• Andersson, E., and Coauthors, 2005: Assimilation and modeling of the atmospheric hydrological cycle in the ECMWF forecasting system. Bull. Amer. Meteor. Soc., 86, 387402, doi:10.1175/BAMS-86-3-387.

    • Search Google Scholar
    • Export Citation
  • Archambault, H., , L. Bosart, , D. Keyser, , and J. Cordeira, 2013: A climatological analysis of the extratropical flow response to recurving western North Pacific tropical cyclones. Mon. Wea. Rev., 141, 23252346, doi:10.1175/MWR-D-12-00257.1.

    • Search Google Scholar
    • Export Citation
  • Back, L., , and C. Bretherton, 2006: Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett.,33, L17810, doi:10.1029/2006GL026672.

  • Berrisford, P., and Coauthors, 2011: Atmospheric conservation properties in ERA-Interim. Quart. J. Roy. Meteor. Soc., 137, 13811399, doi:10.1002/qj.864.

    • Search Google Scholar
    • Export Citation
  • Bloom, S., , L. Takacs, , A. da Silva, , and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 12561271, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., , J. Chen, , F. Robertson, , and R. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor., 47, 2279–2299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., , F. Robertson, , and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 57215739, doi:10.1175/2011JCLI4175.1.

    • Search Google Scholar
    • Export Citation
  • Chavas, D., , and K. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett.,37, L18816, doi:10.1029/2010GL044558.

  • Chiodo, G., , and L. Haimberger, 2010: Interannual changes in mass consistent energy budgets from ERA-Interim and satellite data. J. Geophys. Res.,115, D02112, doi:10.1029/2009JD012049.

  • Chu, J., , C. Sampson, , A. Levine, , and E. Fukada, 2002: The Joint Typhoon Warning Center tropical cyclone best tracks, 1945–2000. Naval Research Laboratory, Reference NRL/MR/7540-02-16. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/TC_bt_report.html.]

  • Dai, A., , J. Wang, , R. H. Ware, , and T. Van Hove, 2002: Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107, 4090, doi:10.1029/2001JD000642.

    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Draxler, R., 1999: HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, 46 pp.

  • Emanuel, K., 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., 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 11431155, doi:10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2008: The hurricane–climate connection. Bull. Amer. Meteor. Soc., 89, ES10–ES20, doi:10.1175/BAMS-89-5-Emanuel.

  • Frank, W., 1977: The structure and energetics of the tropical cyclone. I. Storm structure. Mon. Wea. Rev., 105, 11191135, doi:10.1175/1520-0493(1977)105<1119:TSAEOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frank, W., 1982: Large-scale characteristics of tropical cyclones. Mon. Wea. Rev., 110, 572586, doi:10.1175/1520-0493(1982)110<0572:LSCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frank, W., , and P. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea. Rev., 134, 23972417, doi:10.1175/MWR3204.1.

    • Search Google Scholar
    • Export Citation
  • Galarneau, T., , L. Bosart, , and R. Schumacher, 2010: Predecessor rain events ahead of tropical cyclones. Mon. Wea. Rev., 138, 32723297, doi:10.1175/2010MWR3243.1.

    • Search Google Scholar
    • Export Citation
  • Gill, A., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Grams, C., and Coauthors, 2011: The key role of diabatic processes in modifying the upper-tropospheric wave guide: A North Atlantic case-study. Quart. J. Roy. Meteor. Soc., 137, 21742193, doi:10.1002/qj.891.

    • Search Google Scholar
    • Export Citation
  • Grams, C., , S. Jones, , C. Davis, , P. Harr, , and M. Weissmann, 2013a: The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part I: Upper-level ridgebuilding and modification of the jet. Quart. J. Roy. Meteor. Soc., 139, 21482164, doi:10.1002/qj.2091.

    • Search Google Scholar
    • Export Citation
  • Grams, C., , S. Jones, , and C. Davis, 2013b: The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part II: Downstream evolution. Quart. J. Roy. Meteor. Soc., 139, 21652180, doi:10.1002/qj.2119.

    • Search Google Scholar
    • Export Citation
  • Guinn, T. A., , and W. H. Schubert, 1993: Hurricane spiral bands. J. Atmos. Sci., 50, 33803403, doi:10.1175/1520-0469(1993)050<3380:HSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Halverson, J., , 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, doi:10.1175/JAS3596.1.

    • Search Google Scholar
    • Export Citation
  • Harr, P., , and J. Dea, 2009: Downstream development associated with the extratropical transition of tropical cyclones over the western North Pacific. Mon. Wea. Rev., 137, 12951319, doi:10.1175/2008MWR2558.1.

    • Search Google Scholar
    • Export Citation
  • Harr, P., , D. Anwender, , and S. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Methodology and a case study of Typhoon Nabi (2005). Mon. Wea. Rev., 136, 32053225, doi:10.1175/2008MWR2248.1.

    • Search Google Scholar
    • Export Citation
  • Hart, R., 2011: An inverse relationship between aggregate Northern Hemisphere tropical cyclone activity and subsequent winter climate. Geophys. Res. Lett.,38, L01705, doi:10.1029/2010GL045612.

  • Hart, R., , R. Maue, , and M. Watson, 2007: Estimating local memory of tropical cyclones through MPI anomaly evolution. Mon. Wea. Rev., 135, 39904005, doi:10.1175/2007MWR2038.1.

    • Search Google Scholar
    • Export Citation
  • Hart, R., , R. Maue, , and M. Watson, 2008: How long does the climate record “remember” a tropical cyclone? Bull. Amer. Meteor. Soc., 89, 596598.

    • Search Google Scholar
    • Export Citation
  • Hatsushika, H., , J. Tsutsui, , M. Fiorino, , and K. Onogi, 2006: Impact of wind profile retrievals on the analysis of tropical cyclones in the JRA-25 reanalysis. J. Meteor. Soc. Japan, 84, 891905, doi:10.2151/jmsj.84.891.

    • Search Google Scholar
    • Export Citation
  • Hawkins, H., , and D. Rubsam, 1968: Hurricane Hilda, 1964. Mon. Wea. Rev., 96, 617636, doi:10.1175/1520-0493(1968)096<0617:HH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hawkins, H., , and S. Imbembo, 1976: The structure of a small, intense hurricane—Inez 1966. Mon. Wea. Rev., 104, 418442, doi:10.1175/1520-0493(1976)104<0418:TSOASI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G., 1986: Interannual variability of the Australian summer monsoon at Darwin: 1952–82. Mon. Wea. Rev., 114, 594604, doi:10.1175/1520-0493(1986)114<0594:IVOTAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G., 1995: Scale interaction in the western Pacific monsoon. Meteor. Atmos. Phys., 56, 5779, doi:10.1007/BF01022521.

  • Hoskins, B., , I. Draghici, , and H. Davies, 1978: A new look at the ω-equation. Quart. J. Roy. Meteor. Soc., 104, 3138, doi:10.1002/qj.49710443903.

    • Search Google Scholar
    • Export Citation
  • Houze, R., 2010: Clouds in tropical cyclones. Mon. Wea. Rev., 138, 293–344, doi:10.1175/2009MWR2989.1.

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

    • Search Google Scholar
    • Export Citation
  • Kiladis, G., , K. Straub, , and P. Haertel, 2005: Zonal and vertical structure of the Madden–Julian oscillation. J. Atmos. Sci., 62, 27902809, doi:10.1175/JAS3520.1.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G., , M. Wheeler, , P. Haertel, , K. Straub, , and P. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, doi:10.1029/2008RG000266.

    • Search Google Scholar
    • Export Citation
  • Kiranmayi, L., , and E. Maloney, 2011: Intraseasonal moist static energy budget in reanalysis data. J. Geophys. Res., 116, D21117, doi:10.1029/2011JD016031.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., , C. R. Sampson, , M. DeMaria, , T. P. Marchok, , J. M. Gross, , and C. J. McAdie, 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22, 781791, doi:10.1175/WAF1026.1.

    • 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, doi:10.1175/JCLI-D-13-00096.1.

    • Search Google Scholar
    • Export Citation
  • LaSeur, N., , and H. Hawkins, 1963: An analysis of Hurricane Cleo (1958) based on data from research reconnaissance aircraft. Mon. Wea. Rev., 91, 694709, doi:10.1175/1520-0493(1963)091<0694:AAOHCB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Manning, D., , and R. Hart, 2007: Evolution of North Atlantic ERA40 tropical cyclone representation. Geophys. Res. Lett.,34, L05705, doi:10.1029/2006GL028266.

  • McTaggart-Cowan, R., , L. F. Bosart, , J. R. Gyakum, , and E. H. Atallah, 2007: Hurricane Katrina (2005). Part II: Evolution and hemispheric impacts of a diabatically generated warm pool. Mon. Wea. Rev., 135, 39273949, doi:10.1175/2007MWR2096.1.

    • Search Google Scholar
    • Export Citation
  • Miloshevich, L. M., , H. Vömel, , A. Paukkunen, , A. J. Heymsfield, , and S. J. Oltmans, 2001: Characterization and correction of relative humidity measurements from Vaisala RS80-A radiosondes at cold temperatures. J. Atmos. Oceanic Technol., 18, 135156, doi:10.1175/1520-0426(2001)018<0135:CACORH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nieto Ferreira, R., , and W. Schubert, 1997: Barotropic aspects of ITCZ breakdown. J. Atmos. Sci., 54, 261285, doi:10.1175/1520-0469(1997)054<0261:BAOIB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Onogi, K., and Coauthors, 2007: The JRA-25 reanalysis. J. Meteor. Soc. Japan, 85, 369432, doi:10.2151/jmsj.85.369.

  • Palmén, E., , and C. Newton, 1969: Atmospheric Circulation Systems: Their Structure and Physical Interpretation, Academic Press, 603 pp.

  • Riemer, M., , S. Jones, , and C. Davis, 2008: The impact of extratropical transition on the downstream flow: An idealized modeling study with a straight jet. Quart. J. Roy. Meteor. Soc., 134, 6991, doi:10.1002/qj.189.

    • Search Google Scholar
    • Export Citation
  • Roundy, P., , and W. Frank, 2004: A climatology of waves in the equatorial region. J. Atmos. Sci., 61, 21052132, doi:10.1175/1520-0469(2004)061<2105:ACOWIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Schenkel, B., , and R. Hart, 2011: Potential implications of tropical cyclone passage. Bull. Amer. Meteor. Soc., 91, 12821283.

  • Schenkel, B., , and R. Hart, 2012: An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets. J. Climate, 25, 34533475, doi:10.1175/2011JCLI4208.1.

    • Search Google Scholar
    • Export Citation
  • Schreck, C., , J. Molinari, , and K. Mohr, 2011: Attributing tropical cyclogenesis to equatorial waves in the western North Pacific. J. Atmos. Sci., 68, 195209, doi:10.1175/2010JAS3396.1.

    • Search Google Scholar
    • Export Citation
  • Schreck, C., , J. Molinari, , and A. Aiyyer, 2012: A global view of equatorial waves and tropical cyclogenesis. Mon. Wea. Rev., 140, 774788, doi:10.1175/MWR-D-11-00110.1.

    • Search Google Scholar
    • Export Citation
  • Seager, R., , and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 78767901, doi:10.1175/JCLI-D-13-00018.1.

    • Search Google Scholar
    • Export Citation
  • Seager, R., and Coauthors, 2007: Model projections of an imminent transition to a more arid climate in southwestern North America. Science, 316, 11811184, doi:10.1126/science.1139601.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and S. Camargo, 2005: Influence of western North Pacific tropical cyclones on their large-scale environment. J. Atmos. Sci., 62, 33963407, doi:10.1175/JAS3539.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , S. Wang, , and D. Kim, 2014: Moist static energy budget of the MJO during DYNAMO. J. Atmos. Sci., 71, 42764291, doi:10.1175/JAS-D-14-0052.1.

    • Search Google Scholar
    • Export Citation
  • Stern, D., , and D. Nolan, 2009: Reexamining the vertical structure of tangential winds in tropical cyclones: Observations and theory. J. Atmos. Sci., 66, 35793600, doi:10.1175/2009JAS2916.1.

    • Search Google Scholar
    • Export Citation
  • Stohl, A., , C. Forster, , and H. Sodemann, 2008: Remote sources of water vapor forming precipitation on the Norwegian west coast at 60°N—A tale of hurricanes and an atmospheric river. J. Geophys. Res.,113, D05102, doi:10.1029/2007JD009006.

  • Thorne, P., , and R. Vose, 2010: Reanalyses suitable for characterizing long-term trends: Are they really achievable? Bull. Amer. Meteor. Soc., 91, 353361, doi:10.1175/2009BAMS2858.1.

    • Search Google Scholar
    • Export Citation
  • Tomas, R., , and P. Webster, 1997: The role of inertial instability in determining the location and strength of near-equatorial convection. Quart. J. Roy. Meteor. Soc., 123, 14451482, doi:10.1002/qj.49712354202.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K., , J. Fasullo, , and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, doi:10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , and C. D. Thorncroft, 2013: The role of convectively coupled atmospheric Kelvin waves on African easterly wave activity. Mon. Wea. Rev., 141, 19101924, doi:10.1175/MWR-D-12-00147.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and P. E. Roundy, 2011: The Madden–Julian oscillation’s influence on African easterly waves and downstream tropical cyclogenesis. Mon. Wea. Rev., 139, 27042722, doi:10.1175/MWR-D-10-05028.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and M. Janiga, 2012a: Atlantic tropical cyclogenesis: A three-way interaction between an African easterly wave, diurnally varying convection, and a convectively coupled atmospheric Kelvin wave. Mon. Wea. Rev., 140, 11081124, doi:10.1175/MWR-D-11-00122.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and C. J. Schreck III, 2012b: Impacts of convectively coupled Kelvin waves on environmental conditions for Atlantic tropical cyclogenesis. Mon. Wea. Rev., 140, 21982214, doi:10.1175/MWR-D-11-00305.1.

    • Search Google Scholar
    • Export Citation
  • Vincent, D., , and R. Waterman, 1979: Large-scale atmospheric conditions during the intensification of Hurricane Carmen (1974). I. Temperature, moisture and kinematics. Mon. Wea. Rev., 107, 283294, doi:10.1175/1520-0493(1979)107<0283:LSACDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vincent, D., , and A. Fink, 2001: Tropical cyclone environments over the northeastern and northwestern Pacific based on ERA-15 analyses. Mon. Wea. Rev., 129, 19281948, doi:10.1175/1520-0493(2001)129<1928:TCEOTN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, J., , H. L. Cole, , D. J. Carlson, , E. R. Miller, , K. Beierle, , A. Paukkunen, , and T. K. Laine, 2002: Corrections of humidity measurement errors from the Vaisala RS80 radiosonde—Application to TOGA COARE data. J. Atmos. Oceanic Technol., 19, 9811002, doi:10.1175/1520-0426(2002)019<0981:COHMEF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., 1972: Response of the tropical atmosphere to local, steady forcing. Mon. Wea. Rev., 100, 518541, doi:10.1175/1520-0493(1972)100<0518:ROTTAT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., , and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, doi:10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wimmers, A., , and C. Velden, 2011: Seamless advective blending of total precipitable water retrievals from polar-orbiting satellites. J. Appl. Meteor. Climatol., 50, 10241036, doi:10.1175/2010JAMC2589.1.

    • Search Google Scholar
    • Export Citation
  • Wood, K., , and E. Ritchie, 2014: A 32-year reanalysis intercomparison of tropical cyclone structure in the eastern North Pacific and North Atlantic. 31st Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 53. [Available online at https://ams.confex.com/ams/31Hurr/webprogram/Paper244195.html.]

  • Yanai, M., , S. Esbensen, , and J. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, doi:10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden–Julian oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.

  • View in gallery

    Plan view of precipitable water (mm; contoured) and precipitable water anomalies (mm; shaded) for tropical storms and typhoons, respectively, (a),(b) 3 days before TC passage at the domain center, (c),(d) during TC passage at the domain center, and (e),(f) 3 days after TC passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the region over which area averages are computed for the NWR, SWR, and ER, respectively. Precipitable water anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

  • View in gallery

    Vertical cross section of specific humidity (g kg−1; contoured) and specific humidity anomalies (g kg−1; shaded) in the NWR and ER for (a) tropical storms and (b) typhoons along A to A′ in Figs. 1c and 1d, respectively, and in the SWR for (c) tropical storms and (d) typhoons along B to B′ in Figs. 1c and 1d, respectively. Specific humidity anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval. The gray shading denotes regions where data are below the surface.

  • View in gallery

    Time–longitude plot of precipitable water anomalies (mm; shaded) for (a) tropical storms and (b) typhoons meridionally averaged between 250 km to the south and 250 km to the north of A and A′ in Figs. 1c and 1d, respectively. Anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

  • View in gallery

    Time–longitude plot of precipitable water anomalies (mm; shaded), MJO band-filter anomalies (mm; contoured in black every 0.5 mm), and ERW band-filter anomalies (mm; contoured in blue every 0.5 mm) for (a) tropical storms and (b) typhoons meridionally averaged between 250 km to the south and 250 km to the north of B and B′ in Figs. 1c and 1d, respectively. Anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

  • View in gallery

    Twelve backward 120-h parcel trajectories beginning at (a) 650 hPa in the NWR, (b) 700 hPa in the SWR, and (c) 650 hPa in the ER calculated from data during typhoon passage at the domain center. Parcel trajectories are colored according to parcel pressures (hPa). Each large white dot denotes the initial position of the trajectory at the time of typhoon passage at the domain center while the smaller white dots indicate the position of the parcel 60 h prior. The parcel trajectories are overlaid on precipitable water (mm; shaded) from 120 h prior to typhoon passage at the domain center. The black boxes in (a)–(c) denote the location of the NWR, SWR, and ER, respectively.

  • View in gallery

    As in Fig. 5, but for parcel trajectories computed from climatological data.

  • View in gallery

    Time series of mean parcel trajectory pressure (hPa) in the (a) NWR, (b) SWR, and (c) ER for typhoons computed from TC trajectories and climatological trajectories. The shading represents the standard error of the mean.

  • View in gallery

    As in Fig. 7, but for specific humidity (g kg−1).

  • View in gallery

    Plan view of 1000–750-hPa layer-averaged climatological (contoured) and anomalous (shaded) (a) divergence (10−6 s−1), (b) meridional wind (m s−1), (c) climatological (black contoured) and raw (shaded) absolute vorticity (10−4 s−1), and (d) climatological (black contoured) and raw (shaded) mean sea level pressure (hPa) during typhoon passage at the domain center. The red line denotes the zero raw absolute vorticity line (i.e., dynamical equator), the blue line denotes the zero climatological absolute vorticity line, and the green line denotes the location of the equator in (c) and (d). The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively. The gray shading denotes grid points that are below the surface.

  • View in gallery

    Plan view of (a) horizontal moisture convergence, (b) horizontal moisture advection, (c) surface evaporation, and (d) precipitation (mm day−1; contoured) and their anomalies (mm day−1; shaded) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

  • View in gallery

    Plan view of (a) zonal moisture convergence, (b) meridional moisture convergence, (c) zonal moisture advection, and (d) meridional moisture advection (mm day−1; contoured) and their anomalies (mm day−1; shaded) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

  • View in gallery

    Plan view of (a) convergence of climatological mean moisture by the anomalous meridional wind, (b) advection of climatological mean moisture by the anomalous meridional wind, and (c) advection of anomalous moisture by the anomalous meridional wind (mm day−1) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

  • View in gallery

    Time series of area-averaged vertically integrated moisture budget tendency terms (mm day−1) in the (a) NWR, (b) SWR, and (c) ER during typhoon passage at the domain center.

  • View in gallery

    Time series of area-averaged horizontal moisture convergence, horizontal moisture advection, and their respective meridional and zonal components (mm day−1) in the (a) NWR, (b) SWR, and (c) ER during typhoon passage at the domain center.

  • View in gallery

    Storm-relative azimuthally averaged radius–height cross section of relative humidity (%) for WPAC TCs (a) equatorward of 36°N during 1982–2009 computed from CFSR data and (b) during 1961–70 computed from rawinsonde and flight data taken from Fig. 5 of Frank (1977). The gray shading in (a) denotes regions where data are below the surface.

  • View in gallery

    Schematic depicting salient lower-tropospheric wind vectors (black arrows), moisture anomalies (shaded), how moisture anomalies are generated (colored arrows), and phenomena responsible for generating moisture anomalies during TC passage based upon the results of the present study. The gray and red lines denote the location of the equator and zero absolute vorticity line (i.e., dynamical equator), respectively. The sizes of the colored and black arrows are approximately proportional to the strength of the winds or the strength of the moistening and drying tendencies.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 21 21 6
PDF Downloads 15 15 4

An Analysis of the Environmental Moisture Impacts of Western North Pacific Tropical Cyclones

View More View Less
  • 1 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York, Albany, New York
  • 2 Department of Earth, Ocean, and Atmospheric Science, The Florida State University, Tallahassee, Florida
© Get Permissions
Full access

Abstract

The present study examines the environmental moisture anomalies present during western North Pacific tropical cyclone (TC) passage using storm-relative composites. Composited precipitable water anomalies reveal asymmetric anomalies with dry anomalies to the northwest and southwest of the TC and moist anomalies to the east of the TC. Precipitable water anomalies filtered in space and time suggest that the moisture anomalies in the northwest, southwest, and east regions (NWR, SWR, and ER, respectively) are partially due to the TC, while the anomalies in the SWR are also forced by a convectively suppressed Madden–Julian oscillation (MJO) and equatorial Rossby wave (ERW). Composited vertically integrated moisture budgets and backward parcel trajectories reveal that the moisture anomalies in the NWR, SWR, and ER are primarily due to the convergence of climatological mean moisture by the anomalous meridional wind. This convergence is induced by the secondary circulation of the TC in the NWR and ER and by inertial instability induced by the TC, MJO, and ERW in the SWR and ER as also suggested by prior work. Dry anomalies in the NWR are also forced by the advection of moisture by lower-tropospheric northerly wind anomalies associated with the primary circulation of the TC. Together with prior work, these results suggest that TCs can have significant impacts on their large-scale atmospheric environment extending well beyond the spatiotemporal scales of the lower-tropospheric cyclonic circulation of the TC.

Corresponding author address: Benjamin A. Schenkel, Department of Atmospheric and Environmental Sciences, DAES-ES351, University at Albany, State University of New York, Albany, NY 12222. E-mail: benschenkel@gmail.com

Abstract

The present study examines the environmental moisture anomalies present during western North Pacific tropical cyclone (TC) passage using storm-relative composites. Composited precipitable water anomalies reveal asymmetric anomalies with dry anomalies to the northwest and southwest of the TC and moist anomalies to the east of the TC. Precipitable water anomalies filtered in space and time suggest that the moisture anomalies in the northwest, southwest, and east regions (NWR, SWR, and ER, respectively) are partially due to the TC, while the anomalies in the SWR are also forced by a convectively suppressed Madden–Julian oscillation (MJO) and equatorial Rossby wave (ERW). Composited vertically integrated moisture budgets and backward parcel trajectories reveal that the moisture anomalies in the NWR, SWR, and ER are primarily due to the convergence of climatological mean moisture by the anomalous meridional wind. This convergence is induced by the secondary circulation of the TC in the NWR and ER and by inertial instability induced by the TC, MJO, and ERW in the SWR and ER as also suggested by prior work. Dry anomalies in the NWR are also forced by the advection of moisture by lower-tropospheric northerly wind anomalies associated with the primary circulation of the TC. Together with prior work, these results suggest that TCs can have significant impacts on their large-scale atmospheric environment extending well beyond the spatiotemporal scales of the lower-tropospheric cyclonic circulation of the TC.

Corresponding author address: Benjamin A. Schenkel, Department of Atmospheric and Environmental Sciences, DAES-ES351, University at Albany, State University of New York, Albany, NY 12222. E-mail: benschenkel@gmail.com

1. Introduction

While the impacts of tropical cyclones (TCs) on their extratropical atmospheric environment have been increasingly examined (e.g., extratropical Rossby wave dispersion; Jones et al. 2003; Harr et al. 2008; Riemer et al. 2008; Harr and Dea 2009; Grams et al. 2011; Hart 2011; Archambault et al. 2013; Grams et al. 2013a,b), the potential impacts of TCs on their tropical atmospheric environment are not well known. Prior work has suggested that TCs may cool and dry their tropical environment for weeks following TC passage for the region within approximately 250 km of the TC track (Hart et al. 2007, 2008; Schenkel and Hart 2011) and potentially on spatial scales extending across entire ocean basins (Sobel and Camargo 2005). However, these studies did not objectively define the mean spatiotemporal scales of the environmental moisture anomalies due to a single TC or how these moisture anomalies are generated. Accordingly, the present study objectively examines the anomalous response of the tropical environmental moisture during western North Pacific (WPAC) TC passage.

Prior work has suggested that TCs simultaneously and asymmetrically dry and moisten their tropical environment (Vincent and Waterman 1979; Frank 1982; Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997; Vincent and Fink 2001; Sobel and Camargo 2005; Hart et al. 2007, 2008; Schenkel and Hart 2011) although these studies disagree about the location of the moisture anomalies relative to the TC. It is also important to note that none of these previous studies has investigated the four-dimensional structure of environmental moisture anomalies for a large number of cases. The dry and moist anomalies in the tropical environment identified in prior work may be attributed to four factors. First, dry anomalies may be caused by a reduction in surface latent heat fluxes due to the sea surface temperature (SST) cold wake induced by the TC (Sobel and Camargo 2005; Hart et al. 2007, 2008; Schenkel and Hart 2011). Second, the secondary circulation of the TC may yield environmental drying due to both the descent outside the moist TC core (e.g., Palmén and Newton 1969; Frank 1977, 1982; Emanuel 1986, 1988; Houze 2010) and the anomalously high precipitation efficiency of TCs relative to isolated tropical convection (Emanuel 2008). Third, the warm-core structure of the TC may also yield asymmetric dry and moist anomalies due to quasigeostrophic (QG) adjustment mechanisms (e.g., vertical differential in cyclonic vorticity advection; Hart et al. 2007). Last, the primary circulation of the TC may yield dry anomalies to the west and moist anomalies to the east of the TC due to horizontal moisture advection (Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997; McTaggart-Cowan et al. 2007; Stohl et al. 2008). A summary of the salient results from prior work is found in Table 1 although no such table can be totally complete in the interest of brevity.

Table 1.

Summary of the salient findings from prior work to be examined in the present study.

Table 1.

Given the uncertainty in both the location of the environmental moisture anomalies relative to the TC and the processes responsible for their generation, the present study addresses these questions using a storm-relative composite analysis of several hundred WPAC TCs. Specifically, four-dimensional storm-relative composites of moisture anomalies are used to determine the location and spatiotemporal scales of the dry and moist anomalies in the environment surrounding the TC for comparison with prior work. Both parcel trajectories and vertically integrated moisture budgets computed from the storm-relative composites are used to determine which, if any, of the four aforementioned processes are responsible for generating environmental moisture anomalies. The implications of the results from the present study include, but are not limited to, identification of regions of enhanced and suppressed rainfall outside of the TC convective core and the impact of existing TCs on subsequent TC genesis events due to changes in large-scale environmental moisture.

The remainder of the manuscript is divided into four parts. Section 2 describes the data and methods. Section 3 examines the storm-relative composites of moisture, trajectories computed from the storm-relative composites, and composited vertically integrated moisture budgets. Section 4 discusses the caveats associated with using reanalyses to study TC moisture budgets and the implications of these caveats for the present study. Section 5 summarizes the results and discusses future research.

2. Data and methods

a. Data

The response of environmental moisture during TC passage is studied using coupled atmosphere–ocean reanalysis data. The primary focus of the present analysis is on the environment outside of the convective TC core (i.e., mean size of ~400 km; Frank 1977). All 6-h Joint Typhoon Warning Center (JTWC) best-track (Chu et al. 2002) WPAC TCs that have occurred over the ocean at or equatorward of 20°N from 1982 through 2009 are chosen for analysis. A latitude threshold of 20°N is used to focus on the impacts of TCs upon their tropical environment and to minimize the inclusion of dry subtropical and midlatitude air mass intrusions in the tropics. WPAC TCs are chosen for examination because these TCs are, on average, the largest TCs (Knaff et al. 2007; Chavas and Emanuel 2010; Knaff et al. 2014), possibly suggesting that WPAC TCs have the strongest and broadest impact on their environment. The large size of WPAC TCs may also suggest that WPAC TCs and their environmental impacts are better resolved within reanalyses (Schenkel and Hart 2012). In the remainder of the present study, for brevity WPAC TCs will be called TCs.

In the present study, the atmosphere is represented by the 0.5° × 0.5° grid spacing 6-h National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; Saha et al. 2010). The CFSR is chosen given its relatively strong depiction of TC intensity (Schenkel and Hart 2012), TC track (Schenkel and Hart 2012), and TC structure (Wood and Ritchie 2014). Moreover, the CFSR is currently the only coupled atmosphere–ocean reanalysis (Saha et al. 2010) potentially allowing for a more realistic representation of the feedbacks between the atmosphere and ocean.

b. Methods

The present study uses storm-relative composites of CFSR data to examine the evolution of environmental moisture before, during, and after TC passage. At each reanalysis grid point, a 6-h 28-yr (1982–2009) climatological mean is computed for each variable. Next, the 6-h climatological mean for each grid point is filtered in time using a daily 1–2–3–2–1 filter to reduce the contribution of phenomena with time scales of less than a week (Holland 1986). As an example, the climatological mean at 0000 UTC 1 January would be calculated using data at 0000 UTC 30 December, 0000 UTC 31 December, 0000 UTC 1 January, 0000 UTC 2 January, and 0000 UTC 3 January. Anomalies are computed by subtracting the 6-h climatological mean from an instantaneous variable.

Next, composite TC-centered grids for each 6-h best-track TC data point are constructed for each variable following the methodology of Hart et al. (2007). Specifically, TC-centered grids for each TC are fixed in space upon each best-track TC data point and constructed at 6-h intervals for 15 days prior through 15 days after TC passage, yielding a total of 121 grids for each TC. The composites are computed by averaging the TC-centered grids over all best-track TC data points for each of the 6-h intervals for 15 days prior through 15 days after TC passage.

The composite grid consists of 8000 km in the zonal direction, 7800 km in the meridional direction, and 48 vertical levels. Consistent with prior work (Schenkel and Hart 2011), the composite grid has a uniform horizontal grid spacing of 42 km, which is chosen to retain those features that are resolved in the subtropics and tropics within the reanalysis. The vertical grid spacing of the composite grid is 25 hPa from the surface to 50 hPa with a finer vertical grid spacing used from 50 hPa to the model top (1 hPa).

The composites are separated into two groups to examine whether the magnitude of the environmental moisture anomalies is sensitive to best-track TC intensity: tropical storms (64 kt > maximum 10-m wind speed ≥ 34 kt; N = 589 uniquely named TCs) and typhoons (maximum 10-m wind speed ≥64 kt; N = 355 uniquely named TCs). For a conservative statistical approach, each TC is only counted once toward the sample size of each TC intensity bin regardless of how many times a given TC is included in the composite because of the interdependence of the large-scale environment for consecutive 6-h best-track TC data points. Table 2 contains the mean location, translation speed, intensity, date of occurrence, and number of TCs in each TC intensity bin.

Table 2.

Mean best-track TC latitude (°N), TC longitude (°E), TC translation speed, maximum 10-m TC wind speed, Julian day of TC occurrence, number of uniquely named TCs, and number of 6-h best-track TC data points for tropical storms and typhoons in the present study. The value to the right of the mean is the standard error of the mean.

Table 2.

In the present study, a 10 000-sample bootstrap approach is used to determine whether the composited anomalies are statistically significantly different from zero, consistent with prior work (e.g., Roundy and Frank 2004; Frank and Roundy 2006; Ventrice et al. 2011, 2012a,b; Ventrice and Thorncroft 2013). Specifically, a new distribution of raw anomalies is constructed for a given variable at each grid point by randomly selecting raw anomalies (with replacement) for times in which a best-track TC is present. A mean is then calculated for the new distribution of raw anomalies with the process being repeated until there are 10 000 means. The distribution of 10 000 means is then used to compute the 95% confidence interval of the mean to determine whether the anomalies are statistically significantly different from zero. In the remainder of the present study, the use of the word “significant” indicates that the anomalies are statistically significantly different from climatology at the 95% confidence interval.

To identify how moisture anomalies are generated, vertically integrated moisture budgets are calculated from the storm-relative composites:
e1
where ps is the surface pressure, pt is the model top (1 hPa), g is the acceleration due to gravity, ρw is the density of water, q is the specific humidity, v is the horizontal velocity vector, E is the surface evaporation, P is the precipitation, qs is the 2-m specific humidity, vs is the 10-m horizontal velocity vector, and R is the budget residual. The term on the left-hand side of the budget equation is the Eulerian time tendency of vertically integrated specific humidity. The five terms on the right-hand side of the equation from left to right are horizontal moisture advection, horizontal moisture convergence, the surface evaporation term, the precipitation term, the boundary term, and the budget residual. Similar to prior work (e.g., Seager et al. 2007), the boundary term is not presented since it is over an order of magnitude smaller than the other moisture budget terms. Also consistent with prior work (e.g., Yanai et al. 1973), the precipitation term appears as −P, which is more physically relevant for the moisture anomalies in the present study. Precipitation is output by the microphysical and convective parameterizations (Saha et al. 2010), while surface evaporation is computed using a bulk flux formula (Trenberth et al. 2011). The remaining four terms are computed from the composited variables.
Horizontal moisture convergence and advection terms are also separated into their climatological mean and deviation from the climatological mean:
e2
e3
where u is the zonal velocity and υ is the meridional velocity. The overbar quantities represent the aforementioned composited 6-h climatological mean of a given variable, while the primed quantities represent the composited 6-h deviation from the climatological mean of a variable. The separation of the variables into their climatological mean and deviation from the climatological mean determines how interactions between the TC and its environment force moisture anomalies. In the present study, the terms containing products of means are ignored because they primarily describe time scales that are much longer than the environmental moisture anomalies induced by the TC (e.g., Sobel and Camargo 2005).

To isolate the impacts of TCs from the MJO and convectively coupled equatorial waves, zonal space–time spectral filtering of precipitable water anomalies is used following the methodology of Wheeler and Kiladis (1999). A filter band similar to Wheeler and Kiladis (1999) is used for the MJO; Kiladis et al. (2009) is used for Kelvin waves, equatorial Rossby waves (ERWs), and mixed Rossby–gravity (MRG) waves; and Frank and Roundy (2006) is used for tropical depression (TD)-type waves. Storm-relative composites are then constructed from the filtered precipitable water anomalies to identify which wave types are responsible for the composited precipitable water anomalies.

Last, parcel trajectories are used to examine environmental moisture during TC passage to provide further confirmation of the results from the moisture budgets. Backward 120-h parcel trajectories are calculated using version 4.0 of the National Oceanographic and Atmospheric Administration (NOAA)/Air Resources Laboratory (ARL) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler 1999) from storm-relative composite data. In spite of the relatively coarse temporal and spatial resolution of the storm-relative composites, prior work has revealed that 6-h data and 0.5° data are of sufficiently high resolution to examine parcel trajectories in the large-scale environment of the TC (e.g., Galarneau et al. 2010). The trajectories calculated during TC passage are compared with trajectories computed from climatological data to see how the trajectories differ. The agreement in the conclusions between the moisture budgets and trajectories provides greater confidence in these results while also acknowledging the inherent uncertainties in both analyses.

3. Results

Sections 3a and 3b discuss the spatiotemporal structure and phenomena responsible for the composited environmental moisture before, during, and after TC passage. Sections 3c and 3d utilize both parcel trajectories and plan view plots of vertically integrated moisture budgets to determine how moisture anomalies are generated. Section 3e uses time series of vertically integrated moisture budgets to examine the time scales of the forcing for the moisture anomalies.

a. Horizontal and vertical structure of environmental moisture anomalies

The evolution of the composited environmental moisture during TC passage is depicted by plan view plots of composited precipitable water and precipitable water anomalies 3 days before TC passage (Fig. 1a for tropical storms and Fig. 1b for typhoons), during the TC (Fig. 1c for tropical storms and Fig. 1d for typhoons), and 3 days after TC passage (Fig. 1e for tropical storms and Fig. 1f for typhoons). In the present study, any reference to TC passage refers to TC passage at the domain center unless explicitly mentioned otherwise. Three regions of significant moisture anomalies characterize the evolution of environmental moisture before, during, and after TC passage for both tropical storms and typhoons. Dry anomalies to the southwest of the TC approximately encompass one quarter of the composite domain before, during, and after TC passage and are similar to prior work (Vincent and Fink 2001; Sobel and Camargo 2005). These dry anomalies to the southwest of the TC are referred to as the southwest region (SWR) in the present study. Moist anomalies extending from the TC to approximately 3500 km to the east of the TC before, during, and after TC passage are similar to anomalies identified in prior work (Frank 1982; Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997; Vincent and Fink 2001; Sobel and Camargo 2005) and are referred to as the east region (ER) in the present study. Last, dry anomalies to the north of the TC prior to TC passage that rotate counterclockwise with respect to the TC comprise the third region, which is referred to as the northwest region (NWR) in the present study. The dry anomalies in the NWR have also been identified in prior work (Vincent and Waterman 1979; Frank 1982; Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997; Vincent and Fink 2001; Sobel and Camargo 2005). These dry anomalies in the NWR eventually rotate far enough southward after TC passage and merge with the SWR, creating a basin-scale region of dry anomalies. Similar to prior work (Hart et al. 2007, 2008), the precipitable water anomalies are significantly stronger in magnitude in the NWR, SWR, and ER for typhoons relative to tropical storms. In addition to being sensitive to TC intensity, the composite environmental moisture anomalies also exhibit substantial sensitivity to other factors such as time of year and TC location (not shown).

Fig. 1.
Fig. 1.

Plan view of precipitable water (mm; contoured) and precipitable water anomalies (mm; shaded) for tropical storms and typhoons, respectively, (a),(b) 3 days before TC passage at the domain center, (c),(d) during TC passage at the domain center, and (e),(f) 3 days after TC passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the region over which area averages are computed for the NWR, SWR, and ER, respectively. Precipitable water anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Given that the TC is embedded within the northern flank of the intertropical convergence zone (ITCZ) as denoted by the maximum in precipitable water (Fig. 1), the dry anomalies in the NWR and SWR anomalously strengthen the meridional precipitable water gradient between the ITCZ and its environment to the west of the TC. Similarly, the moist anomalies in the ER anomalously weaken the meridional precipitable water gradient near the core of the ITCZ to the east of the TC. It is also important to note that the mean latitude of the TC in both sets of composites (14.0°N for tropical storms and 15.5°N for typhoons; Table 2) suggests that the dry anomalies in the SWR occur, on average, in the Southern Hemisphere (SH). Interestingly, the location of the dry anomalies across the equator from the TC is analogous to the response to asymmetric heating across the equator (e.g., Webster 1972; Gill 1980; Holland 1995).

In addition to discussing the horizontal structure of moisture anomalies, it is also important to discuss their vertical structure. Figures 2a and 2b depict vertical cross sections of specific humidity and specific humidity anomalies from A to A′ in Figs. 1c and 1d through the NWR and ER for tropical storms and typhoons, respectively. Similarly, Figs. 2c and 2d also depict specific humidity and specific humidity anomalies from B to B′ in Figs. 1c and 1d through the SWR for tropical storms and typhoons, respectively. Significant specific humidity anomalies in the NWR, SWR, and ER extend throughout the lower and midtroposphere with maximum anomalies between 750 and 600 hPa for both tropical storms and typhoons. Only specific humidity anomalies in the NWR and ER are significantly stronger for typhoons than tropical storms. The following section examines whether the moisture anomalies are forced by the TC given the location and time scales of the moisture anomalies during TC passage.

Fig. 2.
Fig. 2.

Vertical cross section of specific humidity (g kg−1; contoured) and specific humidity anomalies (g kg−1; shaded) in the NWR and ER for (a) tropical storms and (b) typhoons along A to A′ in Figs. 1c and 1d, respectively, and in the SWR for (c) tropical storms and (d) typhoons along B to B′ in Figs. 1c and 1d, respectively. Specific humidity anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval. The gray shading denotes regions where data are below the surface.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

b. Time scales and propagation of environmental moisture anomalies

All three regions of significant precipitable water anomalies propagate westward with the TC before, during, and after TC passage as seen in Fig. 1. Figures 3a and 3b contain time–longitude plots of precipitable water anomalies from A to A′ in Figs. 1c and 1d for tropical storms and typhoons, respectively, to more clearly depict the westward propagation of precipitable water anomalies through the NWR and ER. Close examination of Fig. 3 reveals that the moist anomalies in the ER and dry anomalies in the NWR propagate with the TC at approximately 5–6 m s−1 (Table 2). The propagation of the moisture anomalies in the NWR and ER in close proximity with the TC combined with the peak magnitude of moisture anomalies during TC passage suggests that the TC is responsible for forcing the moisture anomalies. It is also important to note that the time scales of the dry anomalies in the NWR are consistent with prior work (Sobel and Camargo 2005), which also argued that the dry anomalies are partially due to the TC. The relatively long time scales of the dry anomalies in the NWR and moist anomalies in the ER compared to the mean 5.6-day lifespan of WPAC TCs (Schenkel and Hart 2012) may also suggest that the moisture anomalies are partially forced by the precursor TC disturbance or other large-scale phenomena (e.g., monsoon trough, convectively coupled equatorial waves, and MJO).

Fig. 3.
Fig. 3.

Time–longitude plot of precipitable water anomalies (mm; shaded) for (a) tropical storms and (b) typhoons meridionally averaged between 250 km to the south and 250 km to the north of A and A′ in Figs. 1c and 1d, respectively. Anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Similar to Fig. 3, Fig. 4 contains time–longitude plots of precipitable water anomalies from B to B′ in Fig. 1, revealing the propagation of dry anomalies through the SWR for tropical storms and typhoons. Figure 4 reveals significant dry anomalies with relatively larger spatiotemporal scales than the NWR, ER, or the TC. Significant dry anomalies occur in the SWR approximately 2 weeks prior to TC passage and gradually spread eastward at approximately 6–7 m s−1 until these anomalies span a majority of the southern half of the domain at TC passage. The spatiotemporal scales of the eastward propagating dry anomalies in the SWR are comparable to the MJO (e.g., Zhang 2005). The time scales and eastward propagation of the dry anomalies in the SWR are also consistent with prior WPAC TC composites (Sobel and Camargo 2005), which suggests that the dry anomalies in the SWR are partially due to the TC. The source of the eastward propagating precipitable water anomalies can be partially identified using zonal space–time spectral filtering of the precipitable water anomalies for convectively coupled equatorial waves and the MJO (black contours in Fig. 4). The eastward propagating dry anomalies in the SWR significantly project onto the convectively suppressed MJO band filter, which is not surprising given the presence of a significant MJO event during 10%–40% of all WPAC TC genesis events (Frank and Roundy 2006; Schreck et al. 2011, 2012). However, the MJO band filtered anomalies only account for approximately 10%–30% of the dry precipitable water anomalies.

Fig. 4.
Fig. 4.

Time–longitude plot of precipitable water anomalies (mm; shaded), MJO band-filter anomalies (mm; contoured in black every 0.5 mm), and ERW band-filter anomalies (mm; contoured in blue every 0.5 mm) for (a) tropical storms and (b) typhoons meridionally averaged between 250 km to the south and 250 km to the north of B and B′ in Figs. 1c and 1d, respectively. Anomalies are only shown if they are statistically significantly different from zero at the 95% confidence interval.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Embedded within the eastward propagating dry precipitable water anomalies is a relatively smaller region of westward propagating dry precipitable water anomalies. These precipitable water anomalies begin at the zonal center of the domain approximately 10 days after the initiation of eastward propagating dry anomalies and move westward at approximately 4–5 m s−1, which is comparable to the TC translation speed (Table 2). Zonal space–time spectral filtering of the westward propagating anomalies reveals that the westward propagating dry precipitable water anomalies significantly project onto the convectively suppressed ERW band filter (blue contours in Fig. 4). Such a result is not surprising given that significant ERWs are present during 25%–55% of all WPAC TC genesis events (Frank and Roundy 2006; Schreck et al. 2011, 2012) and that equatorial wave activity is stronger during MJO events (Schreck et al. 2012). Similar to the eastward propagating precipitable water anomalies, the ER-wave band filter anomalies only account for approximately 10%–20% of the dry precipitable water anomalies. The large fraction of dry anomalies, on average, left unexplained by filtering for the MJO and convectively coupled equatorial waves (~20%–50%), together with the propagation speed and timing of the peak magnitude of dry anomalies, suggests that SWR dry anomalies are partially forced by the TCs. Parcel trajectories and moisture budgets are analyzed in the following section to determine whether the moisture anomalies in the NWR, SWR, and ER are forced by the TC or other phenomena.

c. Parcel trajectory analysis

The examination of the source of the environmental moisture anomalies begins with a parcel trajectory analysis during typhoon passage. In the interest of brevity, the remainder of the present study focuses on typhoons although the results are qualitatively similar for tropical storms. Figure 5 contains backward 120-h parcel trajectories colorized according to parcel pressure originating from the height of the strongest moisture anomaly during TC passage in the NWR (Fig. 5a), SWR (Fig. 5b), and ER (Fig. 5c). The parcel trajectories are overlaid upon precipitable water from 120 h prior to TC passage to determine the moisture content of the environment from which the TC originates. Similar plots are also provided for parcel trajectories computed from climatological data for parcels originating from the NWR (Fig. 6a), SWR (Fig. 6b), and ER (Fig. 6c). The trajectories computed in the environment surrounding the TC during TC passage are known as TC trajectories, while trajectories computed from climatological data are known as climatological trajectories. The mean parcel trajectory pressure is provided for the TC trajectories and climatological trajectories in the NWR (Fig. 7a), SWR (Fig. 7b), and ER (Fig. 7c), while mean parcel trajectory specific humidity is also provided in the NWR (Fig. 8a), SWR (Fig. 8b), and ER (Fig. 8c).

Fig. 5.
Fig. 5.

Twelve backward 120-h parcel trajectories beginning at (a) 650 hPa in the NWR, (b) 700 hPa in the SWR, and (c) 650 hPa in the ER calculated from data during typhoon passage at the domain center. Parcel trajectories are colored according to parcel pressures (hPa). Each large white dot denotes the initial position of the trajectory at the time of typhoon passage at the domain center while the smaller white dots indicate the position of the parcel 60 h prior. The parcel trajectories are overlaid on precipitable water (mm; shaded) from 120 h prior to typhoon passage at the domain center. The black boxes in (a)–(c) denote the location of the NWR, SWR, and ER, respectively.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for parcel trajectories computed from climatological data.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Fig. 7.
Fig. 7.

Time series of mean parcel trajectory pressure (hPa) in the (a) NWR, (b) SWR, and (c) ER for typhoons computed from TC trajectories and climatological trajectories. The shading represents the standard error of the mean.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for specific humidity (g kg−1).

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

An analysis of the TC trajectories originating from the NWR and SWR suggests that the dry anomalies are primarily forced by anomalous descent. Specifically, TC trajectories in the NWR and SWR originate from 70 to 80 hPa lesser pressure (Figs. 7a,b) compared to climatology. Figures 8a and 8b reveal that the origination of the NWR and SWR parcels from higher in the troposphere during TC passage yields parcels that are significantly drier than climatology. Figure 9a contains plan view plots of layer-averaged lower-tropospheric divergence and divergence anomalies during TC passage. Parcels in the NWR and SWR traverse through regions of anomalous lower-tropospheric divergence and associated descent anomalies that are generally collocated with the strongest dry anomalies. The collocation of the lower- and midtropospheric descent anomalies with the dry anomalies in the NWR suggests that the dry anomalies are forced by anomalous descent that is likely induced by the TC. Similarly, the presence of the lower- and midtropospheric descent anomalies in the SWR during the weeks prior to TC passage together with the increase in descent anomalies during TC passage suggests that the dry anomalies are forced by descent anomalies induced by a combination of the TC, the MJO, and ERWs. Parcel trajectories also suggest that the dry anomalies in the NWR during TC passage are partially due to the origination of TC trajectories from a relatively drier source region (Fig. 5a) compared to climatology (Fig. 6a). Plan view plots of layer-averaged lower- and midtropospheric meridional wind and meridional wind anomalies in Fig. 9b suggests that the lower-tropospheric northerly wind anomalies of the TC are partially responsible for the origination of parcels from drier source regions.

Fig. 9.
Fig. 9.

Plan view of 1000–750-hPa layer-averaged climatological (contoured) and anomalous (shaded) (a) divergence (10−6 s−1), (b) meridional wind (m s−1), (c) climatological (black contoured) and raw (shaded) absolute vorticity (10−4 s−1), and (d) climatological (black contoured) and raw (shaded) mean sea level pressure (hPa) during typhoon passage at the domain center. The red line denotes the zero raw absolute vorticity line (i.e., dynamical equator), the blue line denotes the zero climatological absolute vorticity line, and the green line denotes the location of the equator in (c) and (d). The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively. The gray shading denotes grid points that are below the surface.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Conversely, TC trajectories (Fig. 5c) suggest that parcels in the ER originate from a relatively moister source region compared to climatology (Fig. 6c) due partially to anomalous lower- and midtropospheric ascent and lower-tropospheric northward advection. Specifically, the lower-tropospheric southerly winds of the primary circulation of the TC are responsible for the anomalous lower-tropospheric northward advection. Further insight into the anomalous lower-tropospheric descent and anomalous meridional advection identified in the parcel trajectory analysis is provided by examining the composited vertically integrated moisture budget analysis in the following section.

d. Horizontal structure of moisture budget during TC passage

The analysis of the vertically integrated moisture budgets begins with plan view plots of the salient four budget terms (black contours) and their anomalies (shaded) during TC passage: horizontal moisture advection (Fig. 10a), horizontal moisture convergence (Fig. 10b), surface evaporation (Fig. 10c), and precipitation (Fig. 10d). Both surface evaporation and precipitation have anomalous moisture tendencies that oppose the moisture anomalies (i.e., anomalous moistening tendencies in regions with dry anomalies) in the NWR, SWR, and ER. The absence of substantial forcing of moisture anomalies by surface evaporation contradicts the arguments of prior work (Sobel and Camargo 2005; Hart et al. 2007, 2008; Schenkel and Hart 2011). Of the remaining two forcing terms, horizontal moisture convergence is the strongest term in the NWR, SWR, and ER, while horizontal moisture advection also plays a relatively small role in generating dry anomalies in the NWR.

Fig. 10.
Fig. 10.

Plan view of (a) horizontal moisture convergence, (b) horizontal moisture advection, (c) surface evaporation, and (d) precipitation (mm day−1; contoured) and their anomalies (mm day−1; shaded) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Plan view plots of the zonal and meridional components of horizontal moisture convergence and advection during TC passage (Fig. 11) provide further insight into the structure of the moistening tendencies. The zonal and meridional components of horizontal moisture convergence and advection have a quadrupole structure with large cancellation between the respective zonal and meridional components. The opposing quadrupole structure in zonal and meridional moisture convergence is expected given the dipole structure of the lower-tropospheric zonal and meridional wind field for a cyclonic vortex. Close examination reveals that meridional moisture convergence (Fig. 11b) primarily comprises horizontal moisture convergence in the NWR, SWR, and ER. Similarly, meridional moisture advection (Fig. 11d) primarily comprises horizontal moisture advection in the NWR.

Fig. 11.
Fig. 11.

Plan view of (a) zonal moisture convergence, (b) meridional moisture convergence, (c) zonal moisture advection, and (d) meridional moisture advection (mm day−1; contoured) and their anomalies (mm day−1; shaded) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Insight into the phenomena responsible for forcing meridional moisture convergence and advection is provided by decomposing the variables within these terms into the climatological mean and deviation from the climatological mean with the three strongest moisture tendency terms represented in Fig. 12. The meridional moisture convergence term in the NWR, SWR, and ER is primarily composed of convergence of climatological mean moisture by the anomalous lower-tropospheric meridional divergence (Fig. 12a; and the associated lower- and midtropospheric descent) forced by the anomalous lower-tropospheric meridional wind (Fig. 9b) likely partially induced by the TC. The existence of broad-scale lower- and midtropospheric descent to the west of the TC is consistent with the location identified in prior work (Frank 1977, 1982). Similarly, the anomalous lower- and midtropospheric ascent to the east of the TC is consistent with the broad-scale ascent in the wake of the WPAC TCs identified in prior work (Vincent and Waterman 1979; Frank 1982).

While the asymmetries in the lower- and midtropospheric vertical motion (Fig. 9a) in the region immediately surrounding the TC are in agreement with prior work, the reasoning for their existence remains unexplained. Upon first glance, the vertical motion may be accounted for using QG dynamics. However, the computation of Q vectors (Hoskins et al. 1978) from the composites (not shown) does not yield a coherent vertical motion pattern in the NWR and ER, potentially suggesting that the vertical motion anomalies cannot be explained using QG dynamics as hypothesized by Hart et al. (2007). However, the grid resolution of the CFSR may be sufficiently high to emphasize smaller-scale forcing while masking the larger-scale forcing by QG dynamics, making it impossible to completely rule out QG dynamics as a source of the moisture anomalies. In the potential absence of substantial QG forcing by the TC, one plausible explanation is that the anomalous lower-tropospheric descent in the NWR is induced by the secondary circulation of the TC. Further research is necessary to determine why the secondary circulation of the TC is asymmetric.

Additional investigation reveals that the lower-tropospheric convergence in the ER and the lower-tropospheric divergence in the SWR are likely caused by inertial instability induced by the TC as well as the previously identified convectively suppressed MJO and ERW. Potential confirmation of the role of inertial instability in forcing the moisture anomalies is obtained by examining plan view plots of climatological and raw layer-averaged lower-tropospheric absolute vorticity in Fig. 9c and climatological and raw mean sea level pressure in Fig. 9d. An anomalous intrusion of negative absolute vorticity to the north of the composite equator (green line) occurs to the south and east of the TC associated with a region of inertial instability. The anomalous inertial instability is caused by enhanced cross-equatorial absolute vorticity advection that results from an enhanced cross-equatorial pressure gradient caused by the low pressure of the TC and, to a lesser extent, the anomalously high pressure of the convectively suppressed MJO and ERW at the western edge of the equator (Fig. 9d), consistent with Tomas and Webster (1997). The anomalous inertial instability induces lower-tropospheric meridional divergence and convergence to the south and north of the dynamic equator (red line), respectively, because of changes in the relative strength of the pressure gradient force and the Coriolis force acting on parcels as they move northward (Tomas and Webster 1997). Furthermore, composites of typhoons stratified by latitude (not shown) reveal that the dry anomalies in the SWR and moist anomalies in the ER are significantly stronger and broader for low-latitude TCs. Consistent with Tomas and Webster (1997), the stronger and broader moisture anomalies for low-latitude TCs are likely due to the relatively stronger anomalous meridional pressure gradient that results from the low pressure anomalies of the TC being located closer to the equator. It is also important to mention that the convectively suppressed phase of the MJO and ERW likely induce their own lower- and midtropospheric subsidence in the SWR independent of the aforementioned inertial instability arguments. In fact, the composites presented here resemble composites from prior work of WPAC TC genesis events that occur during significant ERWs or MJOs with significant dry and moist anomalies to the southwest and northwest of the TC, respectively (Frank and Roundy 2006).

With regard to meridional moisture advection anomalies, drying tendencies in the NWR are partially due to advection of both climatological and anomalous moisture by the anomalous meridional wind (Figs. 12b,c). In the NWR, the anomalous meridional wind is dominated by lower-tropospheric northerly wind anomalies associated with the lower-tropospheric primary circulation of the TC (Fig. 9b). Both the moisture anomalies, which are largely dominated by the TC, and climatological mean moisture (Fig. 1d) decrease to the north in the NWR such that the northerly wind anomalies induce anomalous drying.

Fig. 12.
Fig. 12.

Plan view of (a) convergence of climatological mean moisture by the anomalous meridional wind, (b) advection of climatological mean moisture by the anomalous meridional wind, and (c) advection of anomalous moisture by the anomalous meridional wind (mm day−1) during typhoon passage at the domain center. The blue boxes to the northwest, southwest, and east of the TC represent the regions over which area averages are computed for the NWR, SWR, and ER, respectively.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

The drying tendencies in the NWR are partially due to meridional moisture advection, in agreement with prior work (Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997; McTaggart-Cowan et al. 2007; Stohl et al. 2008), while the absence of substantial moistening tendencies in the ER contradicts both these previous studies and the parcel trajectory analysis. Although moistening tendencies occur because of meridional moisture advection in the ER (Fig. 12b,c), the drying tendencies due to zonal advection of the TC by the easterly steering flow (Fig. 11c) are dominant, thus explaining the discrepancy between budgets and trajectories. The moistening tendencies from the present study likely disagree with prior work because of those previous studies focusing either on case studies of TCs recurving into the midlatitudes that are inconsistent with our case selection (McTaggart-Cowan et al. 2007; Stohl et al. 2008) or idealized simulations that may not capture the complexity of the interaction between the large-scale environment and the TC in the WPAC (Guinn and Schubert 1993; Nieto Ferreira and Schubert 1997). The results presented here suggest that the moisture anomalies during TC passage are likely generated by the TC in the NWR, SWR, and ER with potential contributions in the ER and SWR from an MJO and ERW. The temporal evolution of the vertically integrated moisture budget terms in the NWR, SWR, and ER are examined next.

e. Temporal evolution of moisture budget

To examine the temporal evolution of the vertically integrated moisture budget, each term is areally averaged within the NWR (Fig. 13a), SWR (Fig. 13b), and ER (Fig. 13c) for 15 days prior to TC passage to 15 days after TC passage at 6-h intervals. Similar to the time of TC passage, the moisture budget in the NWR, SWR, and ER in the days prior to TC passage is generally dominated by horizontal moisture convergence with the NWR exhibiting a small contribution from horizontal moisture advection. These moisture budget tendencies quickly strengthen as the TC moves toward these regions and weaken as the TC moves away, suggesting that the TC is responsible for forcing the moisture anomalies. In contrast, drying tendencies in the SWR gradually increase for nearly a week prior to TC passage and gradually return to climatology.

Fig. 13.
Fig. 13.

Time series of area-averaged vertically integrated moisture budget tendency terms (mm day−1) in the (a) NWR, (b) SWR, and (c) ER during typhoon passage at the domain center.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Similar to the prior section, the time series of horizontal moisture convergence and advection can be separated into their zonal and meridional components in the NWR (Fig. 14a), SWR (Fig. 14b), and ER (Fig. 14c). Consistent with the plan view plots discussed earlier (Fig. 11), drying tendencies due to horizontal moisture convergence and advection are generally dominated by meridional moisture convergence and advection prior to TC passage. Similar to the plan view plots of the moisture budget terms (Fig. 12), meridional moisture convergence in the days prior to TC passage largely comprises the convergence of climatological mean moisture by the anomalous meridional wind (not shown), while meridional moisture advection comprises advection of both anomalous and climatological mean moisture by the anomalous meridional wind (not shown). Given the component terms and the short time scales of the moisture budget tendency terms, these results suggest that the moisture anomalies in the NWR and ER are forced by the TC. In contrast, the components of the moisture budget tendency terms and their relatively long time scales suggest that the dry anomalies in the SWR are forced by a combination of the TC, MJO, and ERW. These results are suggestive of the importance of the interactions between the TC and its large-scale environment in forcing significant moisture anomalies. The next section discusses whether the fidelity of the results presented here is substantially impacted by deficiencies in the representation of moisture and TCs within reanalyses.

Fig. 14.
Fig. 14.

Time series of area-averaged horizontal moisture convergence, horizontal moisture advection, and their respective meridional and zonal components (mm day−1) in the (a) NWR, (b) SWR, and (c) ER during typhoon passage at the domain center.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

4. The use of reanalyses for studying the environmental moisture impacts of TCs

a. Deficiencies

The present section discusses the caveats associated with using reanalyses in the present study, provides justification for the use of reanalyses, and discusses the implications of these caveats upon the results of the present study. Substantial uncertainty is potentially introduced into the present study due to two factors: deficiencies in 1) the reanalysis representation of moisture and 2) the reanalysis representation of TCs. Prior work has identified deficiencies in the reanalysis moisture representation that include nonconservation of moisture (Berrisford et al. 2011) resulting from excessive reanalysis precipitation relative to evaporation (Andersson et al. 2005; Trenberth et al. 2011), the assimilation of new data every 6 h (Bloom et al. 1996; Saha et al. 2010), and spurious trends in reanalysis precipitation, surface fluxes, and precipitable water (Onogi et al. 2007; Bosilovich et al. 2008; Chiodo and Haimberger 2010; Berrisford et al. 2011; Bosilovich et al. 2011; Trenberth et al. 2011). Reanalysis moisture budgets are also subject to substantial uncertainties due to moisture budget terms not being calculated on the native model grid (Seager and Henderson 2013). An additional source of uncertainty occurs as a result of the magnitude of the observation error for moisture being comparable to the magnitude of the moisture anomalies in the present study (i.e., ~1 mm for precipitable water; Wentz and Spencer 1998; Miloshevich et al. 2001; Dai et al. 2002; Wang et al. 2002; Wimmers and Velden 2011). Deficiencies in reanalysis TC representation also represent another major source of uncertainty in the present study because of errors in reanalysis TC position (Schenkel and Hart 2012), weaker than observed reanalysis TC intensity (Hatsushika et al. 2006; Manning and Hart 2007; Onogi et al. 2007; Schenkel and Hart 2012), biased representation of the life cycle of TC intensity (Schenkel and Hart 2012), weaker than observed TC structure (Wood and Ritchie 2014), nonphysical reanalysis TC structure in a small fraction of TCs (Schenkel and Hart 2012; Wood and Ritchie 2014), and spurious trends in reanalysis TC intensity and structure (Manning and Hart 2007).

b. Justification

In spite of these deficiencies in the representation of reanalysis moisture, there are several reasons why reanalyses are suitable for use in the present study. First, the flux divergence of moisture within reanalyses is generally consistent with observations (Trenberth et al. 2011). Moreover, a reduction in reanalysis precipitation to observed values (Onogi et al. 2007; Bosilovich et al. 2008, 2011; Trenberth et al. 2011) would likely yield larger moisture anomalies in the present study given that precipitation restores the anomalies back to climatology. Additional confidence in the use of reanalyses is provided by their use in prior work to study moist processes including the 1993 Mississippi valley flood (Seager and Henderson 2013), the large-scale Pacific tropical circulation (Back and Bretherton 2006), the MJO (Kiladis et al. 2005; Kiranmayi and Maloney 2011; Sobel et al. 2014), and Northern Hemisphere (NH) TCs (Sobel and Camargo 2005; Hart et al. 2007, 2008; Schenkel and Hart 2011). The magnitude of the moisture anomalies in some of these prior studies (e.g., Kiladis et al. 2005; Sobel and Camargo 2005; Kiranmayi and Maloney 2011; Schenkel and Hart 2011) is comparable to or smaller than the magnitude of the moisture anomalies in the present study. A comparison between both moisture anomalies and moisture budgets for composites and case studies of TCs in the present study (not shown) computed from the CFSR and Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-I; Dee et al. 2011) yields the same conclusions, providing confidence that these results are robust among reanalyses. Moreover, both the parcel trajectory analysis and moisture budget analysis presented here also yield similar conclusions. Last, the budget residual for both composites and case studies would not change the primary conclusions from the present study even if the residual is associated with a single term.

In addition to the reasons why reanalyses can be used to examine moisture, there are four reasons why reanalyses are suitable for use in the present study despite the deficiencies in reanalysis TC representation. First, reanalyses are able to simulate the gross structure of TCs (e.g., lower-tropospheric wind maximum and warm-core structure; Wood and Ritchie 2014) despite the coarse horizontal resolution of reanalyses. Second, the present study is dependent on resolving the environmental moisture impacts of TCs, which are resolvable given their large-scale (i.e., horizontal scales greater than 1000 km), weak gradients compared to the strong, small-scale gradients in the TC inner core (i.e., horizontal scales less than 100 km; LaSeur and Hawkins 1963; Hawkins and Rubsam 1968; Hawkins and Imbembo 1976; Halverson et al. 2006; Stern and Nolan 2009). Third, the large-scale environment of particularly poorly represented TCs is eventually returned by data assimilation to a state that is consistent with observations (Saha et al. 2010; Thorne and Vose 2010). Last, and most importantly for the present study, a comparison between storm-relative composites of reanalysis relative humidity for WPAC TCs (Fig. 15a) with observation derived composites from prior work (Fig. 15b; Frank 1977) reveals that the two are comparable outside the convective core of the TC. Specifically, the reanalysis is generally drier below 800 hPa and moister above 800 hPa compared to observations with maximum differences of approximately 10% in the environment outside the TC convective core. In their totality, the arguments presented here suggest that the environmental moisture impacts of TCs are, if anything, likely underestimated in the present study because of 1) muted representation of reanalysis TCs and their environmental impacts and 2) larger than observed moisture tendencies associated with excessive reanalysis precipitation. While reanalyses introduce uncertainty into the present study, both prior work and the additional work presented here provide reasonable confidence that reanalyses are able to represent the mean environmental moisture impacts of TCs.

Fig. 15.
Fig. 15.

Storm-relative azimuthally averaged radius–height cross section of relative humidity (%) for WPAC TCs (a) equatorward of 36°N during 1982–2009 computed from CFSR data and (b) during 1961–70 computed from rawinsonde and flight data taken from Fig. 5 of Frank (1977). The gray shading in (a) denotes regions where data are below the surface.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

5. Summary and conclusions

The present study represents one attempt at quantifying the spatiotemporal scales of environmental moisture anomalies during WPAC TC passage and attributing these anomalies to physical processes using vertically integrated moisture budgets and parcel trajectories. The salient results from the present study are summarized in a schematic (Fig. 16). Table 3 also summarizes whether prior work is confirmed, contested, or unanswered by the present study. The primary conclusion in the present study is that TCs both significantly moisten and dry their environment on spatiotemporal scales extending well beyond the cyclonic circulation of the TC resulting from meridional moisture convergence and, to a lesser extent, meridional moisture advection. Consistent with prior work, significant environmental moisture anomalies are concentrated into three regions during TC passage: a TC-scale region of dry anomalies to the northwest of the TC (NWR), a basin-scale region of dry anomalies to the southwest of the TC (SWR), and synoptic-scale region of moist anomalies to the east of the TC (ER). Vertical cross sections of the NWR, SWR, and ER reveal significant moisture anomalies extending throughout the lower and midtroposphere with maximum anomalies between 750 and 600 hPa.

Fig. 16.
Fig. 16.

Schematic depicting salient lower-tropospheric wind vectors (black arrows), moisture anomalies (shaded), how moisture anomalies are generated (colored arrows), and phenomena responsible for generating moisture anomalies during TC passage based upon the results of the present study. The gray and red lines denote the location of the equator and zero absolute vorticity line (i.e., dynamical equator), respectively. The sizes of the colored and black arrows are approximately proportional to the strength of the winds or the strength of the moistening and drying tendencies.

Citation: Journal of Climate 28, 7; 10.1175/JCLI-D-14-00213.1

Table 3.

Summary of whether salient results from prior work are confirmed, contested, or unanswered by the present study.

Table 3.

All three regions exhibit significant moisture anomalies that occur well before and after TC passage, suggesting that phenomena other than the TC may be responsible for forcing these moisture anomalies. The moisture anomalies in the NWR and ER propagate westward at the same propagation speed as the TC and peak in magnitude during TC passage, which suggests that the TC forces these moisture anomalies. In contrast, the dry anomalies in the SWR comprise a region of westward propagating dry anomalies that significantly project onto the ERW band filter embedded within a broad region of eastward propagating dry anomalies that significantly project onto the MJO band filter. However, the dry anomalies in the SWR appear to be also partially forced by the TC as a result of peak dry anomalies occurring during TC passage, the westward propagation of the dry anomalies at a similar speed to the TC, and MJO and ERW filtered anomalies only comprising, on average, approximately 20%–50% of the dry anomalies.

Both parcel trajectory and moisture budget analyses are employed to isolate how the TC and environmental phenomena are responsible for generating moisture anomalies. However, the trajectory and budget analyses are subject to uncertainty, which should be considered when interpreting the results. Parcel trajectories during TC passage exhibit anomalous descent in the NWR and SWR and anomalous ascent in the ER during TC passage. Parcel trajectories also suggest that parcels in the NWR originate from a relatively drier region compared to climatology. The moisture budgets generally confirm the results of the trajectory analysis revealing that the moisture anomalies in the NWR, SWR, and ER are primarily forced by horizontal moisture convergence in the form of meridional convergence of climatological mean moisture by the anomalous meridional wind. Horizontal moisture advection in the form of meridional advection of climatological and anomalous moisture by the anomalous meridional wind also makes a small contribution to the drying tendencies in the NWR. In the absence of coherent vertical motion anomalies due to QG dynamics, the asymmetries in vertical motion and moisture anomalies in the NWR appear to be forced by the secondary circulation of the TC. Subsidence in the SWR and ascent in the ER occur because of inertial instability induced by the TC and, to lesser extent, a convectively suppressed MJO and ERW. The dry anomalies in the SWR may also be forced by the lower- and midtropospheric descent associated with the convectively suppressed MJO and ERW.

Together with prior work (Sobel and Camargo 2005; Hart et al. 2007, 2008; Schenkel and Hart 2011), the results of the present study suggest that TCs can have significant impacts on their environment that extend well beyond the spatiotemporal scales of the cyclonic circulation of the TC. While the research presented here provides insight into the impacts of TCs upon their environmental moisture, there are several lingering questions that remain unresolved by this research. Specifically, why do asymmetries occur in the secondary circulation of the TC? What fraction of the dry anomalies can be attributed to TCs in the SWR? How do the environmental moisture anomalies change in response to time of year and TC location given the seasonal variations in the large-scale environment and TC size (Knaff et al. 2014)? Can the conclusions of the present study for WPAC TCs be extended to TCs in other basins despite the differences in the large-scale environment and TC size among basins (Knaff et al. 2007; Chavas and Emanuel 2010; Knaff et al. 2014)? These questions will remain the focus of ongoing research.

Acknowledgments

This research was supported by NASA Headquarters under the NASA Earth and Space Science Fellowship Program Grant 11-Earth11R-0014, NSF Grant ATM-0842618, and the NSF Atmospheric and Geospace Sciences Postdoctoral Research Fellowship Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This research has benefited from critical and constructive input from Daniel Voss of the University of Kansas, John Knaff of CSU CIRA, two anonymous reviewers, Ming Cai of the Florida State University, Bill Dewar of the Florida State University, Michael Bosilovich of NASA GSFC, Lance Bosart of the University at Albany, SUNY, Paul Roundy of the University at Albany, SUNY, Kyle MacRitchie of the University at Albany, SUNY, David Ryglicki of FNMOC, and Ryan Truchelut of the Florida State University. We acknowledge the Florida State University Research Computing Center facility and staff for contributions to the results presented in this paper. This work would not have been possible without the availability of the NCEP CFSR dataset from NCAR and the NOAA HYSPLIT model from NOAA/ARL. Some of the computations were calculated in GrADS provided by COLA IGES. Figures were made using Matplotlib written by John D. Hunter and GrADS provided by COLA IGES.

REFERENCES

  • Andersson, E., and Coauthors, 2005: Assimilation and modeling of the atmospheric hydrological cycle in the ECMWF forecasting system. Bull. Amer. Meteor. Soc., 86, 387402, doi:10.1175/BAMS-86-3-387.

    • Search Google Scholar
    • Export Citation
  • Archambault, H., , L. Bosart, , D. Keyser, , and J. Cordeira, 2013: A climatological analysis of the extratropical flow response to recurving western North Pacific tropical cyclones. Mon. Wea. Rev., 141, 23252346, doi:10.1175/MWR-D-12-00257.1.

    • Search Google Scholar
    • Export Citation
  • Back, L., , and C. Bretherton, 2006: Geographic variability in the export of moist static energy and vertical motion profiles in the tropical Pacific. Geophys. Res. Lett.,33, L17810, doi:10.1029/2006GL026672.

  • Berrisford, P., and Coauthors, 2011: Atmospheric conservation properties in ERA-Interim. Quart. J. Roy. Meteor. Soc., 137, 13811399, doi:10.1002/qj.864.

    • Search Google Scholar
    • Export Citation
  • Bloom, S., , L. Takacs, , A. da Silva, , and D. Ledvina, 1996: Data assimilation using incremental analysis updates. Mon. Wea. Rev., 124, 12561271, doi:10.1175/1520-0493(1996)124<1256:DAUIAU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., , J. Chen, , F. Robertson, , and R. Adler, 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor., 47, 2279–2299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation
  • Bosilovich, M., , F. Robertson, , and J. Chen, 2011: Global energy and water budgets in MERRA. J. Climate, 24, 57215739, doi:10.1175/2011JCLI4175.1.

    • Search Google Scholar
    • Export Citation
  • Chavas, D., , and K. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett.,37, L18816, doi:10.1029/2010GL044558.

  • Chiodo, G., , and L. Haimberger, 2010: Interannual changes in mass consistent energy budgets from ERA-Interim and satellite data. J. Geophys. Res.,115, D02112, doi:10.1029/2009JD012049.

  • Chu, J., , C. Sampson, , A. Levine, , and E. Fukada, 2002: The Joint Typhoon Warning Center tropical cyclone best tracks, 1945–2000. Naval Research Laboratory, Reference NRL/MR/7540-02-16. [Available online at http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/TC_bt_report.html.]

  • Dai, A., , J. Wang, , R. H. Ware, , and T. Van Hove, 2002: Diurnal variation in water vapor over North America and its implications for sampling errors in radiosonde humidity. J. Geophys. Res., 107, 4090, doi:10.1029/2001JD000642.

    • Search Google Scholar
    • Export Citation
  • Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Draxler, R., 1999: HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, 46 pp.

  • Emanuel, K., 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., 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 11431155, doi:10.1175/1520-0469(1988)045<1143:TMIOH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Emanuel, K., 2008: The hurricane–climate connection. Bull. Amer. Meteor. Soc., 89, ES10–ES20, doi:10.1175/BAMS-89-5-Emanuel.

  • Frank, W., 1977: The structure and energetics of the tropical cyclone. I. Storm structure. Mon. Wea. Rev., 105, 11191135, doi:10.1175/1520-0493(1977)105<1119:TSAEOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frank, W., 1982: Large-scale characteristics of tropical cyclones. Mon. Wea. Rev., 110, 572586, doi:10.1175/1520-0493(1982)110<0572:LSCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Frank, W., , and P. Roundy, 2006: The role of tropical waves in tropical cyclogenesis. Mon. Wea. Rev., 134, 23972417, doi:10.1175/MWR3204.1.

    • Search Google Scholar
    • Export Citation
  • Galarneau, T., , L. Bosart, , and R. Schumacher, 2010: Predecessor rain events ahead of tropical cyclones. Mon. Wea. Rev., 138, 32723297, doi:10.1175/2010MWR3243.1.

    • Search Google Scholar
    • Export Citation
  • Gill, A., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc., 106, 447462, doi:10.1002/qj.49710644905.

    • Search Google Scholar
    • Export Citation
  • Grams, C., and Coauthors, 2011: The key role of diabatic processes in modifying the upper-tropospheric wave guide: A North Atlantic case-study. Quart. J. Roy. Meteor. Soc., 137, 21742193, doi:10.1002/qj.891.

    • Search Google Scholar
    • Export Citation
  • Grams, C., , S. Jones, , C. Davis, , P. Harr, , and M. Weissmann, 2013a: The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part I: Upper-level ridgebuilding and modification of the jet. Quart. J. Roy. Meteor. Soc., 139, 21482164, doi:10.1002/qj.2091.

    • Search Google Scholar
    • Export Citation
  • Grams, C., , S. Jones, , and C. Davis, 2013b: The impact of Typhoon Jangmi (2008) on the midlatitude flow. Part II: Downstream evolution. Quart. J. Roy. Meteor. Soc., 139, 21652180, doi:10.1002/qj.2119.

    • Search Google Scholar
    • Export Citation
  • Guinn, T. A., , and W. H. Schubert, 1993: Hurricane spiral bands. J. Atmos. Sci., 50, 33803403, doi:10.1175/1520-0469(1993)050<3380:HSB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Halverson, J., , 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, doi:10.1175/JAS3596.1.

    • Search Google Scholar
    • Export Citation
  • Harr, P., , and J. Dea, 2009: Downstream development associated with the extratropical transition of tropical cyclones over the western North Pacific. Mon. Wea. Rev., 137, 12951319, doi:10.1175/2008MWR2558.1.

    • Search Google Scholar
    • Export Citation
  • Harr, P., , D. Anwender, , and S. Jones, 2008: Predictability associated with the downstream impacts of the extratropical transition of tropical cyclones: Methodology and a case study of Typhoon Nabi (2005). Mon. Wea. Rev., 136, 32053225, doi:10.1175/2008MWR2248.1.

    • Search Google Scholar
    • Export Citation
  • Hart, R., 2011: An inverse relationship between aggregate Northern Hemisphere tropical cyclone activity and subsequent winter climate. Geophys. Res. Lett.,38, L01705, doi:10.1029/2010GL045612.

  • Hart, R., , R. Maue, , and M. Watson, 2007: Estimating local memory of tropical cyclones through MPI anomaly evolution. Mon. Wea. Rev., 135, 39904005, doi:10.1175/2007MWR2038.1.

    • Search Google Scholar
    • Export Citation
  • Hart, R., , R. Maue, , and M. Watson, 2008: How long does the climate record “remember” a tropical cyclone? Bull. Amer. Meteor. Soc., 89, 596598.

    • Search Google Scholar
    • Export Citation
  • Hatsushika, H., , J. Tsutsui, , M. Fiorino, , and K. Onogi, 2006: Impact of wind profile retrievals on the analysis of tropical cyclones in the JRA-25 reanalysis. J. Meteor. Soc. Japan, 84, 891905, doi:10.2151/jmsj.84.891.

    • Search Google Scholar
    • Export Citation
  • Hawkins, H., , and D. Rubsam, 1968: Hurricane Hilda, 1964. Mon. Wea. Rev., 96, 617636, doi:10.1175/1520-0493(1968)096<0617:HH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hawkins, H., , and S. Imbembo, 1976: The structure of a small, intense hurricane—Inez 1966. Mon. Wea. Rev., 104, 418442, doi:10.1175/1520-0493(1976)104<0418:TSOASI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G., 1986: Interannual variability of the Australian summer monsoon at Darwin: 1952–82. Mon. Wea. Rev., 114, 594604, doi:10.1175/1520-0493(1986)114<0594:IVOTAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Holland, G., 1995: Scale interaction in the western Pacific monsoon. Meteor. Atmos. Phys., 56, 5779, doi:10.1007/BF01022521.

  • Hoskins, B., , I. Draghici, , and H. Davies, 1978: A new look at the ω-equation. Quart. J. Roy. Meteor. Soc., 104, 3138, doi:10.1002/qj.49710443903.

    • Search Google Scholar
    • Export Citation
  • Houze, R., 2010: Clouds in tropical cyclones. Mon. Wea. Rev., 138, 293–344, doi:10.1175/2009MWR2989.1.

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

    • Search Google Scholar
    • Export Citation
  • Kiladis, G., , K. Straub, , and P. Haertel, 2005: Zonal and vertical structure of the Madden–Julian oscillation. J. Atmos. Sci., 62, 27902809, doi:10.1175/JAS3520.1.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G., , M. Wheeler, , P. Haertel, , K. Straub, , and P. Roundy, 2009: Convectively coupled equatorial waves. Rev. Geophys., 47, RG2003, doi:10.1029/2008RG000266.

    • Search Google Scholar
    • Export Citation
  • Kiranmayi, L., , and E. Maloney, 2011: Intraseasonal moist static energy budget in reanalysis data. J. Geophys. Res., 116, D21117, doi:10.1029/2011JD016031.

    • Search Google Scholar
    • Export Citation
  • Knaff, J. A., , C. R. Sampson, , M. DeMaria, , T. P. Marchok, , J. M. Gross, , and C. J. McAdie, 2007: Statistical tropical cyclone wind radii prediction using climatology and persistence. Wea. Forecasting, 22, 781791, doi:10.1175/WAF1026.1.

    • 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, doi:10.1175/JCLI-D-13-00096.1.

    • Search Google Scholar
    • Export Citation
  • LaSeur, N., , and H. Hawkins, 1963: An analysis of Hurricane Cleo (1958) based on data from research reconnaissance aircraft. Mon. Wea. Rev., 91, 694709, doi:10.1175/1520-0493(1963)091<0694:AAOHCB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Manning, D., , and R. Hart, 2007: Evolution of North Atlantic ERA40 tropical cyclone representation. Geophys. Res. Lett.,34, L05705, doi:10.1029/2006GL028266.

  • McTaggart-Cowan, R., , L. F. Bosart, , J. R. Gyakum, , and E. H. Atallah, 2007: Hurricane Katrina (2005). Part II: Evolution and hemispheric impacts of a diabatically generated warm pool. Mon. Wea. Rev., 135, 39273949, doi:10.1175/2007MWR2096.1.

    • Search Google Scholar
    • Export Citation
  • Miloshevich, L. M., , H. Vömel, , A. Paukkunen, , A. J. Heymsfield, , and S. J. Oltmans, 2001: Characterization and correction of relative humidity measurements from Vaisala RS80-A radiosondes at cold temperatures. J. Atmos. Oceanic Technol., 18, 135156, doi:10.1175/1520-0426(2001)018<0135:CACORH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nieto Ferreira, R., , and W. Schubert, 1997: Barotropic aspects of ITCZ breakdown. J. Atmos. Sci., 54, 261285, doi:10.1175/1520-0469(1997)054<0261:BAOIB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Onogi, K., and Coauthors, 2007: The JRA-25 reanalysis. J. Meteor. Soc. Japan, 85, 369432, doi:10.2151/jmsj.85.369.

  • Palmén, E., , and C. Newton, 1969: Atmospheric Circulation Systems: Their Structure and Physical Interpretation, Academic Press, 603 pp.

  • Riemer, M., , S. Jones, , and C. Davis, 2008: The impact of extratropical transition on the downstream flow: An idealized modeling study with a straight jet. Quart. J. Roy. Meteor. Soc., 134, 6991, doi:10.1002/qj.189.

    • Search Google Scholar
    • Export Citation
  • Roundy, P., , and W. Frank, 2004: A climatology of waves in the equatorial region. J. Atmos. Sci., 61, 21052132, doi:10.1175/1520-0469(2004)061<2105:ACOWIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Schenkel, B., , and R. Hart, 2011: Potential implications of tropical cyclone passage. Bull. Amer. Meteor. Soc., 91, 12821283.

  • Schenkel, B., , and R. Hart, 2012: An examination of tropical cyclone position, intensity, and intensity life cycle within atmospheric reanalysis datasets. J. Climate, 25, 34533475, doi:10.1175/2011JCLI4208.1.

    • Search Google Scholar
    • Export Citation
  • Schreck, C., , J. Molinari, , and K. Mohr, 2011: Attributing tropical cyclogenesis to equatorial waves in the western North Pacific. J. Atmos. Sci., 68, 195209, doi:10.1175/2010JAS3396.1.

    • Search Google Scholar
    • Export Citation
  • Schreck, C., , J. Molinari, , and A. Aiyyer, 2012: A global view of equatorial waves and tropical cyclogenesis. Mon. Wea. Rev., 140, 774788, doi:10.1175/MWR-D-11-00110.1.

    • Search Google Scholar
    • Export Citation
  • Seager, R., , and N. Henderson, 2013: Diagnostic computation of moisture budgets in the ERA-Interim reanalysis with reference to analysis of CMIP-archived atmospheric model data. J. Climate, 26, 78767901, doi:10.1175/JCLI-D-13-00018.1.

    • Search Google Scholar
    • Export Citation
  • Seager, R., and Coauthors, 2007: Model projections of an imminent transition to a more arid climate in southwestern North America. Science, 316, 11811184, doi:10.1126/science.1139601.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , and S. Camargo, 2005: Influence of western North Pacific tropical cyclones on their large-scale environment. J. Atmos. Sci., 62, 33963407, doi:10.1175/JAS3539.1.

    • Search Google Scholar
    • Export Citation
  • Sobel, A., , S. Wang, , and D. Kim, 2014: Moist static energy budget of the MJO during DYNAMO. J. Atmos. Sci., 71, 42764291, doi:10.1175/JAS-D-14-0052.1.

    • Search Google Scholar
    • Export Citation
  • Stern, D., , and D. Nolan, 2009: Reexamining the vertical structure of tangential winds in tropical cyclones: Observations and theory. J. Atmos. Sci., 66, 35793600, doi:10.1175/2009JAS2916.1.

    • Search Google Scholar
    • Export Citation
  • Stohl, A., , C. Forster, , and H. Sodemann, 2008: Remote sources of water vapor forming precipitation on the Norwegian west coast at 60°N—A tale of hurricanes and an atmospheric river. J. Geophys. Res.,113, D05102, doi:10.1029/2007JD009006.

  • Thorne, P., , and R. Vose, 2010: Reanalyses suitable for characterizing long-term trends: Are they really achievable? Bull. Amer. Meteor. Soc., 91, 353361, doi:10.1175/2009BAMS2858.1.

    • Search Google Scholar
    • Export Citation
  • Tomas, R., , and P. Webster, 1997: The role of inertial instability in determining the location and strength of near-equatorial convection. Quart. J. Roy. Meteor. Soc., 123, 14451482, doi:10.1002/qj.49712354202.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K., , J. Fasullo, , and J. Mackaro, 2011: Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J. Climate, 24, 49074924, doi:10.1175/2011JCLI4171.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , and C. D. Thorncroft, 2013: The role of convectively coupled atmospheric Kelvin waves on African easterly wave activity. Mon. Wea. Rev., 141, 19101924, doi:10.1175/MWR-D-12-00147.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and P. E. Roundy, 2011: The Madden–Julian oscillation’s influence on African easterly waves and downstream tropical cyclogenesis. Mon. Wea. Rev., 139, 27042722, doi:10.1175/MWR-D-10-05028.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and M. Janiga, 2012a: Atlantic tropical cyclogenesis: A three-way interaction between an African easterly wave, diurnally varying convection, and a convectively coupled atmospheric Kelvin wave. Mon. Wea. Rev., 140, 11081124, doi:10.1175/MWR-D-11-00122.1.

    • Search Google Scholar
    • Export Citation
  • Ventrice, M. J., , C. D. Thorncroft, , and C. J. Schreck III, 2012b: Impacts of convectively coupled Kelvin waves on environmental conditions for Atlantic tropical cyclogenesis. Mon. Wea. Rev., 140, 21982214, doi:10.1175/MWR-D-11-00305.1.

    • Search Google Scholar
    • Export Citation
  • Vincent, D., , and R. Waterman, 1979: Large-scale atmospheric conditions during the intensification of Hurricane Carmen (1974). I. Temperature, moisture and kinematics. Mon. Wea. Rev., 107, 283294, doi:10.1175/1520-0493(1979)107<0283:LSACDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vincent, D., , and A. Fink, 2001: Tropical cyclone environments over the northeastern and northwestern Pacific based on ERA-15 analyses. Mon. Wea. Rev., 129, 19281948, doi:10.1175/1520-0493(2001)129<1928:TCEOTN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, J., , H. L. Cole, , D. J. Carlson, , E. R. Miller, , K. Beierle, , A. Paukkunen, , and T. K. Laine, 2002: Corrections of humidity measurement errors from the Vaisala RS80 radiosonde—Application to TOGA COARE data. J. Atmos. Oceanic Technol., 19, 9811002, doi:10.1175/1520-0426(2002)019<0981:COHMEF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Webster, P. J., 1972: Response of the tropical atmosphere to local, steady forcing. Mon. Wea. Rev., 100, 518541, doi:10.1175/1520-0493(1972)100<0518:ROTTAT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wentz, F. J., , and R. W. Spencer, 1998: SSM/I rain retrievals within a unified all-weather ocean algorithm. J. Atmos. Sci., 55, 16131627, doi:10.1175/1520-0469(1998)055<1613:SIRRWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wheeler, M., , and G. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399, doi:10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wimmers, A., , and C. Velden, 2011: Seamless advective blending of total precipitable water retrievals from polar-orbiting satellites. J. Appl. Meteor. Climatol., 50, 10241036, doi:10.1175/2010JAMC2589.1.

    • Search Google Scholar
    • Export Citation
  • Wood, K., , and E. Ritchie, 2014: A 32-year reanalysis intercomparison of tropical cyclone structure in the eastern North Pacific and North Atlantic. 31st Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 53. [Available online at https://ams.confex.com/ams/31Hurr/webprogram/Paper244195.html.]

  • Yanai, M., , S. Esbensen, , and J. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, doi:10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2005: Madden–Julian oscillation. Rev. Geophys., 43, RG2003, doi:10.1029/2004RG000158.

Save