Comparison of TC Temperature and Water Vapor Climatologies between the Atlantic and Pacific Oceans from GPS RO Observations

S. Yang Joint Center of Data Assimilation for Research and Application, Nanjing University of Information Science and Technology, Nanjing, China

Search for other papers by S. Yang in
Current site
Google Scholar
PubMed
Close
,
X. Zou Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

Search for other papers by X. Zou in
Current site
Google Scholar
PubMed
Close
, and
P. S. Ray Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, Florida

Search for other papers by P. S. Ray in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Tropical cyclone (TC) temperature and water vapor structures are essential atmospheric variables. In this study, global positioning system (GPS) radio occultation (RO) observations from the GPS RO mission named the Constellation Observing System for Meteorology, Ionosphere, and Climate and the Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding on board both MetOp-A and MetOp-B satellites over the 9-yr period from 2007 to 2015 are used to generate a set of composite structures of temperature and water vapor fields within tropical depressions (TDs), tropical storms (TSs), and hurricanes (HUs) over the Atlantic Ocean and TDs, TSs, and typhoons (TYs) over the western Pacific Ocean. The composite TC structures are different over the two oceanic regions, reflecting different climatological environments. The warm cores for TCs over the western Pacific Ocean have higher altitudes and larger sizes than do those over the Atlantic Ocean for all storm categories. A radial variation of the warm-core temperature anomaly with descending altitude is seen, probably resulting from spiral cloud and rainband features. The large TC water vapor pressure anomalies, which are often more difficult to obtain than temperature anomalies, are located below the maximum warm-core temperature anomaly centers. Thus, the maximum values of the fractional water vapor pressure anomaly, defined as the anomaly divided by the environmental value, for TSs and HUs over the Atlantic Ocean (1.4% for TSs and 2.2% for HUs) are higher than those for TSs and TYs over the western Pacific Ocean (1.2% for TSs and 1.4% for TYs). These TC structures are obtained only after a quality control procedure is implemented, which consists of a range check that removes negative refractivity values and unrealistic temperature values, as well as a biweight check that removes data that deviate from the biweight mean by more than 3 times the biweight standard deviation. A limitation of the present study is an inability to resolve the TC inner-core structures because of a lack of sufficient RO profiles that collocate with TCs in their inner-core regions and the relatively coarse along-track resolutions of GPS RO data.

© 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: Dr. Xiaolei Zou, xzou1@umd.edu

Abstract

Tropical cyclone (TC) temperature and water vapor structures are essential atmospheric variables. In this study, global positioning system (GPS) radio occultation (RO) observations from the GPS RO mission named the Constellation Observing System for Meteorology, Ionosphere, and Climate and the Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding on board both MetOp-A and MetOp-B satellites over the 9-yr period from 2007 to 2015 are used to generate a set of composite structures of temperature and water vapor fields within tropical depressions (TDs), tropical storms (TSs), and hurricanes (HUs) over the Atlantic Ocean and TDs, TSs, and typhoons (TYs) over the western Pacific Ocean. The composite TC structures are different over the two oceanic regions, reflecting different climatological environments. The warm cores for TCs over the western Pacific Ocean have higher altitudes and larger sizes than do those over the Atlantic Ocean for all storm categories. A radial variation of the warm-core temperature anomaly with descending altitude is seen, probably resulting from spiral cloud and rainband features. The large TC water vapor pressure anomalies, which are often more difficult to obtain than temperature anomalies, are located below the maximum warm-core temperature anomaly centers. Thus, the maximum values of the fractional water vapor pressure anomaly, defined as the anomaly divided by the environmental value, for TSs and HUs over the Atlantic Ocean (1.4% for TSs and 2.2% for HUs) are higher than those for TSs and TYs over the western Pacific Ocean (1.2% for TSs and 1.4% for TYs). These TC structures are obtained only after a quality control procedure is implemented, which consists of a range check that removes negative refractivity values and unrealistic temperature values, as well as a biweight check that removes data that deviate from the biweight mean by more than 3 times the biweight standard deviation. A limitation of the present study is an inability to resolve the TC inner-core structures because of a lack of sufficient RO profiles that collocate with TCs in their inner-core regions and the relatively coarse along-track resolutions of GPS RO data.

© 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: Dr. Xiaolei Zou, xzou1@umd.edu
Save
  • Anthes, R. A., and Coauthors, 2008: The COSMIC/FORMOSAT-3 mission: Early results. Bull. Amer. Meteor. Soc., 89, 313334, https://doi.org/10.1175/BAMS-89-3-313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cangialosi, J., and J. Franklin, 2011: 2010 National Hurricane Center verification report. NOAA/National Hurricane Center Rep., 77 pp., http://origin.www.nhc.noaa.gov/verification/pdfs/Verification_2010.pdf.

  • Chavas, D. R., and K. A. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 37, L18816, https://doi.org/10.1029/2010GL044558.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsberry, R., 1995: Recent advancements in dynamical tropical cyclone track predictions. Meteor. Atmos. Phys., 56, 8199, https://doi.org/10.1007/BF01022522.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fujita, T., 1952: Pressure distribution within a typhoon. Geophys. Mag., 23, 437451.

  • Gall, R., J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer, 2013: The Hurricane Forecast Improvement Project. Bull. Amer. Meteor. Soc., 94, 329343, https://doi.org/10.1175/BAMS-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Halverson, J. B., J. Simpson, G. Heymsfield, H. Pierce, T. Hock, and L. Ritchie, 2006: Warm core structure of Hurricane Erin diagnosed from high altitude dropsondes during CAMEX-4. J. Atmos. Sci., 63, 309324, https://doi.org/10.1175/JAS3596.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Honda, T., and Coauthors, 2018: Assimilating all-sky Himawari-8 satellite infrared radiances: A case of Typhoon Soudelor (2015). Mon. Wea. Rev., 146, 213229, https://doi.org/10.1175/MWR-D-16-0357.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjic, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271285, https://doi.org/10.1007/s00703-001-0587-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjic, Z. I., J. P. Gerrity Jr., and S. Nickovic, 2001: An alternative approach to nonhydrostatic modeling. Mon. Wea. Rev., 129, 11641178, https://doi.org/10.1175/1520-0493(2001)129<1164:AAATNM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidder, S. Q., M. D. Goldberg, R. M. Zehr, M. DeMaria, J. F. W. Purdom, C. S. Velden, N. C. Grody, and S. J. Kusselson, 2000: Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bull. Amer. Meteor. Soc., 81, 12411260, https://doi.org/10.1175/1520-0477(2000)081<1241:SAOTCU>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455476, https://doi.org/10.1175/JCLI-D-13-00096.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nat. Geosci., 3, 157163, https://doi.org/10.1038/ngeo779.

  • Komaromi, W. A., and J. D. Doyle, 2017: Tropical cyclone outflow and warm core structure as revealed by HS3 dropsonde data. Mon. Wea. Rev., 145, 13391359, https://doi.org/10.1175/MWR-D-16-0172.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, Y.-H., T.-K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. 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
  • Kurihara, Y., M. A. Bender, R. E. Tuleya, and R. J. Ross, 1990: Prediction experiments of Hurricane Gloria (1985) using a multiply nested movable mesh model. Mon. Wea. Rev., 118, 21852198, https://doi.org/10.1175/1520-0493(1990)118<2185:PEOHGU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 20302045, https://doi.org/10.1175/1520-0493(1993)121<2030:AISOHM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kursinski, E., and Coauthors, 1996: Initial results of radio occultation observations of Earth’s atmosphere using the global positioning system. Science, 271, 11071110, https://doi.org/10.1126/science.271.5252.1107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lanzante, J. R., 1996: Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data. Int. J. Climatol., 16, 11971226, https://doi.org/10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., D.-L. Zhang, and M. Yau, 1997: A multiscale numerical study of Hurricane Andrew (1992). Part I: Explicit simulation and verification. Mon. Wea. Rev., 125, 30733093, https://doi.org/10.1175/1520-0493(1997)125<3073:AMNSOH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lord, S. J., 1991: A bogusing system for vortex circulations in the National Meteorological Center global forecast model. Preprints, 19th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 328–330.

  • Marks, F. D., Jr., and R. A. Houze Jr., 1987: Inner core structure of Hurricane Alicia from airborne Doppler radar observations. J. Atmos. Sci., 44, 12961317, https://doi.org/10.1175/1520-0469(1987)044<1296:ICSOHA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, K., and X. Zou, 2004: Toward developing an objective 4DVAR BDA scheme for hurricane initialization based on TPC observed parameters. Mon. Wea. Rev., 132, 20542069, https://doi.org/10.1175/1520-0493(2004)132<2054:TDAODB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, W., 2014: Radial-vertical profiles of tropical cyclone derived from dropsondes. M.S. thesis, Dept. of Earth, Ocean, and Atmospheric Science, Florida State University, 77 pp.

  • Rivoire, L., T. Birner, and J. A. Knaff, 2016: Evolution of the upper-level thermal structure in tropical cyclones. Geophys. Res. Lett., 43, 10 53010 537, https://doi.org/10.1002/2016GL070622.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schenkel, B. A., N. Lin, D. Chavas, M. Oppenheimer, and A. Brammer, 2017: Evaluating outer tropical cyclone size in reanalysis datasets using QuikSCAT data. J. Climate, 30, 87458762, https://doi.org/10.1175/JCLI-D-17-0122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, X., and X. Zou, 2018: Capturing size and intensity changes of Hurricanes Irma and Maria (2017) from polar-orbiting satellite microwave radiometers. J. Atmos. Sci., 75, 25092522, https://doi.org/10.1175/JAS-D-17-0315.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trinh, V. T., and T. N. Krishnamurti, 1992: Vortex initialization for typhoon track prediction. Meteor. Atmos. Phys., 47, 117126, https://doi.org/10.1007/BF01025612.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, Q., X. Zou, and B. Wang, 2000: Initialization and simulation of a landfalling hurricane using a variational bogus data assimilation scheme. Mon. Wea. Rev., 128, 22522269, https://doi.org/10.1175/1520-0493(2000)128<2252:IASOAL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., Y. Weng, J. A. Sippel, Z. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 21052125, https://doi.org/10.1175/2009MWR2645.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, F., M. Minamide, and E. E. Clothiaux, 2016: Potential impacts of assimilating all-sky infrared satellite radiances from GOES-R on convection-permitting analysis and prediction of tropical cyclones. Geophys. Res. Lett., 43, 29542963, https://doi.org/10.1002/2016GL068468.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J. A., R. F. Rogers, P. Reasor, E. Uhlhorn, and F. D. Marks Jr., 2013: Asymmetric hurricane boundary layer structure from dropsonde composites in relation to the environmental vertical wind shear. Mon. Wea. Rev., 141, 39683984, https://doi.org/10.1175/MWR-D-12-00335.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., and Q. Xiao, 2000: Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836860, https://doi.org/10.1175/1520-0469(2000)057<0836:SOTIAS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., and Z. Zeng, 2006: A quality control procedure for GPS radio occultation data. J. Geophys. Res., 111, D02112, https://doi.org/10.1029/2005JD005846.

    • Search Google Scholar
    • Export Citation
  • Zou, X., F. Weng, B. Zhang, L. Lin, Z. Qin, and V. Tallapragada, 2013: Impacts of assimilation of ATMS data in HWRF on track and intensity forecasts of 2012 four landfall hurricanes. J. Geophys. Res. Atmos., 118, 11 55811 576, https://doi.org/10.1002/2013JD020405.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zou, X., Z. Qin, and Y. Zheng, 2015: Improved tropical storm forecasts with GOES-13/15 imager radiance assimilation and asymmetric vortex initialization in HWRF. Mon. Wea. Rev., 143, 24852505, https://doi.org/10.1175/MWR-D-14-00223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 475 215 15
PDF Downloads 314 80 9