• Ali, M. M., D. Swain, and R. A. Weller, 2004: Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach. Geophys. Res. Lett., 31, L20308, doi:10.1029/2004GL021192.

    • Search Google Scholar
    • Export Citation
  • Baker-Yeboah, S., D. R. Watts, and D. A. Byrne, 2009: Measurements of sea surface height variability in the eastern South Atlantic from pressure sensor–equipped inverted echo sounders: Baroclinic and barotropic components. J. Atmos. Oceanic Technol., 26, 25932609, doi:10.1175/2009JTECHO659.1.

    • Search Google Scholar
    • Export Citation
  • Balaguru, K., G. R. Foltz, L. R. Leung, E. D’Asaro, K. A. Emanuel, H. Liu, and S. E. Zedler, 2015: Dynamic potential intensity: An improved representation of the ocean’s impact on tropical cyclones. Geophys. Res. Lett., 42, 6739–6746, doi:10.1002/2015GL064822.

    • Search Google Scholar
    • Export Citation
  • Bender, M. A., and I. Ginis, 2000: Real-case simulations of hurricane–ocean interaction using a high-resolution coupled model: Effects on hurricane intensity. Mon. Wea. Rev., 128, 917946, doi:10.1175/1520-0493(2000)128<0917:RCSOHO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bender, M. A., I. Ginis, and Y. Kurihara, 1993: Numerical simulations of tropical cyclone–ocean interaction with a high-resolution coupled model. J. Geophys. Res., 98, 23 24523 263, doi:10.1029/93JD02370.

    • Search Google Scholar
    • Export Citation
  • Bernie, D. J., S. J. Woolnough, J. M. Slingo, and E. Guilyardi, 2005: Modeling diurnal and intraseasonal variability of the ocean mixed layer. J. Climate, 18, 11901202, doi:10.1175/JCLI3319.1.

    • Search Google Scholar
    • Export Citation
  • Boyer, T. P., and Coauthors, 2009: Introduction. World Ocean Database 2009, NOAA Atlas NESDIS 66, 216 pp.

  • Carnes, M. R., 2009: Description and evaluation of GDEM-V3.0. NRL Memo. Rep. NRL/MR/7330-09-9165, 24 pp.

  • Carnes, M. R., J. L. Mitchell, and P. W. Dewitt, 1990: Synthetic temperature profiles derived from Geosat altimetry—Comparison with air-dropped expendable bathythermograph profiles. J. Geophys. Res., 95, 17 97917 992, doi:10.1029/JC095iC10p17979.

    • Search Google Scholar
    • Export Citation
  • Cione, J. J., 2015: The relative roles of the ocean and atmosphere as revealed by buoy air–sea observations in hurricanes. Mon. Wea. Rev., 143, 904913, doi:10.1175/MWR-D-13-00380.1.

    • Search Google Scholar
    • Export Citation
  • Cione, J. J., and E. Uhlhorn, 2003: Sea surface temperature variability in hurricanes: Implications with respect to intensity change. Mon. Wea. Rev., 131, 17831796, doi:10.1175//2562.1.

    • Search Google Scholar
    • Export Citation
  • Cione, J. J., P. G. Black, and S. H. Houston, 2000: Surface observations in the hurricane environment. Mon. Wea. Rev., 128, 15501561, doi:10.1175/1520-0493(2000)128<1550:SOITHE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cione, J. J., E. A. Kalina, J. A. Zhang, and E. W. Uhlhorn, 2013: Observations of air–sea interaction and intensity change in hurricanes. Mon. Wea. Rev., 141, 23682382, doi:10.1175/MWR-D-12-00070.1.

    • Search Google Scholar
    • Export Citation
  • D’Asaro, E. A., and Coauthors, 2014: Impact of typhoons on the ocean in the Pacific. Bull. Amer. Meteor. Soc., 95, 14051418, doi:10.1175/BAMS-D-12-00104.1.

    • Search Google Scholar
    • Export Citation
  • Ducet, N., P. Y. Le Traon, and G. Reverdin, 2000: Global high-resolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and-2. J. Geophys. Res., 105, 19 47719 498, doi:10.1029/2000JC900063.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585605, doi:10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., 1999: Thermodynamic control of hurricane intensity. Nature, 401, 665669, doi:10.1038/44326.

  • Emanuel, K. A., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61, 843858, doi:10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fox, D. N., W. J. Teague, C. N. Barron, M. R. Carnes, and C. M. Lee, 2002: The Modular Ocean Data Assimilation System (MODAS). J. Atmos. Oceanic Technol., 19, 240252, doi:10.1175/1520-0426(2002)019<0240:TMODAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fu, L. L., E. J. Christensen, C. A. Yamarone, M. Lefebvre, Y. Menard, M. Dorrer, and P. Escudier, 1994: TOPEX/POSEIDON mission overview. J. Geophys. Res., 99, 24 36924 381, doi:10.1029/94JC01761.

    • Search Google Scholar
    • Export Citation
  • Gallacher, P. C., R. Rotunno, and K. A. Emanuel, 1989: Tropical cyclogenesis in a coupled ocean-atmosphere model. Preprints, 18th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 121–122.

  • Gentemann, C. L., T. Meissner, and F. J. Wentz, 2010: Accuracy of satellite sea surface temperatures at 7 and 11 GHz. IEEE T. Geosci. Remote, 48, 10091018, doi:10.1109/TGRS.2009.2030322.

    • Search Google Scholar
    • Export Citation
  • Gilson, J., D. Roemmich, B. Cornuelle, and L. L. Fu, 1998: Relationship of TOPEX/Poseidon altimetric height to steric height and circulation in the North Pacific. J. Geophys. Res., 103, 27 94727 965, doi:10.1029/98JC01680.

    • Search Google Scholar
    • Export Citation
  • Goni, G., and J. A. Trinanes, 2003: Ocean thermal structure monitoring could aid in the intensity forecast of tropical cyclones. Eos, Trans. Amer. Geophys. Union, 84, 573580, doi:10.1029/2003EO510001.

    • Search Google Scholar
    • Export Citation
  • Goni, G., S. Kamholz, S. Garzoli, and D. Olson, 1996: Dynamics of the Brazil-Malvinas Confluence based on inverted echo sounders and altimetry. J. Geophys. Res., 101, 16 27316 289, doi:10.1029/96JC01146.

    • Search Google Scholar
    • Export Citation
  • Goni, G., and Coauthors, 2009: Applications of satellite-derived ocean measurements to tropical cyclone intensity forecasting. Oceanography, 22, 190197, doi:10.5670/oceanog.2009.78.

    • Search Google Scholar
    • Export Citation
  • Gould, J., and Coauthors, 2004: Argo profiling floats bring new era of in situ ocean observations. Eos, Trans. Amer. Geophys. Union, 85, 185191, doi:10.1029/2004EO190002.

    • Search Google Scholar
    • Export Citation
  • Guinehut, S., A.-L. Dhomps, G. Larnicol, and P.-Y. Le Traon, 2012: High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Sci., 8, 845857, doi:10.5194/os-8-845-2012.

    • Search Google Scholar
    • Export Citation
  • Hong, X. D., S. W. Chang, S. Raman, L. K. Shay, and R. Hodur, 2000: The interaction between Hurricane Opal (1995) and a warm core ring in the Gulf of Mexico. Mon. Wea. Rev., 128, 13471365, doi:10.1175/1520-0493(2000)128<1347:TIBHOA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huang, P., I. I. Lin, C. Chou, and R. H. Huang, 2015: Change in ocean subsurface environment to suppress tropical cyclone intensification under global warming. Nat. Commun., 6, 7188, doi:10.1038/ncomms8188.

    • Search Google Scholar
    • Export Citation
  • Jacob, S. D., L. K. Shay, A. J. Mariano, and P. G. Black, 2000: The 3D mixed layer response to Hurricane Gilbert. J. Phys. Oceanogr., 30, 14071429, doi:10.1175/1520-0485(2000)030<1407:TOMLRT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jaimes, B., and L. K. Shay, 2009: Mixed layer cooling in mesoscale oceanic eddies during hurricanes Katrina and Rita. Mon. Wea. Rev., 137, 41884207, doi:10.1175/2009MWR2849.1.

    • Search Google Scholar
    • Export Citation
  • Jaimes, B., L. K. Shay, and E. W. Uhlhorn, 2015: Enthalpy and momentum fluxes during Hurricane Earl relative to underlying ocean features. Mon. Wea. Rev., 143, 111131, doi:10.1175/MWR-D-13-00277.1.

    • Search Google Scholar
    • Export Citation
  • Kantha, L. H., and C. A. Clayson, 1994: An improved mixed layer model for geophysical applications. J. Geophys. Res., 99, 25 23525 266, doi:10.1029/94JC02257.

    • Search Google Scholar
    • Export Citation
  • Kara, A. B., P. A. Rochford, and H. E. Hurlburt, 2002: Naval Research Laboratory mixed layer depth (NMLD) climatologies. NRL Rep. NRL/FR/7330-02-9995, 26 pp.

  • Kara, A. B., P. A. Rochford, and H. E. Hurlburt, 2003: Mixed layer depth variability over the global ocean. J. Geophys. Res., 108, 3079, doi:10.1029/2000JC000736.

    • Search Google Scholar
    • Export Citation
  • Klemas, V., and X. H. Yan, 2014: Subsurface and deeper ocean remote sensing from satellites: An overview and new results. Prog. Oceanogr., 122, 19, doi:10.1016/j.pocean.2013.11.010.

    • Search Google Scholar
    • Export Citation
  • Ko, D. S., S. Y. Chao, C. C. Wu, and I. I. Lin, 2014: Impacts of Typhoon Megi (2010) on the South China Sea. J. Geophys. Res., 119, 44744489, doi:10.1002/2013JC009785.

    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., 2014: Increasing wind sinks heat. Nat. Climate Change, 4, 172173, doi:10.1038/nclimate2138.

  • Leipper, D. F., and D. Volgenau, 1972: Hurricane heat potential of the Gulf of Mexico. J. Phys. Oceanogr., 2, 218224, doi:10.1175/1520-0485(1972)002<0218:HHPOTG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., and Coauthors, 2013: An ocean cooling potential intensity index for tropical cyclones. Geophys. Res. Lett., 40, 18781882, doi:10.1002/grl.50091.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., and J. Chan, 2015: Recent decrease in typhoon destructive potential and global warming implications. Nat. Commun., 6, 7182, doi:10.1038/ncomms8182.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., C. C. Wu, K. A. Emanuel, I. H. Lee, C. R. Wu, and I. F. Pun, 2005: The interaction of Supertyphoon Maemi (2003) with a warm ocean eddy. Mon. Wea. Rev., 133, 26352649, doi:10.1175/MWR3005.1.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., C. C. Wu, I. F. Pun, and D. S. Ko, 2008: Upper-ocean thermal structure and the western North Pacific category-5 typhoons. Part I: Ocean features and the category-5 typhoons’ intensification. Mon. Wea. Rev., 136, 32883306, doi:10.1175/2008MWR2277.1.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., I. F. Pun, and C. C. Wu, 2009: Upper-ocean thermal structure and the western North Pacific category-5 typhoons. Part II: Dependence on translation speed. Mon. Wea. Rev., 137, 37443757, doi:10.1175/2009MWR2713.1.

    • Search Google Scholar
    • Export Citation
  • Lin, I. I., I. F. Pun, and C. C. Lien, 2014: “Category-6” supertyphoon Haiyan in global warming hiatus: Contribution from subsurface ocean warming. Geophys. Res. Lett., 41, 8547–8553, doi:10.1002/2014GL061281.

    • Search Google Scholar
    • Export Citation
  • Mainelli, M., M. DeMaria, L. K. Shay, and G. Goni, 2008: Application of oceanic heat content estimation to operational forecasting of recent Atlantic category 5 hurricanes. Wea. Forecasting, 23, 316, doi:10.1175/2007WAF2006111.1.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure-model for geophysical fluid problems. Rev. Geophys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation
  • Meyers, P. C., L. K. Shay, and J. K. Brewster, 2014: Development and analysis of the systematically merged Atlantic regional temperature and salinity climatology for oceanic heat content estimates. J. Atmos. Oceanic Technol., 31, 131149, doi:10.1175/JTECH-D-13-00100.1.

    • Search Google Scholar
    • Export Citation
  • Pascual, A., Y. Faugere, G. Larnicol, and P. Y. Le Traon, 2006: Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophys. Res. Lett., 33, L02611, doi:10.1029/2005GL024633.

    • Search Google Scholar
    • Export Citation
  • Pascual, A., C. Boone, G. Larnicol, and P. Y. Le Traon, 2009: On the quality of real-time altimeter gridded fields: Comparison with in situ data. J. Atmos. Oceanic Technol., 26, 556569, doi:10.1175/2008JTECHO556.1.

    • Search Google Scholar
    • Export Citation
  • Powell, M. D., P. J. Vickery, and T. A. Reinhold, 2003: Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422, 279283, doi:10.1038/nature01481.

    • Search Google Scholar
    • Export Citation
  • Price, J. F., 1981: Upper ocean response to a hurricane. J. Phys. Oceanogr., 11, 153175, doi:10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Price, J. F., 2009: Metrics of hurricane-ocean interaction: Vertically-integrated or vertically-averaged ocean temperature? Ocean Sci., 5, 351368, doi:10.5194/os-5-351-2009.

    • Search Google Scholar
    • Export Citation
  • Price, J. F., R. A. Weller, and R. Pinkel, 1986: Diurnal cycling: Observations and models of the upper ocean response to diurnal heating, cooling, and wind mixing. J. Geophys. Res., 91, 84118427, doi:10.1029/JC091iC07p08411.

    • Search Google Scholar
    • Export Citation
  • Pun, I. F., I. I. Lin, C. R. Wu, D. H. Ko, and W. T. Liu, 2007: Validation and application of altimetry-derived upper ocean thermal structure in the western North Pacific Ocean for typhoon-intensity forecast. IEEE Geosci. Remote Sens., 45, 16161630, doi:10.1109/TGRS.2007.895950.

    • Search Google Scholar
    • Export Citation
  • Pun, I. F., I. I. Lin, and M. H. Lo, 2013: Recent increase in high tropical cyclone heat potential area in the Western North Pacific Ocean. Geophys. Res. Lett., 40, 46804684, doi:10.1002/grl.50548.

    • Search Google Scholar
    • Export Citation
  • Pun, I. F., I. I. Lin, and D. S. Ko, 2014: New generation of satellite-derived ocean thermal structure for the Western North Pacific typhoon intensity forecasting. Prog. Oceanogr., 121, 109124, doi:10.1016/j.pocean.2013.10.004.

    • Search Google Scholar
    • Export Citation
  • Roemmich, D., S. Riser, R. Davis, and Y. Desaubies, 2004: Autonomous profiling floats: Workhorse for broad-scale ocean observations. Mar. Technol. Soc. J., 38, 2129, doi:10.4031/002533204787522802.

    • Search Google Scholar
    • Export Citation
  • Sanford, T. B., P. G. Black, J. R. Haustein, J. W. Feeney, G. Z. Forristall, and J. F. Price, 1987: Ocean response to a hurricane. Part I: Observations. J. Phys. Oceanogr., 17, 20652083, doi:10.1175/1520-0485(1987)017<2065:ORTAHP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schade, L. R., and K. A. Emanuel, 1999: The ocean’s effect on the intensity of tropical cyclones: Results from a simple coupled atmosphere–ocean model. J. Atmos. Sci., 56, 642651, doi:10.1175/1520-0469(1999)056<0642:TOSEOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., and E. W. Uhlhorn, 2008: Loop current response to Hurricanes Isidore and Lili. Mon. Wea. Rev., 136, 32483274, doi:10.1175/2007MWR2169.1.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., and J. K. Brewster, 2010: Oceanic heat content variability in the eastern Pacific Ocean for hurricane intensity forecasting. Mon. Wea. Rev., 138, 21102131, doi:10.1175/2010MWR3189.1.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., P. G. Black, A. J. Mariano, J. D. Hawkins, and R. L. Elsberry, 1992: Upper ocean response to Hurricane Gilbert. J. Geophys. Res., 97, 20 22720 248, doi:10.1029/92JC01586.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., G. J. Goni, and P. G. Black, 2000: Effects of a warm oceanic feature on Hurricane Opal. Mon. Wea. Rev., 128, 13661383, doi:10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shay, L. K., J. K. Brewster, and E. Maturi, 2012: Algorithm theoretical basis document for ocean heat content version 1.0. Satellite Products and Services Review Board, NOAA/RSMAS, 32 pp.

  • Walker, N. D., R. R. Leben, and S. Balasubramanian, 2005: Hurricane-forced upwelling and chlorophyll a enhancement within cold-core cyclones in the Gulf of Mexico. Geophys. Res. Lett., 32, L18610, doi:10.1029/2005GL023716.

    • Search Google Scholar
    • Export Citation
  • Walker, N. D., R. R. Leben, C. T. Pilley, M. Shannon, D. C. Herndon, I. F. Pun, I. I. Lin, and C. L. Gentemann, 2014: Slow translation speed causes rapid collapse of northeast Pacific Hurricane Kenneth over cold core eddy. Geophys. Res. Lett., 41, 75957601, doi:10.1002/2014GL061584.

    • Search Google Scholar
    • Export Citation
  • Willis, J. K., D. Roemmich, and B. Cornuelle, 2003: Combining altimetric height with broadscale profile data to estimate steric height, heat storage, subsurface temperature, and sea-surface temperature variability. J. Geophys. Res., 108, 3292, doi:10.1029/2002JC001755.

  • Wong, A., R. Keeley, and T. Carval, and the Argo Data Management Team, 2012: Argo quality control manual, version 2.7. Argo, 47 pp.

  • Wu, C. C., C. Y. Lee, and I. I. Lin, 2007: The effect of the ocean eddy on tropical cyclone intensity. J. Atmos. Sci., 64, 35623578, doi:10.1175/JAS4051.1.

    • Search Google Scholar
    • Export Citation
  • Wu, X., X. H. Yan, Y. H. Jo, and W. T. Liu, 2012: Estimation of subsurface temperature anomaly in the North Atlantic using a self-organizing map neural network. J. Atmos. Oceanic Technol., 29, 16751688, doi:10.1175/JTECH-D-12-00013.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 13 13 13
PDF Downloads 2 2 2

Satellite-Derived Ocean Thermal Structure for the North Atlantic Hurricane Season

View More View Less
  • 1 Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
  • | 2 Woods Hole Oceanographic Institution, Woods Hole, Massachusetts
Restricted access

Abstract

This paper describes a new model (method) called Satellite-derived North Atlantic Profiles (SNAP) that seeks to provide a high-resolution, near-real-time ocean thermal field to aid tropical cyclone (TC) forecasting. Using about 139 000 observed temperature profiles, a spatially dependent regression model is developed for the North Atlantic Ocean during hurricane season. A new step introduced in this work is that the daily mixed layer depth is derived from the output of a one-dimensional Price–Weller–Pinkel ocean mixed layer model with time-dependent surface forcing.

The accuracy of SNAP is assessed by comparison to 19 076 independent Argo profiles from the hurricane seasons of 2011 and 2013. The rms differences of the SNAP-estimated isotherm depths are found to be 10–25 m for upper thermocline isotherms (29°–19°C), 35–55 m for middle isotherms (18°–7°C), and 60–100 m for lower isotherms (6°–4°C). The primary error sources include uncertainty of sea surface height anomaly (SSHA), high-frequency fluctuations of isotherm depths, salinity effects, and the barotropic component of SSHA. These account for roughly 29%, 25%, 19%, and 10% of the estimation error, respectively. The rms differences of TC-related ocean parameters, upper-ocean heat content, and averaged temperature of the upper 100 m, are ~10 kJ cm−2 and ~0.8°C, respectively, over the North Atlantic basin. These errors are typical also of the open ocean underlying the majority of TC tracks. Errors are somewhat larger over regions of greatest mesoscale variability (i.e., the Gulf Stream and the Loop Current within the Gulf of Mexico).

Corresponding author address: Dr. Iam-Fei Pun, Dept. of Atmospheric Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 106, Taiwan. E-mail: faye@as.ntu.edu.tw

Abstract

This paper describes a new model (method) called Satellite-derived North Atlantic Profiles (SNAP) that seeks to provide a high-resolution, near-real-time ocean thermal field to aid tropical cyclone (TC) forecasting. Using about 139 000 observed temperature profiles, a spatially dependent regression model is developed for the North Atlantic Ocean during hurricane season. A new step introduced in this work is that the daily mixed layer depth is derived from the output of a one-dimensional Price–Weller–Pinkel ocean mixed layer model with time-dependent surface forcing.

The accuracy of SNAP is assessed by comparison to 19 076 independent Argo profiles from the hurricane seasons of 2011 and 2013. The rms differences of the SNAP-estimated isotherm depths are found to be 10–25 m for upper thermocline isotherms (29°–19°C), 35–55 m for middle isotherms (18°–7°C), and 60–100 m for lower isotherms (6°–4°C). The primary error sources include uncertainty of sea surface height anomaly (SSHA), high-frequency fluctuations of isotherm depths, salinity effects, and the barotropic component of SSHA. These account for roughly 29%, 25%, 19%, and 10% of the estimation error, respectively. The rms differences of TC-related ocean parameters, upper-ocean heat content, and averaged temperature of the upper 100 m, are ~10 kJ cm−2 and ~0.8°C, respectively, over the North Atlantic basin. These errors are typical also of the open ocean underlying the majority of TC tracks. Errors are somewhat larger over regions of greatest mesoscale variability (i.e., the Gulf Stream and the Loop Current within the Gulf of Mexico).

Corresponding author address: Dr. Iam-Fei Pun, Dept. of Atmospheric Sciences, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 106, Taiwan. E-mail: faye@as.ntu.edu.tw
Save