined from reconnaissance-based “best track” data (1997–2003). 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 3D.5. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75193.htm.]

  • Brueske, K. F., and Velden C. S. , 2003: Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series Advanced Microwave Sounding Unit (AMSU). Mon. Wea. Rev., 131, 687–697, doi:10.1175/1520-0493(2003)131<0687:SBTCIE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Choudhury, B. J., Schmugge T. J. , Chang A. , and Newton R. W. , 1979: Effect of surface roughness on the microwave emission from soils. J. Geophys. Res., 84, 5699–5706, doi:10.1029/JC084iC09p05699.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , Knaff J. A. , and Vonder Haar T. H. , 2004: Evaluation of Advanced Microwave Sounder Unit (AMSU) tropical-cyclone intensity and size estimation algorithms. J. App. Meteor., 43, 282–296, doi:10.1175/1520-0450(2004)043<0282:EOAMSU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Demuth, J. L., DeMaria M. , and Knaff J. A. , 2006: Improvement of Advanced Microwave Sounding Unit tropical cyclone intensity and size estimation algorithms. J. Appl. Meteor. Climatol., 45, 1573–1581, doi:10.1175/JAM2429.1.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420–430, doi:10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. NESDIS 11, 47 pp.

  • Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686–688, doi:10.1038/nature03906.

    • Search Google Scholar
    • Export Citation

  • English, S. J., and Hewison T. J. , 1998: Fast generic millimeter-wave emissivity model. Microwave Remote Sensing of the Atmosphere and Environment, T Hayasaka et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 3503), 288–300, doi:10.1117/12.319490.

  • Guard, C. P., 1988: Tropical cyclone studies: Part 3—Results of a tropical cyclone accuracy study using polar orbiting satellite data. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 3-1–3-36.

  • Herndon, D., Velden C. S. , Brueske K. , Wacker R. , and Kabat B. , 2004: Upgrades to the UW-CIMSS AMSU-based tropical cyclone intensity estimation algorithm. 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 4D.1. [Available online at https://ams.confex.com/ams/26HURR/techprogram/paper_75933.htm.]

  • Hong, S., 2009a: Detection of Asian dust (Hwangsa) over the Yellow Sea by decomposition of unpolarized infrared reflectivity. Atmos. Environ., 43, 5887–5893, doi:10.1016/j.atmosenv.2009.08.024.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2009b: Retrieval of refractive index over specular surfaces for remote sensing applications. J. Appl. Remote Sens., 3, 033560, doi:10.1117/1.3265997.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010a: Decomposition of unpolarized emissivity. Int. J. Remote Sens., 31, 2109–2114, doi:10.1080/01431160903329349.

  • Hong, S., 2010b: Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sens. Environ., 114, 1136–1140, doi:10.1016/j.rse.2009.12.015.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010c: Global retrieval of small-scale roughness over land surfaces at microwave frequency. J. Hydrol., 389, 121–126, doi:10.1016/j.jhydrol.2010.05.036.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2010d: Surface roughness and polarization ratio in microwave remote sensing. Int. J. Remote Sens., 31, 2709–2716, doi:10.1080/01431161003627855.

    • Search Google Scholar
    • Export Citation

  • Hong, S., 2013: Polarization conversion for specular components of surface reflection. IEEE Geosci. Remote Sens. Lett., 10, 1469–1472, doi:10.1109/LGRS.2013.2260524.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2010: Global trends of sea ice: Small-scale roughness and refractive index. J. Climate, 23, 4669–4676, doi:10.1175/2010JCLI3697.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2011: A physically-based inversion algorithm for retrieving soil moisture in passive microwave remote sensing. J. Hydrol., 405, 24–30, doi:10.1016/j.jhydrol.2011.05.005.

    • Search Google Scholar
    • Export Citation

  • Hong, S., and Shin I. , 2013: Wind speed retrieval based on sea surface roughness measurements from spaceborne microwave radiometers. J. Appl. Meteor. Climatol., 52, 507–516, doi:10.1175/JAMC-D-11-0209.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , and Ou M. , 2010: Comparison of the Infrared Surface Emissivity Model (ISEM) with a physical emissivity model. J. Atmos. Oceanic Technol., 27, 345–352, doi:10.1175/2009JTECHA1311.1.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Shin I. , Byun Y. , Seo H.-J. , and Kim Y. , 2014: Analysis of sea ice surface properties using ASH and Hong approximations in satellite remote sensing. Remote Sens. Lett., 5, 139–147, doi:10.1080/2150704X.2014.888106.

    • Search Google Scholar
    • Export Citation

  • Hong, S., Seo H.-J. , Kim N. , and Shin I. , 2015: Physical retrieval of tropical ocean surface wind speed under rain-free conditions using spaceborne microwave radiometers. Remote Sens. Lett., 6, 380–389, doi:10.1080/2150704X.2015.1037466.

    • Search Google Scholar
    • Export Citation

  • Hoshino, S., and Nakazawa T. , 2007: Estimation of tropical cyclone’s intensity using TRMM/TMI brightness temperature data. J. Meteor. Soc. Japan, 85, 437–454. http://www.jstage.jst.go.jp/article/jmsj/85/4/437/_pdf/-char/ja/, doi:10.2151/jmsj.85.437.

    • Search Google Scholar
    • Export Citation

  • JMA, 2013: Annual report on the activities of the RSMC Tokyo–Typhoon Center 2012. 92 pp. [Available online at http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/AnnualReport/2012/Text/Text2012.pdf.]

  • JTWC, 1974: Annual tropical cyclone report: 1974. U.S. Fleet Weather Central, JTWC, 126 pp. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/atcr/1974atcr.pdf.]

  • Kawanishi, T., and Coauthors, 2003: The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA’s contribution to the EOS for global energy and water cycle studies. IEEE Trans. Geosci. Remote Sens., 41, 184–194, doi:10.1109/TGRS.2002.808331.

    • Search Google Scholar
    • Export Citation

  • Knaff, J. A., Brown D. P. , Courtney J. , Gallina G. M. , and Beven J. L. II, 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25, 1362–1379, doi:10.1175/2010WAF2222375.1.

    • Search Google Scholar
    • Export Citation

  • Koba, H., Osano S. , Hagiwara T. , Akashi S. , and Kikuchi T. , 1991: Relationships between the CI number and central pressure and maximum wind speed in typhoons (in Japanese). Geophys. Mag., 44, 15–25.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., and Velden C. S. , 2004: A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique. Mon. Wea. Rev., 132, 165–173, doi:10.1175/1520-0493(2004)132<0165:APBITC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Kramer, H. J., 2014: GCOM (Global Change Observation Mission-Water). European Space Agency, accessed 31 October 2014. [Available online at https://directory.eoportal.org/web/eoportal/satellite-missions/g/gcom.]

  • Kruk, M. C., Knapp K. R. , and Levinson D. H. , 2010: A technique for combining global tropical cyclone best track data. J. Atmos. Oceanic Technol., 27, 680–692, doi:10.1175/2009JTECHA1267.1.

    • Search Google Scholar
    • Export Citation

  • Kummerow, C., and Ferraro R. , 2006: EOS/AMSR-E level-2 rainfall. Algorithm Theoretical Basis Doc., 10 pp. [Available online at http://nsidc.org/sites/nsidc.org/files/files/amsr_atbd_supp06_L2_rain.pdf.]

  • Liu, Q., and Weng F. , 2003: Retrieval of sea surface wind vectors from simulated satellite microwave polarimetric measurements. Radio Sci., 38, 8078, doi:10.1029/2002RS002729.

    • Search Google Scholar
    • Export Citation

  • Liu, Q., Weng F. , and English S. J. , 2011: An improved fast microwave water emissivity model. IEEE Trans. Geosci. Remote Sens., 49, 1238–1250, doi:10.1109/TGRS.2010.2064779.

    • Search Google Scholar
    • Export Citation

  • Lu, X., and Yu H. , 2013: An objective tropical cyclone intensity estimation model based on digital IR satellite images. Trop. Cyclone Res. Rev., 2, 233–241, doi:10.6057/2013TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Mayfield, M., McAdie C. J. , and Pike A. C. , 1988: Tropical cyclone studies: Part 2—A preliminary evaluation of the dispersion of tropical cyclone position and intensity estimates determined from satellite imagery. Federal Coordinator for Meteorological Services and Supporting Research FCM-R11-1988, 2-1–2-17.

  • Meissner, T., and Wentz F. , 2009: Wind vector retrievals under rain with passive satellite microwave radiometers. IEEE Trans. Geosci. Electron., 47, 3065–3083.

    • Search Google Scholar
    • Export Citation

  • NOAA ARL, 2014: Global Data Assimilation System (GDAS1) archive information. Accessed 3 February 2014. [Available online at http://ready.arl.noaa.gov/gdas1.php.]

  • Randa, J., and Coauthors, 2008: Recommended terminology for microwave radiometry. NIST Tech. Note 1551, 32 pp.

  • Saitoh, S., and Shibata A. , 2010: AMSR-E all weather sea surface wind speed (in Japanese). Tenki, 57 (1), 5–18. [Available online at http://www.metsoc.jp/tenki/pdf/2010/2010_01_0005.pdf.]

    • Search Google Scholar
    • Export Citation

  • Saunders, R., 2006: RTTOV-8—Science and validation report. Version 1.6, NWP SAF Doc. NWPSAF-MO-TV-007, Met Office Doc. R8REP2006, 46 pp.

  • Shibata, A., 2002: AMSR/AMSR-E sea surface wind speed algorithm. EORC Bull./Tech. Rep. 9, 45–46.

  • Shibata, A., 2003: A change of microwave radiation from the ocean surface induced by air-sea temperature difference. Radio Sci., 38, 8063, doi:10.1029/2002RS002670.

    • Search Google Scholar
    • Export Citation

  • Shibata, A., 2007: Effect of air-sea temperature difference on ocean microwave brightness temperature estimated from AMSR, SeaWinds, and buoys. J. Oceanogr., 63, 863–872, doi:10.1007/s10872-007-0073-y.

    • Search Google Scholar
    • Export Citation

  • Stogryn, A., 1967: The apparent temperature of the sea at microwave frequencies. IEEE Trans. Antennas Propag., 15, 278–286, doi:10.1109/TAP.1967.1138900.

    • Search Google Scholar
    • Export Citation

  • Tang, C., 1974: The effect of droplets in the air–sea transition zone on the sea brightness temperature. J. Phys. Oceanogr., 4, 579–593, doi:10.1175/1520-0485(1974)004<0579:TEODIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Tousey, R., 1939: On calculating the optical constants from reflection coefficients. J. Opt. Soc. Amer., 29, 235–238, doi:10.1364/JOSA.29.000235.

    • Search Google Scholar
    • Export Citation

  • Uhlhorn, E. W., and Black P. G. , 2003: Verification of remotely sensed sea surface winds in hurricanes. J. Atmos. Oceanic Technol., 20, 99–116, doi:10.1175/1520-0426(2003)020<0099:VORSSS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Ulaby, F. T., Moore R. K. , and Fung A. E. , 1981: Microwave Remote Sensing Fundamentals and Radiometry. Vol. 1, Microwave Remote Sensing: Active and Passive, Artech House, 470 pp.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176.

  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 1195–1210, doi:10.1175/BAMS-87-9-1195.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., Burton A. , and Kuroiwa K. , 2012: The First International Workshop on Satellite Analysis of Tropical Cyclones: Summary of current operational methods to estimate intensity. Trop. Cyclone Res. Rev., 1, 469–481, doi:10.6057/2012TCRR04.05.

    • Search Google Scholar
    • Export Citation

  • Webster, P. J., Holland G. J. , Curry J. A. , and Chang H.-R. , 2005: Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844–1846, doi:10.1126/science.1116448.

    • Search Google Scholar
    • Export Citation

  • Wentz, F. J., 2002: AMSR ocean algorithm. EORC Bull./Tech. Rep. 9, 8–28.

  • Wentz, F. J., and Meissner T. , 2000: AMSR ocean algorithm. Version 2, Algorithm Theoretical Basis Doc., Remote Sensing Systems Tech. Proposal 121599A-1, 74 pp.

  • Wentz, F. J., Mattox L. A. , and Peteherych S. , 1986: New algorithms for microwave measurements of ocean winds: Applications to Seasat and the special sensor microwave imager. J. Geophys. Res., 91, 2289–2307, doi:10.1029/JC091iC02p02289.

    • Search Google Scholar
    • Export Citation

  • Wilheit, T. T., 1979: A model for the microwave emissivity of the ocean’s surface as a function of wind speed. IEEE Trans. Geosci. Electron., 17, 244–249, doi:10.1109/TGE.1979.294653.

    • Search Google Scholar
    • Export Citation

  • Wu, Y., and Weng F. , 2011: Detection and correction of AMSR-E radio-frequency interference (RFI). Acta Meteor. Sin., 25, 669–681, doi:10.1007/s13351-011-0510-0.

    • Search Google Scholar
    • Export Citation

  • Yan, B., and Weng F. , 2008: Applications of AMSR-E measurements for tropical cyclone predictions Part I: Retrieval of sea surface temperature and wind speed. Adv. Atmos. Sci., 25, 227–245, doi:10.1007/s00376-008-0227-x.

    • Search Google Scholar
    • Export Citation
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