1. Introduction
Though the bulk of research on tropical cyclones to date has focused on the dynamics of storm intensity, as measured by the maximum wind speed
How is outer storm size defined? Following the seminal work of Frank (1977), as well as more recent work by Chavas et al. (2015), the complete radial structure of a tropical cyclone may be considered to leading order to be composed of a narrow inner convecting region that varies with intensity and a broad outer nonconvecting region that does not vary with intensity, suggesting that any metric that lies sufficiently far outside of the convecting inner core may be suitable. More specifically,
Ultimately, the objective is an understanding of the underlying physics that governs the length scale of the outer storm circulation. Such an understanding would help explain storm size and its variation in nature, both on short time scales for operational forecasting as well as on longer time scales across climate states, including global warming. Toward this end, recent work has provided new theoretical insights into physical environmental parameters that govern this length scale. First, in rotating radiative–convective equilibrium (RCE), storm size has been demonstrated in both axisymmetric and three-dimensional geometry to scale with the ratio of the potential intensity to the Coriolis parameter
In addition, quantitative information on storm size variation is critical for tropical cyclone risk analysis given that
Thus, in an effort to update and improve upon the results of Chavas and Emanuel (2010), here we first create new datasets of
2. Data and methodology
a. Storm parameters
For analysis of outer storm size, we estimate the radius at which the azimuthal mean of the azimuthal wind equals 12 m s−1 (
b. Environmental parameters
To calculate the length scale
The scaling for storm size in rotating radiative–convective equilibrium of Khairoutdinov and Emanuel (2013), given by
The relative SST is defined as the difference between the local SST and the tropical mean SST, the latter taken to be the mean2 over the latitudinal band [30°S, 30°N] following the methodology of Lin et al. (2015) and past work on the effects of remote SST on tropical cyclone activity (e.g., Vecchi and Soden 2007; Camargo et al. 2013). This quantity is calculated from the HadISST monthly SST dataset. Results are not sensitive to the precise latitudinal band employed to calculate the tropical mean SST; very similar results are obtained for [15°S, 15°N], [20°S, 20°N], and [25°S, 25°N]. Finally, for calculation of the storm central pressure deficit, the environmental sea level pressure is defined as its value interpolated to the storm time and location from monthly ERA-Interim data in the same manner as the above quantities. The use of monthly mean data acts to smooth out the contribution from the low pressure of the storm itself.
3. Size distributions
We first examine the geographic variation of storm size over the ocean basins, which is displayed in Fig. 2. Basins are defined as in Knutson et al. (2015) such that each storm’s basin is assigned according to its location of genesis in the best-track database. For
We may compare the results between our two storm size metrics in Fig. 2, which displays the same geographic distribution for
Prior to further analysis, we first revisit the observed dependence of storm size on intensity. Our focus here is to study
Following from the results of Fig. 3, we focus our analysis on storms with intensities
Statistics, including median, first and third quartiles, mean μ, standard deviation σ, and coefficient of variation, of distributions of
For comparison purposes, Knaff et al. (2014) estimated the wind speed at r = 500 km at 850 hPa, denoted V500, from which they extrapolate outward using a simple linear decay model with fixed decay rate to estimate the radius of 5-kt winds
4. Relationship to relevant environmental parameters
Ideally we seek a physical explanation of the large variability in storm size evident in Fig. 4. Thus, as a first step toward fully understanding variability in size, we test three recently identified parameters with credible physical relationships with tropical cyclone size:
a. Tropical cyclone length scale
We begin by testing the theoretical tropical cyclone length scale
Though a positive relationship between size and
Finally, Fig. 5c displays the direct relationship between size and
b. Rotating RCE scaling of Khairoutdinov and Emanuel (2013)
We next test the rotating RCE scaling of Khairoutdinov and Emanuel (2013), given by
As noted above, the scaling of Khairoutdinov and Emanuel (2013) applies to a world in which surface frictional dissipation within tropical cyclones dominates entropy production in the system. Given that tropical cyclones typically do not cover a substantial fraction of the tropical oceans, it seems likely that the underlying assumptions of this theory may not apply in nature; indeed, the real world may lie closer to the unaggregated RCE limit in which entropy production is dominated by irreversible phase changes and diffusion of water vapor instead of frictional dissipation (Pauluis and Held 2002) and tropical cyclones impose only a weak influence on the long-term mean state. This result is also in line with the finding of Chavas and Emanuel (2014) that equilibrium storm size in an axisymmetric model does not scale with the deformation radius. Moreover, as noted in section 2, the complete theoretical prediction for
c. Relative SST
Based on large samples of storm size determined from rainfall fields, Lin et al. (2015) noted a strong dependence of mean storm size on relative SST (i.e., SST within the local environment of the storm minus the tropical mean SST). Notably, relative SST correlates strongly with
Thus, the logical next question is the following: Does wind field size also exhibit a dependence on relative SST? The relationship between storm size and relative SST is shown in Fig. 7. Indeed, a systematic dependence on relative SST does exist for both mean
Statistics of
Additionally, note from Table 2 that the variance increases in conjunction with the mean such that the coefficient of variation (CV) remains relatively stable across the positive relative SST bins, particularly for
5. Wind–pressure–size relationship
Thus, here we perform a simple analysis using best-track data to test empirically whether the effect of size on central pressure deficit is discernible in our new-size dataset. Figure 8 displays the joint dependence of minimum central pressure deficit on intensity and size. We focus exclusively on data from the Atlantic basin, where independent estimates of wind intensity and minimum central pressure are most prevalent, with data filtered for cases with
Despite the caveats associated with the best-track dataset noted above, the dependence of minimum central pressure on storm size is discernible from the combination of the QuikSCAT and best-track datasets in line with prior work on this subject; indeed, cases for which standard wind–pressure relationships were applied would introduce a low bias in our empirical estimate of the dependence of dp on size. These results motivate a deeper theoretical exploration of the wind–pressure–size relationship and comparison with in situ observational databases of independent wind and pressure measurements, which is the subject of future work.
6. Discussion and conclusions
This work revisits the statistics of observed tropical cyclone size explored in Chavas and Emanuel (2010) using a recently updated version of the QuikSCAT satellite scatterometer-based ocean wind vector database and places the analysis in the context of recent advances in our theoretical understanding of storm size and structure. We first create new databases of the radius of 12 m s−1 winds estimated from observations from the QSCAT-R database and of the outer radius of vanishing wind
We find that both metrics of outer storm size are approximately lognormally distributed, with global median values of 303 km for
Importantly, for risk applications, the magnitude of the variation of size with relative SST found here is substantial given the close relationship between
Our results beg the following question: Why does
Additionally, we note that here we have sought explanations for variance in storm size based strictly on local environmental parameters. However, the inability of such parameters to explain large amounts of residual variance may also indicate an important role for storm-specific parameters. First and foremost, the potential existence of long time scales to equilibrium noted above may imply a long memory for size for a given storm, perhaps extending as far back as genesis. Indeed, the length scale of the precursor disturbance is not explored here, though it has been shown to correlate strongly with storm size at maturity in idealized modeling studies (Rotunno and Emanuel 1987) as well as in observations in the western North Pacific basin (Cocks and Gray 2002; Lee et al. 2010). Second, past work has noted a dependence of size simply on time since genesis (Kossin et al. 2007), which would suggest some internal physical process to the tropical cyclone that encourages expansion with time; the underlying physics of such behavior, however, have yet to be elucidated. Finally, an examination of the direct physical relationship between storm size and environmental moisture would be fruitful, though it is complicated by the strong time-dependent influence the overturning circulation of the storm itself imposes on the local moisture field. Such questions warrant further research.
We have focused principally on storm size within the tropics, purposefully avoiding the role of extratropical transition. The analyses of Fig. 2 appear to suggest that at middle and high latitudes, where extratropical transition is common,
Despite such limitations, the size distributions presented here are useful for risk analysis applications and as an observational benchmark for comparison with alternative observational size databases as well as output from both idealized modeling studies (e.g., Knutson et al. 2015) and operational weather forecasts. The analyses on the environmental parameters provide a basis for developing environment- and climate-dependent models for storm size in the future. Finally, we note that the outer radius translates to the surface area occupied by a storm, and thus its dynamics may impose limits on the number of storms that can form in a limited area at a given time. Thus, the results presented here may be relevant for the statistics of tropical cyclogenesis in nature, particularly in localized regions where conditions favorable for genesis are spatially limited or where there is a tendency for multiple storms to form in close proximity to one another.
Acknowledgments
Support for this work was provided for DC and NL by the National Science Foundation under Award AGS-1331362 and EAR-1520683 and for WD and YL by the Ministry of Science and Technology of China Award 2014CB441303. All data in this analysis are publicly available upon request from the corresponding author.
REFERENCES
Ahmad, K. A., W. L. Jones, T. Kasparis, S. W. Vergara, I. S. Adams, and J. D. Park, 2005: Oceanic rain rate estimates from the QuikSCAT radiometer: A global precipitation mission pathfinder. J. Geophys. Res., 110, D11101, doi:10.1029/2004JD005560.
Arakawa, H., 1952: Mame-Taifu or midget typhoon. Geophys. Mag., 23, 463–474.
Bister, M., and K. A. Emanuel, 1998: Dissipative heating and hurricane intensity. Meteor. Atmos. Phys., 65, 233–240, doi:10.1007/BF01030791.
Bister, M., and K. A. Emanuel, 2002: Low frequency variability of tropical cyclone potential intensity. 2. Climatology for 1982–1995. J. Geophys. Res., 107, 4621, doi:10.1029/2001JD000780.
Brand, S., 1972: Very large and very small typhoons of the western North Pacific Ocean. J. Meteor. Soc. Japan, 50, 332–341.
Camargo, S. J., M. Ting, and Y. Kushnir, 2013: Influence of local and remote SST on North Atlantic tropical cyclone potential intensity. Climate Dyn., 40, 1515–1529, doi:10.1007/s00382-012-1536-4.
Chan, J. C., 2005: The physics of tropical cyclone motion. Annu. Rev. Fluid Mech., 37, 99–128, doi:10.1146/annurev.fluid.37.061903.175702.
Chan, K. T., and J. C. Chan, 2012: Size and strength of tropical cyclones as inferred from QuikSCAT data. Mon. Wea. Rev., 140, 811–824, doi:10.1175/MWR-D-10-05062.1.
Chan, K. T., and J. C. Chan, 2015: Global climatology of tropical cyclone size as inferred from QuikSCAT data. Int. J. Climatol., 35, 4843–4848, doi:10.1002/joc.4307.
Chavas, D. R., and K. A. Emanuel, 2010: A QuikSCAT climatology of tropical cyclone size. Geophys. Res. Lett., 37, L18816, doi:10.1029/2010GL044558.
Chavas, D. R., and K. A. Emanuel, 2014: Equilibrium tropical cyclone size in an idealized state of axisymmetric radiative–convective equilibrium. J. Atmos. Sci., 71, 1663–1680, doi:10.1175/JAS-D-13-0155.1.
Chavas, D. R., and J. Vigh, 2014: QSCAT-R: The QuikSCAT tropical cyclone radial structure dataset. NCAR Tech. Note TN-513+STR, 27 pp.
Chavas, D. R., N. Lin, and K. Emanuel, 2015: A model for the complete radial structure of the tropical cyclone wind field. Part I: Comparison with observed structure. J. Atmos. Sci., 72, 3647–3662, doi:10.1175/JAS-D-15-0014.1.
Cocks, S. B., and W. M. Gray, 2002: Variability of the outer wind profiles of western North Pacific typhoons: Classifications and techniques for analysis and forecasting. Mon. Wea. Rev., 130, 1989–2005, doi:10.1175/1520-0493(2002)130<1989:VOTOWP>2.0.CO;2.
Courtney, J., and J. A. Knaff, 2009: Adapting the Knaff and Zehr wind–pressure relationship for operational use in Tropical Cyclone Warning Centres. Aust. Meteor. Oceanogr. J., 58, 167–179.
Davis, C. A., 2015: The formation of moist vortices and tropical cyclones in idealized simulations. J. Atmos. Sci., 72, 3499–3516, doi:10.1175/JAS-D-15-0027.1.
Dee, D., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.
Donelan, M., B. Haus, N. Reul, W. Plant, M. Stiassnie, H. Graber, O. Brown, and E. Saltzman, 2004: On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys. Res. Lett., 31, L18306, doi:10.1029/2004GL019460.
Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585–605, doi:10.1175/1520-0469(1986)043<0585:AASITF>2.0.CO;2.
Emanuel, K. A., 1987: The dependence of hurricane intensity on climate. Nature, 326, 483–485, doi:10.1038/326483a0.
Emanuel, K. A., 1994: Atmospheric Convection. Oxford University Press, 580 pp.
Emanuel, K. A., 1995a: The behavior of a simple hurricane model using a convective scheme based on subcloud-layer entropy equilibrium. J. Atmos. Sci., 52, 3960–3968, doi:10.1175/1520-0469(1995)052<3960:TBOASH>2.0.CO;2.
Emanuel, K. A., 1995b: Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics. J. Atmos. Sci., 52, 3969–3976, doi:10.1175/1520-0469(1995)052<3969:SOTCTS>2.0.CO;2.
Emanuel, K. A., 2004: Tropical cyclone energetics and structure. Atmospheric Turbulence and Mesoscale Meteorology, E. Fedorovich, R. Rotunno, and B. Stevens, Eds., Cambridge University Press, 165–192.
Emanuel, K. A., and R. Rotunno, 2011: Self-stratification of tropical cyclone outflow. Part I: Implications for storm structure. J. Atmos. Sci., 68, 2236–2249, doi:10.1175/JAS-D-10-05024.1.
Emanuel, K. A., J. David Neelin, and C. S. Bretherton, 1994: On large-scale circulations in convecting atmospheres. Quart. J. Roy. Meteor. Soc., 120, 1111–1143, doi:10.1002/qj.49712051902.
Frank, W. M., 1977: The structure and energetics of the tropical cyclone. I: Storm structure. Mon. Wea. Rev., 105, 1119–1135, doi:10.1175/1520-0493(1977)105<1119:TSAEOT>2.0.CO;2.
Hart, R. E., and J. L. Evans, 2001: A climatology of the extratropical transition of Atlantic tropical cyclones. J. Climate, 14, 546–564, doi:10.1175/1520-0442(2001)014<0546:ACOTET>2.0.CO;2.
Hill, K. A., and G. M. Lackmann, 2009: Influence of environmental humidity on tropical cyclone size. Mon. Wea. Rev., 137, 3294–3315, doi:10.1175/2009MWR2679.1.
Irish, J. L., and D. T. Resio, 2010: A hydrodynamics-based surge scale for hurricanes. Ocean Eng., 37, 69–81, doi:10.1016/j.oceaneng.2009.07.012.
Khairoutdinov, M., and K. Emanuel, 2013: Rotating radiative-convective equilibrium simulated by a cloud-resolving model. J. Adv. Model. Earth Syst., 5, 816–825, doi:10.1002/2013MS000253.
Kieu, C. Q., H. Chen, and D.-L. Zhang, 2010: An examination of the pressure–wind relationship for intense tropical cyclones. Wea. Forecasting, 25, 895–907, doi:10.1175/2010WAF2222344.1.
Kim, H.-S., G. A. Vecchi, T. R. Knutson, W. G. Anderson, T. L. Delworth, A. Rosati, F. Zeng, and M. Zhao, 2014: Tropical cyclone simulation and response to CO2 doubling in the GFDL CM2.5 high-resolution coupled climate model. J. Climate, 27, 8034–8054, doi:10.1175/JCLI-D-13-00475.1.
Knaff, J. A., and R. M. Zehr, 2007: Reexamination of tropical cyclone wind–pressure relationships. Wea. Forecasting, 22, 71–88, doi:10.1175/WAF965.1.
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, 781–791, doi:10.1175/WAF1026.1.
Knaff, J. A., S. P. Longmore, and D. A. Molenar, 2014: An objective satellite-based tropical cyclone size climatology. J. Climate, 27, 455–476, doi:10.1175/JCLI-D-13-00096.1; Corrigendum, 28, 8648–8651, doi:10.1175/JCLI-D-15-0610.1.
Knutson, T., J. Sirutis, M. Zhao, R. Tuleya, M. Bender, G. Vecchi, G. Villarini, and D. Chavas, 2015: Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios. J. Climate, 28, 7203–7224, doi:10.1175/JCLI-D-15-0129.1.
Kossin, J. P., J. A. Knaff, H. I. Berger, D. C. Herndon, T. A. Cram, C. S. Velden, R. J. Murnane, and J. D. Hawkins, 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, 89–101, doi:10.1175/WAF985.1.
Kossin, J. P., K. A. Emanuel, and G. A. Vecchi, 2014: The poleward migration of the location of tropical cyclone maximum intensity. Nature, 509, 349–352, doi:10.1038/nature13278.
Lander, M. A., 1994: Description of a monsoon gyre and its effects on the tropical cyclones in the western North Pacific during August 1991. Wea. Forecasting, 9, 640–654, doi:10.1175/1520-0434(1994)009<0640:DOAMGA>2.0.CO;2.
Lee, C.-S., K. K. Cheung, W.-T. Fang, and R. L. Elsberry, 2010: Initial maintenance of tropical cyclone size in the western North Pacific. Mon. Wea. Rev., 138, 3207–3223, doi:10.1175/2010MWR3023.1.
Lin, N., and D. Chavas, 2012: On hurricane parametric wind and applications in storm surge modeling. J. Geophys. Res., 117, D09120, doi:10.1029/2011JD017126.
Lin, N., and K. A. Emanuel, 2016: Grey swan tropical cyclones. Nat. Climate Change, 6, 106–111, doi:10.1038/nclimate2777.
Lin, N., K. A. Emanuel, M. Oppenheimer, and E. Vanmarcke, 2012: Physically based assessment of hurricane surge threat under climate change. Nat. Climate Change, 2, 462–467, doi:10.1038/nclimate1389.
Lin, N., P. Lane, K. A. Emanuel, R. M. Sullivan, and J. P. Donnelly, 2014: Heightened hurricane surge risk in northwest Florida revealed from climatological-hydrodynamic modeling and paleorecord reconstruction. J. Geophys. Res., 119, 8606–8623, doi:10.1002/2014JD021584.
Lin, Y., M. Zhao, and M. Zhang, 2015: Tropical cyclone rainfall area controlled by relative sea surface temperature. Nat. Commun., 6, 6591, doi:10.1038/ncomms7591.
Massey, F. J., Jr., 1951: The Kolmogorov–Smirnov test for goodness of fit. J. Amer. Stat. Assoc., 46, 68–78, doi:10.1080/01621459.1951.10500769.
Matyas, C. J., 2010: Associations between the size of hurricane rain fields at landfall and their surrounding environments. Meteor. Atmos. Phys., 106, 135–148, doi:10.1007/s00703-009-0056-1.
Merrill, R. T., 1984: A comparison of large and small tropical cyclones. Mon. Wea. Rev., 112, 1408–1418, doi:10.1175/1520-0493(1984)112<1408:ACOLAS>2.0.CO;2.
Mueller, K. J., M. DeMaria, J. Knaff, J. P. Kossin, and T. H. Vonder Haar, 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, 21, 990–1005, doi:10.1175/WAF955.1.
Pauluis, O., and I. M. Held, 2002: Entropy budget of an atmosphere in radiative–convective equilibrium. Part I: Maximum work and frictional dissipation. J. Atmos. Sci., 59, 125–139, doi:10.1175/1520-0469(2002)059<0125:EBOAAI>2.0.CO;2.
Pielke, R. A., Jr., J. Gratz, C. W. Landsea, D. Collins, M. A. Saunders, and R. Musulin, 2008: Normalized hurricane damages in the United States: 1900–2005. Nat. Hazards Rev., 9, 29–42, doi:10.1061/(ASCE)1527-6988(2008)9:1(29).
Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77–78, 53–64, doi:10.1016/S0167-6105(98)00131-7.
Ramsay, H. A., and A. H. Sobel, 2011: Effects of relative and absolute sea surface temperature on tropical cyclone potential intensity using a single-column model. J. Climate, 24, 183–193, doi:10.1175/2010JCLI3690.1.
Reasor, P. D., M. T. Montgomery, F. D. Marks Jr., and J. F. Gamache, 2000: Low-wavenumber structure and evolution of the hurricane inner core observed by airborne dual-doppler radar. Mon. Wea. Rev., 128, 1653–1680, doi:10.1175/1520-0493(2000)128<1653:LWSAEO>2.0.CO;2.
Reed, K. A., and D. R. Chavas, 2015: Uniformly rotating global radiative–convective equilibrium in the Community Atmosphere Model, version 5. J. Adv. Model. Earth Syst., 7, 1938–1955, doi:10.1002/2015MS000519.
Rotunno, R., and K. A. Emanuel, 1987: An air–sea interaction theory for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric numerical model. J. Atmos. Sci., 44, 542–561, doi:10.1175/1520-0469(1987)044<0542:AAITFT>2.0.CO;2.
Rotunno, R., and G. H. Bryan, 2012: Effects of parameterized diffusion on simulated hurricanes. J. Atmos. Sci., 69, 2284–2299, doi:10.1175/JAS-D-11-0204.1.
Schwerdt, R. W., F. P. Ho, and R. R. Watkins, 1979: Meteorological criteria for standard project hurricane and probable maximum hurricane windfields, Gulf and East Coasts of the United States. NOAA Tech. Rep. NWS 23, 317 pp.
Shoemaker, D. N., 1989: Relationships between tropical cyclone deep convection and the radial extent of damaging winds. University of Colorado Department of Atmospheric Science Paper 457, 109 pp. [Available online at http://hdl.handle.net/10217/78813.]
Sitkowski, M., J. P. Kossin, and C. M. Rozoff, 2011: Intensity and structure changes during hurricane eyewall replacement cycles. Mon. Wea. Rev., 139, 3829–3847, doi:10.1175/MWR-D-11-00034.1.
Smith, R. K., C. W. Schmidt, and M. T. Montgomery, 2011: An investigation of rotational influences on tropical-cyclone size and intensity. Quart. J. Roy. Meteor. Soc., 137, 1841–1855, doi:10.1002/qj.862.
Sobel, A. H., and C. S. Bretherton, 2000: Modeling tropical precipitation in a single column. J. Climate, 13, 4378–4392, doi:10.1175/1520-0442(2000)013<4378:MTPIAS>2.0.CO;2.
Stephens, G. L., 1990: On the relationship between water vapor over the oceans and sea surface temperature. J. Climate, 3, 634–645, doi:10.1175/1520-0442(1990)003<0634:OTRBWV>2.0.CO;2.
Stewart, S., 2014: National Hurricane Center annual summary: 2012 Atlantic hurricane season. National Hurricane Center Tropical Cyclone Rep., 11 pp. [Available online at http://www.nhc.noaa.gov/data/tcr/summary_atlc_2012.pdf.]
Stiles, B. W., R. E. Danielson, W. L. Poulsen, M. J. Brennan, S. Hristova-Veleva, T.-P. Shen, and A. G. Fore, 2014: Optimized tropical cyclone winds from QuikSCAT: A neural network approach, 52, 7418–7434, doi:10.1109/TGRS.2014.2312333.
Tang, B., and K. Emanuel, 2010: Midlevel ventilation’s constraint on tropical cyclone intensity. J. Atmos. Sci., 67, 1817–1830, doi:10.1175/2010JAS3318.1.
Tang, B., and K. Emanuel, 2012: A ventilation index for tropical cyclones. Bull. Amer. Meteor. Soc., 93, 1901–1912, doi:10.1175/BAMS-D-11-00165.1.
Uhlhorn, E. W., B. W. Klotz, T. Vukicevic, P. D. Reasor, and R. F. Rogers, 2014: Observed hurricane wind speed asymmetries and relationships to motion and environmental shear. Mon. Wea. Rev., 142, 1290–1311, doi:10.1175/MWR-D-13-00249.1.
Vecchi, G. A., and B. J. Soden, 2007: Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature, 450, 1066–1070, doi:10.1038/nature06423.
Wang, Y., 2009: How do outer spiral rainbands affect tropical cyclone structure and intensity? J. Atmos. Sci., 66, 1250–1273, doi:10.1175/2008JAS2737.1.
Weatherford, C., and W. Gray, 1988: Typhoon structure as revealed by aircraft reconnaissance. Part I: Data analysis and climatology. Mon. Wea. Rev., 116, 1032–1043, doi:10.1175/1520-0493(1988)116<1032:TSARBA>2.0.CO;2.
Zhai, A. R., and J. H. Jiang, 2014: Dependence of U.S. hurricane economic loss on maximum wind speed and storm size. Environ. Res. Lett., 9, 064019, doi:10.1088/1748-9326/9/6/064019.
Zhou, W., I. M. Held, and S. T. Garner, 2014: Parameter study of tropical cyclones in rotating radiative–convective equilibrium with column physics and resolution of a 25-km GCM. J. Atmos. Sci., 71, 1058–1069, doi:10.1175/JAS-D-13-0190.1.
Parameter specifications:
An unweighted mean is used following Lin et al. (2015); calculation using an area-weighted mean leads to a maximum single-month difference in tropical mean SST of 0.099 K and has a negligible effect on the results.