• Browning, K. A., and R. Wexler, 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor., 7, 105113, doi:10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and J. M. Fritsch, 2000: Diabatically driven discrete propagation of surface fronts: A numerical analysis. J. Atmos. Sci., 57, 20612079, doi:10.1175/1520-0469(2000)057<2061:DDDPOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., S. Lee, and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, doi:10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., S. E. Zebiak, and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 13711395, doi:10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Crum, T. D., and R. L. Alberty, 1993: The WSR-88D and the WSR-88D Operational Support Facility. Bull. Amer. Meteor. Soc., 74, 16691687, doi:10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 1985: Linear responses of a stratified tropical atmosphere to convective forcing. J. Atmos. Sci., 42, 19441959, doi:10.1175/1520-0469(1985)042<1944:LROAST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., H. H. Hendon, and R. A. Houze, 1984: Some implications of the mesoscale circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci., 41, 113121, doi:10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haynes, P. H., and M. E. McIntyre, 1987: On the evolution of vorticity and potential vorticity in the presence of diabatic heating and frictional or other forces. J. Atmos. Sci., 44, 828841, doi:10.1175/1520-0469(1987)044<0828:OTEOVA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hertenstein, R. F. A., and W. H. Schubert, 1991: Potential vorticity anomalies associated with squall lines. Mon. Wea. Rev., 119, 16631672, doi:10.1175/1520-0493(1991)119<1663:PVAAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Herzegh, P. H., and P. V. Hobbs, 1980: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. II: Warm-frontal clouds. J. Atmos. Sci., 37, 597611, doi:10.1175/1520-0469(1980)037<0597:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., T. J. Matejka, P. H. Herzegh, J. D. Locatelli, and R. A. Houze Jr., 1980: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. I: A case study of a cold front. J. Atmos. Sci., 37, 568596, doi:10.1175/1520-0469(1980)037<0568:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Homeyer, C. R., 2014: Formation of the enhanced-v infrared cloud top feature from high-resolution three-dimensional radar observations. J. Atmos. Sci., 71, 332348, doi:10.1175/JAS-D-13-079.1.

    • Search Google Scholar
    • Export Citation
  • Hopper, L. J., 2011: Investigations in southeast Texas precipitating storms: Modeled and observed characteristics, model sensitivities, and educational benefits. Ph.D. thesis, Texas A&M University, 141 pp. [Available online at http://repository.tamu.edu/bitstream/handle/1969.1/ETD-TAMU-2011-12-10341/HOPPER-DISSERTATION.pdf?sequence=2.]

  • Hopper, L. J., and C. Schumacher, 2009: Baroclinicity influences on storm divergence and stratiform rain: Subtropical upper-level disturbances. Mon. Wea. Rev., 137, 13381357, doi:10.1175/2008MWR2564.1.

    • Search Google Scholar
    • Export Citation
  • Hopper, L. J., and C. Schumacher, 2012: Modeled and observed variations in storm divergence and stratiform rain production in southeastern Texas. J. Atmos. Sci., 69, 11591181, doi:10.1175/JAS-D-11-092.1.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396409.

  • Houze, R. A., 1997: Stratiform precipitation in regions of convection: A meteorological paradox? Bull. Amer. Meteor. Soc., 78, 21792196, doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., S. A. Rutledge, T. J. Matejka, and P. V. Hobbs, 1981: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. III: Air motions and precipitation growth in a warm-frontal rainband. J. Atmos. Sci., 38, 639649, doi:10.1175/1520-0469(1981)038<0639:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., 1984: Partitioning tropical heat and moisture budgets into cumulus and mesoscale components: Implications for cumulus parameterization. Mon. Wea. Rev., 112, 15901601, doi:10.1175/1520-0493(1984)112<1590:PTHAMB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Joss, V. J., and A. Waldvogel, 1967: Ein spektrograph für niederschlagstropfen mit automatischer auswertung (A spectograph for precipitation drops with automatic evaluation). Pure Appl. Geophys., 68, 240246, doi:10.1007/BF00874898.

    • Search Google Scholar
    • Export Citation
  • Lin, J., B. Mapes, M. Zhang, and M. Newman, 2004: Stratiform precipitation, vertical heating profiles, and the Madden–Julian oscillation. J. Atmos. Sci., 61, 296309, doi:10.1175/1520-0469(2004)061<0296:SPVHPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., D. J. Perkey, and J. M. Fritsch, 1981: Evolution of upper tropospheric features during the development of a mesoscale convective complex. J. Atmos. Sci., 38, 16641674, doi:10.1175/1520-0469(1981)038<1664:EOUTFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 1993: Gregarious tropical convection. J. Atmos. Sci., 50, 20262037, doi:10.1175/1520-0469(1993)050<2026:GTC>2.0.CO;2.

  • Mapes, B. E., and R. A. Houze, 1993: An integrated view of the 1987 Australian monsoon and its mesoscale convective systems. II: Vertical structure. Quart. J. Roy. Meteor. Soc., 119, 733754, doi:10.1002/qj.49711951207.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and R. A. Houze, 1995: Diabatic divergence profiles in western Pacific mesoscale convective systems. J. Atmos. Sci., 52, 18071828, doi:10.1175/1520-0469(1995)052<1807:DDPIWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and J. Lin, 2005: Dopper radar observations of mesoscale wind divergence in regions of tropical convection. Mon. Wea. Rev., 133, 18081824, doi:10.1175/MWR2941.1.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, doi:10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Matejka, T. J., R. A. Houze Jr., and P. V. Hobbs, 1980: Microphysics and dynamics of clouds associated with mesoscale rainbands in extratropical cyclones. Quart. J. Roy. Meteor. Soc., 106, 2956, doi:10.1002/qj.49710644704.

    • Search Google Scholar
    • Export Citation
  • Ninomiya, K., 1971a: Dynamical analysis of outflow from tornado-producing thunderstorms as revealed by ATS III pictures. J. Appl. Meteor., 10, 275294, doi:10.1175/1520-0450(1971)010<0275:DAOOFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ninomiya, K., 1971b: Mesoscale modification of synoptic situations from thunderstorm development as revealed by ATS III and aerological data. J. Appl. Meteor., 10, 11031121, doi:10.1175/1520-0450(1971)010<1103:MMOSSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., and H. Jiang, 1990: A theory for long-lived mesoscale convective systems. J. Atmos. Sci., 47, 30673077, doi:10.1175/1520-0469(1990)047<3067:ATFLLM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sanders, F., 2005: Real front or baroclinic trough? Wea. Forecasting, 20, 647651, doi:10.1175/WAF846.1.

  • Schaefer, J. T., 1974: The life cycle of the dryline. J. Appl. Meteor., 13, 444449, doi:10.1175/1520-0450(1974)013<0444:TLCOTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schultz, D. M., 2004: Cold fronts with and without prefrontal wind shifts in the central United States. Mon. Wea. Rev., 132, 20402053, doi:10.1175/1520-0493(2004)132<2040:CFWAWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schumacher, C., R. A. Houze, and I. Kraucunas, 2004: The tropical dynamical response to latent heating estimates derived from the TRMM precipitation radar. J. Atmos. Sci., 61, 13411358, doi:10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shige, S., Y. N. Takayabu, S. Kida, W.-K. Tao, X. Zeng, C. Yokoyama, and T. L‘Ecuyer, 2009: Spectral retrieval of latent heating profiles from TRMM PR data. Part IV: Comparisons of lookup tables from two- and three-dimensional cloud-resolving model simulations. J. Climate, 22, 55775594, doi:10.1175/2009JCLI2919.1.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., R. A. Houze, and S. E. Yuter, 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 19782007, doi:10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 1996: Effects of persistent, midlatitude mesoscale regions of convection on the large-scale environment during the warm season. J. Atmos. Sci., 53, 35033527, doi:10.1175/1520-0469(1996)053<3503:EOPMMR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. L. Anderson, 2001: Is midlatitude convection an active or a passive player in producing global circulation patterns? J. Climate, 14, 22222237, doi:10.1175/1520-0442(2001)014<2222:IMCAAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., and K.-M. Lau, 1989: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part II: Structure and propagation of mobile wave-CISK modes and their modification by lower boundary forcings. J. Atmos. Sci., 46, 3756, doi:10.1175/1520-0469(1989)046<0037:OOLFOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., S. Lang, X. Zeng, S. Shige, and Y. Takayabu, 2010: Relating convective and stratiform rain to latent heating. J. Climate, 23, 18741893, doi:10.1175/2009JCLI3278.1.

    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., and H. V. Murphey, 2008: Airborne Doppler radar and sounding analysis of an oceanic cold front. Mon. Wea. Rev., 136, 14751491, doi:10.1175/2007MWR2241.1.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., E. S. Sarachik, and D. S. Battisti, 2000: Vertical structure of convective heating and the three-dimensional structure of the forced circulation on an equatorial beta plane. J. Atmos. Sci., 57, 21692187, doi:10.1175/1520-0469(2000)057<2169:VSOCHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., and R. A. Houze, 1997: Measurements of raindrop size distributions over the pacific warm pool and implications for ZR relations. J. Appl. Meteor., 36, 847867, doi:10.1175/1520-0450(1997)036<0847:MORSDO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, D.-L., and R. Harvey, 1995: Enhancement of extratropical cyclogenesis by a mesoscale convective system. J. Atmos. Sci., 52, 11071127, doi:10.1175/1520-0469(1995)052<1107:EOECBA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • View in gallery
    Fig. 1.

    Composite figures of representative cases for (a) cold frontal, (b) warm frontal, (c) deep convective upper-level disturbance (ULD), and (d) nondeep convective ULD storm classifications. All images are adapted from an online image archive maintained by the Mesoscale and Microscale Meteorology Division of NCAR. The blue and red frontal boundaries in (a),(b) represent surface cold and warm fronts, respectively.

  • View in gallery
    Fig. 2.

    Scatterplot of the estimated altitudes of peak convergence vs the fraction of convective rain between 32 and 56 km in range from the radar for all hours and storm classifications. Gray contours show a joint frequency distribution of the fraction of the total number of observations in 20% × 1 km bins (1% contours ranging from 2% to 5%). Red horizontal and vertical lines demarcate rain regions characterized as primarily convective, primarily stratiform, or indeterminate.

  • View in gallery
    Fig. 3.

    Three-dimensional composite NEXRAD WSR-88D observations of (a) column-maximum radar reflectivity and (b) a vertical cross section along in (a) for a storm system at 1710 UTC 3 Jun 2010 that is identified as indeterminate in ADRAD observations in Fig. 2. The location of ADRAD and a contour of 56 km in range from the radar location are shown by the black cross symbol and circle in the map, respectively. In (b), the boundaries of the 56-km ADRAD range are shown by the thick black tick marks along the bottom axis.

  • View in gallery
    Fig. 4.

    Mean (a) zonal wind speed, (b) meridional wind speed, (c) total horizontal wind speed, and (d) wind direction for cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines) storm classifications.

  • View in gallery
    Fig. 5.

    Seasonal distributions of the fraction of hourly observations used in divergence calculations for cold frontal (blue), warm frontal (red), deep convective upper-level disturbance (black), and nondeep convective upper-level disturbance (green) storm classifications. For each storm classification, the sum of all seasons equals 100%.

  • View in gallery
    Fig. 6.

    Frequency distributions of the level of nondivergence for (a) primarily convective, (b) above the midlevel convergence in primarily stratiform, and (c) indeterminate rain regions identified in Fig. 2 for each storm classification: cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines). For each distribution, LND altitudes are binned at a resolution of 1 km and centered at integer kilometers.

  • View in gallery
    Fig. 7.

    Profiles of mean storm divergence for (a) primarily convective, (b) primarily stratiform, and (c) indeterminate rain regions identified in Fig. 2 and (d) all rain regions for each storm classification: cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines). Note that the divergence scale is slightly different for (a). The number of hourly observations contributing to these mean profiles and related uncertainties are given in Table 1. For (a)–(c), mean divergence profiles are computed in relative altitude to the LND and scaled to the mean LND in each case. The divergence profiles in (d) are computed by weighting the scaled LND-relative profiles by frequency for each storm classification.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 148 40 12
PDF Downloads 59 23 9

Assessing the Applicability of the Tropical Convective–Stratiform Paradigm in the Extratropics Using Radar Divergence Profiles

Cameron R. HomeyerNational Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by Cameron R. Homeyer in
Current site
Google Scholar
PubMed
Close
,
Courtney SchumacherDepartment of Atmospheric Sciences, Texas A&M University, College Station, Texas

Search for other papers by Courtney Schumacher in
Current site
Google Scholar
PubMed
Close
, and
Larry J. Hopper Jr.Department of Atmospheric Sciences, School of Sciences, University of Louisiana at Monroe, Monroe, Louisiana

Search for other papers by Larry J. Hopper Jr. in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

Long-term radar observations from a subtropical location in southeastern Texas are used to examine the impact of storm systems with tropical or extratropical characteristics on the large-scale circulation. Climatological vertical profiles of the horizontal wind divergence are analyzed for four distinct storm classifications: cold frontal (CF), warm frontal (WF), deep convective upper-level disturbance (DC-ULD), and nondeep convective upper-level disturbances (NC-ULD). DC-ULD systems are characterized by weakly baroclinic or equivalent barotropic environments that are more tropical in nature, while the remaining classifications are representative of common midlatitude systems with varying degrees of baroclinicity. DC-ULD systems are shown to have the highest levels of nondivergence (LND) and implied diabatic heating maxima near 6 km, whereas the remaining baroclinic storm classifications have LND altitudes that are about 0.5–1 km lower. Analyses of climatological mean divergence profiles are also separated by rain regions that are primarily convective, stratiform, or indeterminate. Convective–stratiform separations reveal similar divergence characteristics to those observed in the tropics in previous studies, with higher altitudes of implied heating in stratiform rain regions, suggesting that the convective–stratiform paradigm outlined in previous studies is applicable in the midlatitudes. Divergence profiles that cannot be classified as primarily convective or stratiform are typically characterized by large regions of stratiform rain with areas of embedded convection of shallow to moderate extent (i.e., echo tops <10 km). These indeterminate profiles illustrate that, despite not being very deep and accounting for a relatively small fraction of a given storm system, convection dominates the vertical divergence profile and implied heating in these cases.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Cameron Homeyer, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80305. E-mail: chomeyer@ucar.edu

Abstract

Long-term radar observations from a subtropical location in southeastern Texas are used to examine the impact of storm systems with tropical or extratropical characteristics on the large-scale circulation. Climatological vertical profiles of the horizontal wind divergence are analyzed for four distinct storm classifications: cold frontal (CF), warm frontal (WF), deep convective upper-level disturbance (DC-ULD), and nondeep convective upper-level disturbances (NC-ULD). DC-ULD systems are characterized by weakly baroclinic or equivalent barotropic environments that are more tropical in nature, while the remaining classifications are representative of common midlatitude systems with varying degrees of baroclinicity. DC-ULD systems are shown to have the highest levels of nondivergence (LND) and implied diabatic heating maxima near 6 km, whereas the remaining baroclinic storm classifications have LND altitudes that are about 0.5–1 km lower. Analyses of climatological mean divergence profiles are also separated by rain regions that are primarily convective, stratiform, or indeterminate. Convective–stratiform separations reveal similar divergence characteristics to those observed in the tropics in previous studies, with higher altitudes of implied heating in stratiform rain regions, suggesting that the convective–stratiform paradigm outlined in previous studies is applicable in the midlatitudes. Divergence profiles that cannot be classified as primarily convective or stratiform are typically characterized by large regions of stratiform rain with areas of embedded convection of shallow to moderate extent (i.e., echo tops <10 km). These indeterminate profiles illustrate that, despite not being very deep and accounting for a relatively small fraction of a given storm system, convection dominates the vertical divergence profile and implied heating in these cases.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: Cameron Homeyer, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80305. E-mail: chomeyer@ucar.edu

1. Introduction

Atmospheric motions can occur, in part, because of the nonuniformity of diabatic heating processes associated with precipitating cloud systems. This concept is especially relevant in the tropics where variations in the vertical structure of heating play an important role in the large-scale circulation through the generation of potential vorticity anomalies. Houze (1982) observed that the maximum heating (composed of latent, radiative, and eddy sensible components) in stratiform rain regions occurs at higher altitudes than in convective rain regions. Hartmann et al. (1984) used this observation to show that the elevated heating profile associated with a mesoscale convective system (MCS) composed of both convective and stratiform precipitation regions was able to produce a much more realistic tropical dynamical response than a convective-only profile. Haynes and McIntyre (1987) and Mapes and Houze (1995) helped explain this result by showing that the generation of potential vorticity is directly proportional to the vertical gradient of the heating profile in convective systems in the tropics. Many studies since Hartmann et al. (1984) have examined how the vertical structure of tropical heating influences the dynamical response (e.g., DeMaria 1985; Sui and Lau 1989; Wu et al. 2000; Chiang et al. 2001; Schumacher et al. 2004).

Diabatic heating variations also play a role in the large-scale circulation in the midlatitudes, but their contributions are not as well quantified or understood. Beginning with Ninomiya (1971a), case studies have shown that midlatitude MCSs that cover large regions for extended periods or that occur in weakly baroclinic environments can have an active dynamic feedback with the larger-scale circulation through vertical variations in diabatic heating, especially in the trailing stratiform rain region of an MCS (e.g., Hertenstein and Schubert 1991). Many other midlatitude studies indicate that large convective systems affect the local large-scale environment by creating a large anticyclonic flow perturbation aloft, intensifying upper-level divergence, and enhancing the large-scale baroclinicity (Ninomiya 1971b; Maddox et al. 1981; Raymond and Jiang 1990; Zhang and Harvey 1995; Stensrud 1996). Stensrud and Anderson (2001) further argued that regions of persistent midlatitude convection can affect the hemispheric circulation and Chang et al. (2002) described how diabatic heating from condensational processes strengthens midlatitude storm tracks. In addition, horizontal variations in diabatic heating within frontal storm systems have been shown to strengthen frontogenesis (Bryan and Fritsch 2000; Wakimoto and Murphey 2008). However, very little research exists beyond individual case studies on the relationship between precipitating systems and the large-scale circulation outside of the tropics.

Hopper and Schumacher (2009, 2012) attempted to rectify this void by using a larger set of cases and a combination of mesoscale modeling and observations to analyze microphysical and dynamical differences between storms of varying organization [e.g., leading-line trailing-stratiform (LLTS) MCSs versus less organized systems] occurring in a range of synoptic conditions (i.e., warm season, weakly baroclinic, and strongly baroclinic) in southeastern Texas. Microphysical comparison metrics included stratiform rain production and vertical profiles of reflectivity, while dynamical metrics included vertical velocity and divergence profiles. Divergence is especially useful because it can be linked back to diabatic heating profiles (e.g., Mapes and Houze 1995) and is more easily observable than vertical velocity and diabatic heating. Hopper and Schumacher (2009, 2012) found that storms occurring in less baroclinic environments have more convective rain area, less stratiform rain production, and more elevated divergence profiles. The fidelity of the model simulations was dependent on the organization of the system, which was in turn dependent on the baroclinicity of the environment. Model results were also sensitive to the microphysics and convective parameterizations and whether convection was parameterized on an intermediate 9-km nested grid. Both studies also suggested that MCSs could have a significant feedback with the large-scale circulation at higher latitudes in both warm-season and weakly baroclinic environments.

This study uses long-term radar observations in southeastern Texas, which experiences a diverse spectrum of precipitating systems common in the tropics and midlatitudes, to evaluate the dynamical characteristics of storms based on their organization and synoptic forcing. This study’s overarching goal is to provide a more quantitative context for assessing mesoscale–synoptic interactions and the role precipitating systems may have in large-scale flow outside of the tropics. We do this by utilizing a multiyear subtropical dataset to observationally constrain the divergence (and implied heating profiles) that may be used in future studies. The results have implications for the modeling and forecasting of a variety of extratropical storms while also evaluating how applicable the convective–stratiform paradigm (see Houze 1997) is at higher latitudes.

2. Methods

a. Storm classifications

Four classifications are used to categorize storms depending on the primary mechanism responsible for initiating convective or nonconvective precipitation. Surface and upper-air maps, satellite images, and surface radar reflectivity images from the online archive maintained by the National Center for Atmospheric Research (NCAR) Mesoscale and Microscale Meteorology Division (online at http://www.mmm.ucar.edu/imagearchive/) have been matched with the independent radar data used in this study and analyzed to determine each storm’s classification. Archives of mesoscale discussions and mesoanalyses from the National Weather Service Storm Prediction Center and vertical cross sections of radar data are also used in classifying some storms. In this study, we consider that stratiform rain can originate from 1) deep, vertically oriented convective sources (deep convection) and 2) synoptic-scale lifting without a deep convective source that does not exclude slantwise, elevated, or shallow convection (nondeep convection). The composite schematics illustrated in Fig. 1 depict representative cases for these classifications whose broader spectrum of background environments are described below:

  • Cold frontal (CF; Fig. 1a) or trough precipitation initiates along a surface cold front associated with a midlatitude cyclone, dryline (Schaefer 1974), prefrontal trough or wind shift (Schultz 2004), baroclinic surface trough (Sanders 2005), or convectively induced outflow boundaries associated with a dissipating cold front. Deep convection along the leading edge of these boundaries is driven by strong surface convergence, but regions of nondeep convective precipitation associated with frontal lifting may also occur along the upper boundary of the frontal zone (e.g., Matejka et al. 1980; Hobbs et al. 1980).

  • Warm frontal (WF; Fig. 1b) precipitation typically includes widespread precipitation resulting from synoptic-scale frontal lifting or mesoscale updrafts on the cool side of an advancing surface warm front associated with a midlatitude cyclone (Matejka et al. 1980; Herzegh and Hobbs 1980; Houze et al. 1981). Embedded convection is typically of a slantwise or elevated nature, but deep convection may occur near and parallel to the surface warm front. Isentropic lifting and propagating shortwave troughs are also often present in these cases but are not a necessity.

  • Deep convective upper-level disturbance (DC-ULD; Fig. 1c) precipitation initiates in the presence of a stationary or propagating midlevel circulation (700–500-hPa closed low or shortwave trough) that does not have an associated surface cold or warm front. Precipitation in these cases may also form below jet streak circulations, but deep convection must be present for this classification that typically includes mesoscale, warm-season circulations.

  • Nondeep convective upper-level disturbance (NC-ULD; Fig. 1d) precipitation includes the subset of ULDs whose stratiform precipitation does not originate from a deep convective source and may include storms with slantwise or elevated convection. In these cases, precipitation results from synoptic-scale lifting associated with positive differential vorticity advection induced by midlevel circulations or jet streaks and occasionally in combination with weak warm air advection at middle levels.

Fig. 1.
Fig. 1.

Composite figures of representative cases for (a) cold frontal, (b) warm frontal, (c) deep convective upper-level disturbance (ULD), and (d) nondeep convective ULD storm classifications. All images are adapted from an online image archive maintained by the Mesoscale and Microscale Meteorology Division of NCAR. The blue and red frontal boundaries in (a),(b) represent surface cold and warm fronts, respectively.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

A few possible storm classifications have been combined with other storm types or omitted from analysis in this study. Named tropical cyclones are excluded because of their infrequency relative to other storm types. Discrete and/or weakly forced storms initiating from forcing mechanisms not described above (e.g., supercell thunderstorms, airmass thunderstorms, and sea-breeze convection) have also been omitted because their echo coverages are typically too low to generate reliable divergence estimates. Excluding tropical cyclones and discrete/weakly forced storms should not significantly alter climatological divergence profiles because they each account for only 2%–3% of annual rainfall over the radar domain, based on a climatology from March 2002 to February 2010 (Hopper 2011). In addition, stationary frontal storms are classified as either warm or cold frontal, depending on whether their convective elements propagate away from the cold front into the warm sector (i.e., cold frontal) or move parallel to the front or into the cool sector (i.e., warm frontal). Splitting stationary frontal storms and merging several types of surface boundaries into the cold frontal category simplifies the climatology while combining storm types that have similar dynamical and microphysical properties indicated by detailed analyses of radar divergence estimates shown in this study and disdrometer-based observations of drop size distributions (not shown).

In cases where multiple forcing mechanisms are present, frontal classifications are given precedence over ULD classifications unless there is a spatial break (>100 km) between precipitation associated with a ULD and the location where the frontal precipitating system initiated downstream. One example illustrating this occurrence is the NC-ULD depicted in Fig. 1d whose precipitation associated with large-scale lifting east of an upper-level low is clearly separated from precipitation that initiated along a warm front downstream from Louisiana to the Florida Panhandle (not shown). ULD classifications are also used if evaporative cooling associated with precipitation initially produced by an ULD induces the formation of a mesoscale baroclinic frontal zone that is not connected to a surface cyclone. Although some degree of subjectivity is inherent in qualitative classification systems, storms across the climatology have been crosschecked for internal consistency.

b. Radar observations and analysis

Climatological mean velocity–azimuth display (VAD) wind profiles and horizontal wind divergence are estimated for each storm classification using 4 yr of data (June 2006–July 2010) from Texas A&M University’s S-band Aggie Doppler Radar (ADRAD) located in College Station, Texas (30.6°N, 96.3°W). ADRAD typically performs full azimuthal scans at 24 unique elevation angles ranging from 0.4° to 29.5° every 12 min. The azimuthal scan rate for ADRAD is 18° s−1, so each 360° elevation scan takes 20 s to complete. An extensive description of ADRAD’s scan strategies, calibration shifts, and quality control methods utilized during this period are provided in Hopper and Schumacher (2012).

Hourly VAD wind and divergence profiles are computed using the technique of Browning and Wexler (1968) and following the methods in Mapes and Lin (2005) with some modifications outlined here. The results in this study are computed at a vertical resolution of 25 hPa, twice that of Mapes and Lin (2005), whose methods are subject to biases from nonuniformly distributed echo coverage in azimuth and at close ranges from the radar. In the method, divergence profiles are estimated at select ranges from the radar using radial velocity observations within 8-km bins centered on each annulus. These divergence calculations are representative of the dynamical characteristics of the entire storm system within the farthest range considered from the radar. In this study, we use divergence estimates at three of the aforementioned 8-km-wide annuli spanning 32–56 km in range in order to avoid biases in the divergence calculation at close ranges from the radar and to provide an estimate of the uncertainty of each hourly calculation. To avoid errors resulting from incomplete azimuthal coverage, we retain hours with storm area fractions ≥80% in the 32–56-km range space and at all altitudes up to 8 km for analysis. A threshold of 80% in storm area coverage was found to ensure near-concentric echo coverage from inspection of individual cases.

It is important to note here that adiabatic contributions to the divergence profiles (i.e., dry dynamics), which could impact their vertical structure and implied heating, are not diagnosed in this study. However, based on the scaling arguments given in Mapes and Houze (1995), such contributions to radar-observed divergence are likely negligible in most cases. In particular, significant adiabatic contributions will occur for MCSs with tropospheric temperature perturbations comparable to their latent heating rates (i.e., strongly baroclinic frontal systems) and low precipitation amounts (<1 cm), but the diabatic component of the divergence profile typically remains dominant in such cases (e.g., Hopper and Schumacher 2012). For the storm classifications in this study, NC-ULD systems would have the highest likelihood of meeting the low precipitation criteria. However, the storm coverage criteria used to limit the analysis of divergence profiles likely ensures that precipitation amounts are sufficiently large in all cases and thus further limits contributions from dry dynamics.

Radar-derived wind and divergence profiles are separated by the storm classifications given in section 2a and by the fraction of convective rainfall for analysis. Separating profiles by the fraction of convective area coverage rather than rainfall produces similar results. To identify convective and stratiform rain regions at each radar observation time, we follow methods outlined in Steiner et al. (1995) and Yuter and Houze (1997) and updated for southeastern Texas in Hopper and Schumacher (2012). Rainfall estimates for each radar observation are obtained using a well-documented method of regressing low elevation radar reflectivity Z against ground-based observations of rain rate R to develop a ZR relationship (e.g., Marshall and Palmer 1948). The ZR relationship has the form
e1
where the factors a and b are 177.3 and 1.66 in this study, respectively. We derive a and b using observations during the entire period of this study from ADRAD and ground-based drop size distributions from two Joss–Waldvogel (J-W) impact disdrometers (Joss and Waldvogel 1967) at about 4.9 and 165.9 km in range from the radar. Only rain periods with at least 100 drops and rain rates of at least 0.1 mm h−1 are used to compute the ZR regression.

The storm coverage criteria outlined above yield 359 h of ADRAD data for analysis. CF systems account for nearly half of the hourly observations (164), followed by DC-ULD (112), WF (48), and NC-ULD (35) systems. In addition, convective–stratiform separations and associated radar reflectivity-based rainfall estimations show that CF, DC-ULD, and WF systems have the highest climatological convective rain fractions (27%–38%), whereas NC-ULD systems show convective rain fractions <10% (see also Table 1). Because of decreasing storm coverage at higher altitudes from the radar, uncertainties in the hourly estimates of winds and divergence often become larger than their observed magnitudes. Therefore, the following analyses are restricted to altitudes below 10 km.

Table 1.

The number of hours, number of storms, convective rain fractions, and the 0–8-km mean standard deviations of hourly divergence estimates (σhr) and climatological mean divergence profiles (σm) for each storm classification and RRP: convective (C), stratiform (S), indeterminate (I), and the total (All).

Table 1.

It is well known that divergence profiles for convective and stratiform rain regions can be related to atmospheric heating and vertical motion through mass continuity. In particular, positive vertical gradients of divergence typically represent heating and ascent while negative gradients represent cooling and descent. Consequently, peak diabatic heating and cooling rates in the vertical are often observed near a level of nondivergence (LND, where divergence is 0) because LNDs are representative of the central altitude of a gradient layer. Convective rain regions are typically characterized by low-level convergence that transitions to divergence at upper levels in the detraining anvil layer (e.g., Mapes and Houze 1993), representing significant heating in the middle troposphere. Stratiform rain regions are characterized by low-level divergence, midlevel convergence coincident with the melting (freezing) level, and upper-level divergence. This vertical divergence structure for stratiform rain is associated with significant cooling in the lower troposphere and heating in the upper troposphere, which typically occurs at higher altitudes than in convective rain regions. Because of these significant differences in divergence (and heating), it is desirable to develop an understanding of the characteristic contributions from convective and stratiform rain regions. Although complex methods to wholly separate convective and stratiform contributions to divergence are useful [e.g., linear regressions of divergence profiles versus reflectivity-estimated surface rain rates in Mapes and Lin (2005)], contributions from convective and stratiform rain regions can be characterized through identification of the primary rain region (RRP) for each hourly observation.

We identify the RRP using a simple approach that compares the altitude of maximum convergence with the fraction of total rainfall from convective rain regions within 56 km in range of the radar for all analyzed hours and storm classifications (Fig. 2). Although this approach does not result in a pure separation of each rain region, it does isolate times in the life cycle of a storm that are dominated by convective or stratiform rain and thus presumably heating (e.g., Johnson 1984; Lin et al. 2004). The contoured joint frequency distribution in Fig. 2 clearly illustrates these convective and stratiform modes of rainfall and divergence. In particular, profiles dominated by stratiform rain correspond to a frequency maximum at altitudes of peak convergence from 2.5 to 6 km and convective rain fractions from 0% to 25%. Alternatively, profiles dominated by convective rain are illustrated by a frequency maximum at convective rain fractions from 40% to 80% and altitudes of peak convergence from 0 to 3 km. These observed modes in the relationship between convective rainfall and the altitude of maximum convergence allow us to characterize three RRP: convective (convective rainfall >40%), stratiform (convective rainfall <40% and maximum convergence altitudes above 2.5 km), and indeterminate (convective rainfall <40% and maximum convergence altitudes below 2.5 km). An altitude threshold of 2.5 km for convergence maxima here corresponds to the climatological altitude minimum of the melting level in southeastern Texas determined from 10 yr of radiosonde data near the radar domain (not shown). Analysis of each storm classification separately shows comparable altitude transitions to that identified in Fig. 2 (not shown). Although the identified stratiform and convective RRP follow expected relationships from previous studies, there are a significant number of observations with low altitudes of maximum convergence and low convective rain fractions that are not consistent with either predominately stratiform or convective processes, and they are therefore classified as indeterminate here.

Fig. 2.
Fig. 2.

Scatterplot of the estimated altitudes of peak convergence vs the fraction of convective rain between 32 and 56 km in range from the radar for all hours and storm classifications. Gray contours show a joint frequency distribution of the fraction of the total number of observations in 20% × 1 km bins (1% contours ranging from 2% to 5%). Red horizontal and vertical lines demarcate rain regions characterized as primarily convective, primarily stratiform, or indeterminate.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

Further inspection of indeterminate RRP profiles from Fig. 2 illustrates that contributing hourly observations are often characterized by predominantly stratiform rain regions throughout the ADRAD domain with embedded convection that the two-dimensional convective–stratiform separation algorithm used in this study has difficulty identifying accurately. These indeterminate profiles represent an often-complex mixture of convective and stratiform rain regions, though convection typically dominates the divergence structure, providing the low altitudes of maximum convergence observed. The vast majority of the convection during these indeterminate hours is relatively shallow (i.e., less than 10 km deep and vertical extent of radar reflectivity >35 dBZ less than 2 km above the melting level). However, slantwise and elevated convection that is also shallow but forms because of large-scale lifting instead of or in addition to gravitational instability may contribute in some cases as well.

Column-maximum radar reflectivity and a vertical cross section of radar reflectivity observations of a representative storm system with an indeterminate RRP are shown in Fig. 3. These radar observations are from three-dimensional composites of several radars from the Next Generation Weather Radar (NEXRAD) program Weather Surveillance Radar-1988 Doppler (WSR-88D) network (Crum and Alberty 1993) created following the methods outlined in Homeyer (2014). The vertical cross section (Fig. 3b), taken through the area within 56 km in range from ADRAD, shows the embedded shallow to moderate convection within the larger stratiform rain region. In addition to embedded convection in stratiform rain, there are a few indeterminate RRP profiles observed during hours of transition from convective to stratiform rain in LLTS MCS systems that show large fluctuations in divergence at lower altitudes. During these transition hours, convergence maxima at low altitudes are produced by descending midlevel rear inflow jets across the radar domain (e.g., see Fig. 5c in Hopper and Schumacher 2012). Although these profiles represent observations of systems with dynamical characteristics that differ from convection embedded in broad areas of stratiform rain, they account for less than 10% of the analyzed profiles and excluding them from the climatological analysis does not alter the results.

Fig. 3.
Fig. 3.

Three-dimensional composite NEXRAD WSR-88D observations of (a) column-maximum radar reflectivity and (b) a vertical cross section along in (a) for a storm system at 1710 UTC 3 Jun 2010 that is identified as indeterminate in ADRAD observations in Fig. 2. The location of ADRAD and a contour of 56 km in range from the radar location are shown by the black cross symbol and circle in the map, respectively. In (b), the boundaries of the 56-km ADRAD range are shown by the thick black tick marks along the bottom axis.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

3. Results

To examine the fidelity of the storm classifications used and the characteristic storm motion for each case, Fig. 4 shows VAD profiles of mean zonal, meridional, and total wind speeds and their direction. The near-surface wind directions for the CF (northwesterly) and WF (southeasterly) classifications are representative of their characteristic surface wind speeds near their respective boundaries and propagation for the CF cases in the study region. In addition, all storm classifications are characterized by southwesterly flow at middle and upper levels, except for the DC-ULD cases, which have nearly due southerly flow. There are also clear distinctions between the vertical structure of the wind profiles’ magnitudes for each storm classification. CF systems are characterized by strong upper-level winds (>8 km) and the deepest speed shear (from 0 to 8 km in Fig. 4c), whereas WF systems are characterized by the strongest winds and speed shear at middle levels (from 3 to 7 km). DC-ULD systems are characterized by strong speed shear in the boundary layer like CF and WF storms but contain the weakest winds at middle and upper levels that are weakly veering with height, representative of weakly baroclinic systems or tropical systems that in some cases may be equivalent barotropic with unidirectional weak winds. NC-ULD systems show total wind speed structures similar to WF systems with weaker magnitudes at middle and upper levels and have the weakest lower-level winds (<3 km) of all storm classifications. In addition, NC-ULD systems also have the weakest boundary layer shear and instead display strong speed shears at 3 km (~700 hPa). These contrasting features make sense considering most analyzed NC-ULD events are associated with upper-level jet streaks and/or midlevel shortwaves without organized surface fronts.

Fig. 4.
Fig. 4.

Mean (a) zonal wind speed, (b) meridional wind speed, (c) total horizontal wind speed, and (d) wind direction for cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines) storm classifications.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

Seasonal distributions of the number of hourly observations for each storm classification are shown in Fig. 5, and provide further support for the accuracy of the storm classifications used. All storm classifications show distributions consistent with the seasonality in their forcing mechanisms outlined in section 2a. CF and WF systems occur primarily in the transition and winter seasons, although CF storms still occur during summer whereas WF storms do not. NC-ULD systems occur during the transition and winter seasons with peak occurrence in fall and winter, when their primary forcing mechanisms are common in southeastern Texas. DC-ULD systems are dominated by events in the summer months, with all remaining events distributed in the transition seasons. The seasonality for DC-ULD events is consistent with their more tropical (or barotropic) characteristics.

Fig. 5.
Fig. 5.

Seasonal distributions of the fraction of hourly observations used in divergence calculations for cold frontal (blue), warm frontal (red), deep convective upper-level disturbance (black), and nondeep convective upper-level disturbance (green) storm classifications. For each storm classification, the sum of all seasons equals 100%.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

Storm systems in midlatitudes and the environment they occur within show large seasonality not observed in the tropics. As identified in long-term radiosonde observations near southeastern Texas discussed in section 2b, the observed seasonality in the melting level generally spans altitudes from about 2.5 km in the winter to 5.5 km in the summer. The tropopause also shows significant range from near 10 km in the cold season to near 16 km in the warm season. This seasonality directly affects the vertical extent of storms and their divergence structures. Figure 6 shows one aspect of this seasonality: frequency distributions of the LND for convective and indeterminate RRP and the upper LND for stratiform RRP for each storm classification. Because the LND in these distributions often implies the altitude of maximum diabatic heating, developing an understanding of its seasonality is an important consideration for accurately diagnosing the large-scale influence of each storm classification. For each RRP and storm classification, the range of LNDs span layers ≥5 km. In addition, the stratiform LND distributions for each storm classification (Fig. 6b) are at higher altitudes than convective RRP (Fig. 6a), consistent with previous studies in the tropics (e.g., Mapes and Houze 1993). CF systems show the broadest distribution of the LND for each RRP, frequently reaching lower altitudes than WF and DC-ULD systems. The stratiform LND distribution for NC-ULD systems shows a distinct peak at altitudes much lower than that observed for the remaining storm classifications, consistent with their typical occurrence in the cold season when the melting level (and consequently the LND) reaches its lowest altitude. LND distributions are centered at the lowest altitudes for indeterminate RRP (Fig. 6c), in agreement with the identification of relatively shallow convection as the primary source for their low levels of maximum convergence. In addition, there are some distributions in Fig. 6 that show two modes of LND altitude (e.g., WF convective RRP and NC-ULD indeterminate RRP). However, these bimodal distributions have some of the smallest sample sizes of any classification and RRP and may not represent physical characteristics other than the range of LND altitudes.

Fig. 6.
Fig. 6.

Frequency distributions of the level of nondivergence for (a) primarily convective, (b) above the midlevel convergence in primarily stratiform, and (c) indeterminate rain regions identified in Fig. 2 for each storm classification: cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines). For each distribution, LND altitudes are binned at a resolution of 1 km and centered at integer kilometers.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

Figure 7 shows climatological mean divergence profiles by storm classification for hours of convective, stratiform, and indeterminate RRP and the mean profile for all hours contributing to each classification. Because the freezing level and the LND show large seasonality for each storm classification and RRP, we compute mean divergence profiles in relative altitude to the LND for convective and indeterminate RRP and the upper LND for stratiform RRP. Computing mean profiles in relative altitude retains the characteristic structures and amplitudes of convergence and divergence, including vertical gradients through the LND, whereas computing them in native altitude broadens the otherwise sharp transitions, biases the mean LND, and increases the uncertainty in the climatological divergence profile. For individual profiles whose LND is higher than the mean LND, portions of the profile near the surface that are shifted below ground are removed.

Fig. 7.
Fig. 7.

Profiles of mean storm divergence for (a) primarily convective, (b) primarily stratiform, and (c) indeterminate rain regions identified in Fig. 2 and (d) all rain regions for each storm classification: cold frontal (blue lines), warm frontal (red lines), deep convective upper-level disturbance (black lines), and nondeep convective upper-level disturbance (green lines). Note that the divergence scale is slightly different for (a). The number of hourly observations contributing to these mean profiles and related uncertainties are given in Table 1. For (a)–(c), mean divergence profiles are computed in relative altitude to the LND and scaled to the mean LND in each case. The divergence profiles in (d) are computed by weighting the scaled LND-relative profiles by frequency for each storm classification.

Citation: Journal of Climate 27, 17; 10.1175/JCLI-D-13-00561.1

The RRP profiles in Fig. 7 are presented in relative altitude to the mean LND for each storm type (Figs. 7a–c), whereas the climatological mean of all observations for each storm classification shown in Fig. 7d is a frequency-weighted mean of the relative altitude means for each RRP. Mean uncertainties for the 0–8-km layer of the climatological mean divergence profiles (σm) and hourly divergence estimates (σhr), in addition to the number of hourly observations used and mean convective area fractions, are given in Table 1. Following the identification of RRP in Fig. 2, there are a similar number of stratiform and convective hours for each classification, while indeterminate hours account for the fewest number of hours in each case. In addition, σhr values are typically half as large as their respective σm values in each case, suggesting that the hourly divergence estimates are robust. Although uncertainties in σm are larger than σhr, they are generally smaller than the maxima in convergence and divergence in each profile.

For convective RRP divergence profiles (Fig. 7a), CF systems have the lowest LND altitude, in agreement with a cold or transition season surface-forced system. The LND is higher in convective profiles for WF and DC-ULD systems, with WF systems showing the largest observed magnitudes of peak convergence and divergence and DC-ULDs showing the smallest magnitudes. The vertical gradient of divergence (or slope) through the LND (and implied heating rates) is largest for the WF and CF systems and smallest for DC-ULD systems. Although the magnitude of the vertical gradient is lowest for the DC-ULD systems, their larger depth suggests that convective heating and the related dynamical response extends to higher altitudes than in CF and WF systems.

Climatological divergence profiles for stratiform RRP (Fig. 7b) also show distinct differences between storm classifications. WF and DC-ULD systems show the highest upper LND (~7 km) and levels of maximum convergence (~6 km), whereas the upper LND and maximum convergence for CF systems are at least 1 km lower, implying similar displacements in the altitude of maximum heating. As observed in the distributions of the stratiform upper LND in Fig. 6b, the NC-ULD systems show the lowest upper LND (~5 km) and altitude of maximum convergence (~4 km) of the mean stratiform RRP profiles. The strongest divergence aloft is also observed for NC-ULD systems, in agreement with frequent jet streak forcing. Similar to convective RRP profiles, DC-ULD systems for stratiform RRP show the weakest vertical gradient in divergence near the upper LND. Although WF systems show a slightly larger vertical gradient near the LND, the vertical depth of the gradient is less than DC-ULD systems and is the shallowest gradient of the four classifications (from 6 to 7.5 km). CF and NC-ULD systems show the strongest and deepest vertical divergence gradients in the stratiform RRP profiles, extending from about 4.5 to 7.5 km.

In addition to characteristics of the LND and implied heating structures, there are unique differences in the characteristics of the midlevel convergence layer within stratiform rain regions between storm classifications. CF systems are characterized by the strongest midlevel convergence with a sharp peak near 5 km, consistent with additional dynamical contribution from strong rear inflow jets in LLTS MCSs that are common in CF events. The maximum in midlevel convergence for WF systems is the broadest of any classification, spanning altitudes from 3.5 to 6 km. This large depth likely reflects instantaneous contributions from both classes of stratiform rain production in WF systems outlined in section 2a: production from surface-based and/or slantwise convection near or along the WF surface boundary responsible for the lower maximum and production from elevated convection farther north from the WF surface boundary responsible for the upper maximum. Midlevel convergence is concentrated at the highest altitudes in DC-ULD cases, implying that DC-ULDs have the deepest lower-tropospheric diabatic cooling and most elevated diabatic heating profiles.

Divergence profiles of indeterminate RRP (Fig. 7c) show moderate low-level convergence, with maxima between 1.5 and 2.5 km in altitude, transitioning to similar magnitudes of upper-level divergence first peaking between 4 and 5 km in altitude. As in the convective and stratiform RRP divergence profiles, DC-ULD systems show the highest LND at an altitude near 4 km, whereas the LNDs for the remaining storm classifications are more than 1 km lower. The low altitudes of peak convergence and the LND in the indeterminate profiles provide further evidence that they are primarily associated with shallow to moderate convection that is not adequately resolved by the two-dimensional convective–stratiform separation algorithm. However, contributions from stratiform rain regions may still be important considering that upper-level divergence estimates are relatively large and become nearly constant with altitude at middle levels. The vertical divergence gradients through the LND for all storm classifications in the indeterminate RRP profiles are comparable to the largest observed in any RRP, despite being limited to lower altitudes. This characteristic suggests that shallow to moderate convection may play an important role in the large-scale circulation at low levels in the extratropics, where the environmental circulations are commonly weaker than at upper levels.

In addition to examining the dynamical contributions from each RRP, the climatological mean divergence profiles for each storm classification (Fig. 7d) show their bulk characteristic influence in southeastern Texas. These profiles are computed by combining each RRP profile weighted by their fraction of hourly observations. Similar to the profiles separated by rain region, the climatological means for all profiles show that DC-ULD systems are characterized by the highest altitude of the LND and implied heating, with the LND for WF, CF, and NC-ULD systems about 0.5–1 km below DC-ULD systems. The climatological mean profile for NC-ULD systems shows two maxima in convergence: one below 2 km from the indeterminate climatology and the other above 4 km from the stratiform climatology. In addition, the vertical gradient for the NC-ULD systems is larger than the remaining systems through the LND, likely representative of the limited seasonality for NC-ULD systems compared to the remaining classifications. The magnitude of the vertical divergence gradient through the LND in the total climatological mean for the remaining classifications is largest in the CF systems, followed by WF and DC-ULD systems, while the vertical depth of the divergent layer is comparable. However, the altitudes of the mean LNDs and their implied heating maxima are still higher and concentrate more stratiform heating at upper levels (and lower-tropospheric cooling) in the DC-ULD cases compared to their more baroclinic counterparts. Therefore, these DC-ULD systems are likely most capable of producing adiabatically driven, slow-moving gravity waves in the subtropics that warm the upper troposphere and cool the lower troposphere, thus destabilizing the lower atmosphere and encouraging at least shallow expansive convection to develop nearby (e.g., Mapes 1993; Mapes and Houze 1995).

4. Summary and discussion

Significant differences in the dynamical characteristics of storms from four distinct forcing mechanisms are observed for a subtropical site in southeastern Texas. These four storm classifications are used to isolate influences from systems with midlatitude (baroclinic) and tropical (barotropic) characteristics. Cold frontal (CF) storms, warm frontal (WF) storms, and nondeep convective upper-level disturbances (NC-ULD) show association with strong mid- and upper-level winds and occur primarily during winter and transition seasons, consistent with characteristics of midlatitude systems. Deep convective upper-level disturbances (DC-ULD) that occur during the summer and transition seasons display weak horizontal wind speeds at all altitudes, characteristic of weakly baroclinic or more tropical systems.

A simple method was introduced to identify analysis times when divergence profiles are dominated by convective or stratiform rain regions. In addition to convective and stratiform classifications, an additional group of observations did not show characteristics in agreement with either classification and were labeled as indeterminate. Divergence profiles separated by storm classification and primary rain region (RRP) show characteristics similar to previous studies that fully separate convective and stratiform components and illustrate that the convective–stratiform paradigm is generally applicable at higher latitudes. In particular, stratiform RRP divergence profiles show higher altitudes of implied heating than convective RRP for all storm classifications, in agreement with previous studies in the tropics.

The observed differences and variability in divergence profiles for all storm classifications are largely dependent on the seasonality of the melting level and associated levels of nondivergence (LND) and convergence maxima. Climatological mean RRP divergence profiles were computed in relative altitude to the LND in order to remove biases introduced by the large seasonality in the LND in each case. In general, DC-ULD systems were shown to have the highest altitudes of implied heating for each RRP, typically followed in order by WF, CF, and NC-ULD systems. In addition, NC-ULD systems showed the largest divergence gradients through the LND in the stratiform RRP and total climatological mean profiles, reflective of their limited seasonality compared to the remaining storm classifications. Despite having a higher LND, the vertical gradient of divergence through the LND (and implied heating rates) in each case were smaller in DC-ULD systems than the remaining baroclinic storm classifications because the upper-tropospheric heating and lower-tropospheric cooling is distributed over a greater vertical depth.

Perhaps one of the more important results of this study is that divergence profiles often imply convective heating when the overwhelming majority of the observed rain region is stratiform. This characteristic is observed for all storm classifications and seasons in this study and is especially evident in mean profiles for indeterminate RRP. In particular, indeterminate profiles show a consistent structure across all storm classifications that is representative of relatively shallow and/or slantwise convection, with convergence maxima below 2.5 km in altitude and LND altitudes lower than both convective and stratiform RRP. In addition, the magnitude of divergence shows little variation from middle to upper levels, suggesting that stratiform rain regions may also contribute significantly to the divergence (and heating) profile in these situations. The convection of moderate vertical extent in this subtropical study may be comparable to the tropical cumulus congestus described in Johnson et al. (1999). However, its identification is hampered because it is often embedded in large stratiform regions, and variations due to the seasonality in environmental baroclinicity and melting levels require additional consideration. Therefore, further improvements to classifying convective and stratiform rain regions from radar reflectivity fields are likely warranted.

Finally, one limitation of this study is that only data from a single radar and a limited period of record are used. The extensive network of operational radars in the continental United States could provide a more detailed understanding of the seasonality and variability of these heating profiles in midlatitudes for baroclinic systems with similar characteristics. In particular, additional observational studies are needed to develop an understanding of the role convective heating plays when convection is embedded within larger regions of stratiform rain. Identifying relative contributions from shallow to moderate and deep convection in these storm systems will improve our understanding of the vertical distribution of divergence, diabatic heating, and related feedbacks to convection and the large-scale circulation. This topic is especially relevant to the recently launched National Aeronautics and Space Administration (NASA) Global Precipitation Measurement satellite mission, which extends the objectives of the NASA Tropical Rainfall Measuring Mission to higher latitudes, including the estimation of four-dimensional heating associated with precipitating systems (e.g., Shige et al. 2009; Tao et al. 2010). In addition, incorporating characteristic heating profiles in regional and global climate models similar to those in this study will lead to the improvement of forecasted storm systems and facilitate analysis of their large-scale dynamical influence in the extratropics.

Acknowledgments

We thank Brian Mapes and Jialin Lin for providing the radar VAD and divergence code and for helpful comments during the preparation of the manuscript; Aaron Funk at Texas A&M for providing ZR calculations for the ADRAD observations; and the three reviewers, whose comments helped to improve the manuscript. The first author thanks the Advanced Study Program (ASP) at NCAR for postdoctoral support. This research was also funded by National Science Foundation Grant ATM-0449782 to Texas A&M University.

REFERENCES

  • Browning, K. A., and R. Wexler, 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor., 7, 105113, doi:10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and J. M. Fritsch, 2000: Diabatically driven discrete propagation of surface fronts: A numerical analysis. J. Atmos. Sci., 57, 20612079, doi:10.1175/1520-0469(2000)057<2061:DDDPOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., S. Lee, and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, doi:10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chiang, J. C. H., S. E. Zebiak, and M. A. Cane, 2001: Relative roles of elevated heating and surface temperature gradients in driving anomalous surface winds over tropical oceans. J. Atmos. Sci., 58, 13711395, doi:10.1175/1520-0469(2001)058<1371:RROEHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Crum, T. D., and R. L. Alberty, 1993: The WSR-88D and the WSR-88D Operational Support Facility. Bull. Amer. Meteor. Soc., 74, 16691687, doi:10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • DeMaria, M., 1985: Linear responses of a stratified tropical atmosphere to convective forcing. J. Atmos. Sci., 42, 19441959, doi:10.1175/1520-0469(1985)042<1944:LROAST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hartmann, D. L., H. H. Hendon, and R. A. Houze, 1984: Some implications of the mesoscale circulations in tropical cloud clusters for large-scale dynamics and climate. J. Atmos. Sci., 41, 113121, doi:10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haynes, P. H., and M. E. McIntyre, 1987: On the evolution of vorticity and potential vorticity in the presence of diabatic heating and frictional or other forces. J. Atmos. Sci., 44, 828841, doi:10.1175/1520-0469(1987)044<0828:OTEOVA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hertenstein, R. F. A., and W. H. Schubert, 1991: Potential vorticity anomalies associated with squall lines. Mon. Wea. Rev., 119, 16631672, doi:10.1175/1520-0493(1991)119<1663:PVAAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Herzegh, P. H., and P. V. Hobbs, 1980: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. II: Warm-frontal clouds. J. Atmos. Sci., 37, 597611, doi:10.1175/1520-0469(1980)037<0597:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hobbs, P. V., T. J. Matejka, P. H. Herzegh, J. D. Locatelli, and R. A. Houze Jr., 1980: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. I: A case study of a cold front. J. Atmos. Sci., 37, 568596, doi:10.1175/1520-0469(1980)037<0568:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Homeyer, C. R., 2014: Formation of the enhanced-v infrared cloud top feature from high-resolution three-dimensional radar observations. J. Atmos. Sci., 71, 332348, doi:10.1175/JAS-D-13-079.1.

    • Search Google Scholar
    • Export Citation
  • Hopper, L. J., 2011: Investigations in southeast Texas precipitating storms: Modeled and observed characteristics, model sensitivities, and educational benefits. Ph.D. thesis, Texas A&M University, 141 pp. [Available online at http://repository.tamu.edu/bitstream/handle/1969.1/ETD-TAMU-2011-12-10341/HOPPER-DISSERTATION.pdf?sequence=2.]

  • Hopper, L. J., and C. Schumacher, 2009: Baroclinicity influences on storm divergence and stratiform rain: Subtropical upper-level disturbances. Mon. Wea. Rev., 137, 13381357, doi:10.1175/2008MWR2564.1.

    • Search Google Scholar
    • Export Citation
  • Hopper, L. J., and C. Schumacher, 2012: Modeled and observed variations in storm divergence and stratiform rain production in southeastern Texas. J. Atmos. Sci., 69, 11591181, doi:10.1175/JAS-D-11-092.1.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., 1982: Cloud clusters and large-scale vertical motions in the tropics. J. Meteor. Soc. Japan, 60, 396409.

  • Houze, R. A., 1997: Stratiform precipitation in regions of convection: A meteorological paradox? Bull. Amer. Meteor. Soc., 78, 21792196, doi:10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., S. A. Rutledge, T. J. Matejka, and P. V. Hobbs, 1981: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. III: Air motions and precipitation growth in a warm-frontal rainband. J. Atmos. Sci., 38, 639649, doi:10.1175/1520-0469(1981)038<0639:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., 1984: Partitioning tropical heat and moisture budgets into cumulus and mesoscale components: Implications for cumulus parameterization. Mon. Wea. Rev., 112, 15901601, doi:10.1175/1520-0493(1984)112<1590:PTHAMB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 23972418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Joss, V. J., and A. Waldvogel, 1967: Ein spektrograph für niederschlagstropfen mit automatischer auswertung (A spectograph for precipitation drops with automatic evaluation). Pure Appl. Geophys., 68, 240246, doi:10.1007/BF00874898.

    • Search Google Scholar
    • Export Citation
  • Lin, J., B. Mapes, M. Zhang, and M. Newman, 2004: Stratiform precipitation, vertical heating profiles, and the Madden–Julian oscillation. J. Atmos. Sci., 61, 296309, doi:10.1175/1520-0469(2004)061<0296:SPVHPA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maddox, R. A., D. J. Perkey, and J. M. Fritsch, 1981: Evolution of upper tropospheric features during the development of a mesoscale convective complex. J. Atmos. Sci., 38, 16641674, doi:10.1175/1520-0469(1981)038<1664:EOUTFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., 1993: Gregarious tropical convection. J. Atmos. Sci., 50, 20262037, doi:10.1175/1520-0469(1993)050<2026:GTC>2.0.CO;2.

  • Mapes, B. E., and R. A. Houze, 1993: An integrated view of the 1987 Australian monsoon and its mesoscale convective systems. II: Vertical structure. Quart. J. Roy. Meteor. Soc., 119, 733754, doi:10.1002/qj.49711951207.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and R. A. Houze, 1995: Diabatic divergence profiles in western Pacific mesoscale convective systems. J. Atmos. Sci., 52, 18071828, doi:10.1175/1520-0469(1995)052<1807:DDPIWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mapes, B. E., and J. Lin, 2005: Dopper radar observations of mesoscale wind divergence in regions of tropical convection. Mon. Wea. Rev., 133, 18081824, doi:10.1175/MWR2941.1.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, doi:10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Matejka, T. J., R. A. Houze Jr., and P. V. Hobbs, 1980: Microphysics and dynamics of clouds associated with mesoscale rainbands in extratropical cyclones. Quart. J. Roy. Meteor. Soc., 106, 2956, doi:10.1002/qj.49710644704.

    • Search Google Scholar
    • Export Citation
  • Ninomiya, K., 1971a: Dynamical analysis of outflow from tornado-producing thunderstorms as revealed by ATS III pictures. J. Appl. Meteor., 10, 275294, doi:10.1175/1520-0450(1971)010<0275:DAOOFT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ninomiya, K., 1971b: Mesoscale modification of synoptic situations from thunderstorm development as revealed by ATS III and aerological data. J. Appl. Meteor., 10, 11031121, doi:10.1175/1520-0450(1971)010<1103:MMOSSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Raymond, D. J., and H. Jiang, 1990: A theory for long-lived mesoscale convective systems. J. Atmos. Sci., 47, 30673077, doi:10.1175/1520-0469(1990)047<3067:ATFLLM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sanders, F., 2005: Real front or baroclinic trough? Wea. Forecasting, 20, 647651, doi:10.1175/WAF846.1.

  • Schaefer, J. T., 1974: The life cycle of the dryline. J. Appl. Meteor., 13, 444449, doi:10.1175/1520-0450(1974)013<0444:TLCOTD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schultz, D. M., 2004: Cold fronts with and without prefrontal wind shifts in the central United States. Mon. Wea. Rev., 132, 20402053, doi:10.1175/1520-0493(2004)132<2040:CFWAWP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Schumacher, C., R. A. Houze, and I. Kraucunas, 2004: The tropical dynamical response to latent heating estimates derived from the TRMM precipitation radar. J. Atmos. Sci., 61, 13411358, doi:10.1175/1520-0469(2004)061<1341:TTDRTL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Shige, S., Y. N. Takayabu, S. Kida, W.-K. Tao, X. Zeng, C. Yokoyama, and T. L‘Ecuyer, 2009: Spectral retrieval of latent heating profiles from TRMM PR data. Part IV: Comparisons of lookup tables from two- and three-dimensional cloud-resolving model simulations. J. Climate, 22, 55775594, doi:10.1175/2009JCLI2919.1.

    • Search Google Scholar
    • Export Citation
  • Steiner, M., R. A. Houze, and S. E. Yuter, 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor., 34, 19782007, doi:10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., 1996: Effects of persistent, midlatitude mesoscale regions of convection on the large-scale environment during the warm season. J. Atmos. Sci., 53, 35033527, doi:10.1175/1520-0469(1996)053<3503:EOPMMR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stensrud, D. J., and J. L. Anderson, 2001: Is midlatitude convection an active or a passive player in producing global circulation patterns? J. Climate, 14, 22222237, doi:10.1175/1520-0442(2001)014<2222:IMCAAO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sui, C.-H., and K.-M. Lau, 1989: Origin of low-frequency (intraseasonal) oscillations in the tropical atmosphere. Part II: Structure and propagation of mobile wave-CISK modes and their modification by lower boundary forcings. J. Atmos. Sci., 46, 3756, doi:10.1175/1520-0469(1989)046<0037:OOLFOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Tao, W.-K., S. Lang, X. Zeng, S. Shige, and Y. Takayabu, 2010: Relating convective and stratiform rain to latent heating. J. Climate, 23, 18741893, doi:10.1175/2009JCLI3278.1.

    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., and H. V. Murphey, 2008: Airborne Doppler radar and sounding analysis of an oceanic cold front. Mon. Wea. Rev., 136, 14751491, doi:10.1175/2007MWR2241.1.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., E. S. Sarachik, and D. S. Battisti, 2000: Vertical structure of convective heating and the three-dimensional structure of the forced circulation on an equatorial beta plane. J. Atmos. Sci., 57, 21692187, doi:10.1175/1520-0469(2000)057<2169:VSOCHA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., and R. A. Houze, 1997: Measurements of raindrop size distributions over the pacific warm pool and implications for ZR relations. J. Appl. Meteor., 36, 847867, doi:10.1175/1520-0450(1997)036<0847:MORSDO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, D.-L., and R. Harvey, 1995: Enhancement of extratropical cyclogenesis by a mesoscale convective system. J. Atmos. Sci., 52, 11071127, doi:10.1175/1520-0469(1995)052<1107:EOECBA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
Save