Radar-Derived Estimates of Latent Heating in the Subtropics

Tina J. Cartwright Department of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by Tina J. Cartwright in
Current site
Google Scholar
PubMed
Close
and
Peter S. Ray Department of Meteorology, The Florida State University, Tallahassee, Florida

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

Abstract

Atmospheric warming from cloud heating has a major affect on worldwide atmospheric circulations and climate. Studies have shown that the dominant source for cloud heating is the phase change of water. The location and magnitude of cloud heating has a substantial impact on atmospheric circulations. Therefore, identifying the location of phase changes provides information necessary for accurate modeling of atmospheric circulations and climate.

Radar reflectivity is a signature predominantly produced from rain formed from condensation, the primary process that produces heating. Through the application of principal component analysis on a nonhydrostatic cloud model, heating, and derived reflectivity data, a technique to illustrate a future heating algorithm capable of estimating cloud heating from reflectivity data is examined. Formative, intensifying, and mature stages of a Convection and Precipitation Electrification Experiment squall-type convective system were used to demonstrate these results. The accuracy of the technique’s estimates for the mean convective and stratiform profiles to within 1.0 K h−1 on average throughout the vertical column shows the merit of this statistical technique. The use of this type of technique in conjunction with the network of NEXRAD and spaceborne radars could provide valuable data for applications ranging from cumulus parameterization to 4D data assimilation and model initialization.

Corresponding author address: Dr. Peter Ray, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520.

Email: pray@huey.met.fsu.edu

Abstract

Atmospheric warming from cloud heating has a major affect on worldwide atmospheric circulations and climate. Studies have shown that the dominant source for cloud heating is the phase change of water. The location and magnitude of cloud heating has a substantial impact on atmospheric circulations. Therefore, identifying the location of phase changes provides information necessary for accurate modeling of atmospheric circulations and climate.

Radar reflectivity is a signature predominantly produced from rain formed from condensation, the primary process that produces heating. Through the application of principal component analysis on a nonhydrostatic cloud model, heating, and derived reflectivity data, a technique to illustrate a future heating algorithm capable of estimating cloud heating from reflectivity data is examined. Formative, intensifying, and mature stages of a Convection and Precipitation Electrification Experiment squall-type convective system were used to demonstrate these results. The accuracy of the technique’s estimates for the mean convective and stratiform profiles to within 1.0 K h−1 on average throughout the vertical column shows the merit of this statistical technique. The use of this type of technique in conjunction with the network of NEXRAD and spaceborne radars could provide valuable data for applications ranging from cumulus parameterization to 4D data assimilation and model initialization.

Corresponding author address: Dr. Peter Ray, Department of Meteorology, The Florida State University, Tallahassee, FL 32306-4520.

Email: pray@huey.met.fsu.edu

Save
  • Chong, M., and D. Hauser, 1990: A tropical squall line observed during the COPT 81 experiment in West Africa. Part III: Heat and moisture budgets. Mon. Wea. Rev.,118, 1696–1706.

  • Churchill, D. D., and R. A. Houze Jr., 1984: Development and structure of winter monsoon cloud cluster on 10 December 1978. J. Atmos. Sci.,41, 933–960.

  • Frank, W. M., and J. L. M. McBride, 1989: The vertical distribution of heating in AMEX and GATE cloud clusters. J. Atmos. Sci.,46, 3464–3478.

  • Gallus, W. A., and R. H. Johnson, 1991: Heat and moisture budgets of an intense mid latitude squall line. J. Atmos. Sci.,48, 122–146.

  • Gamache, J. F., and R. A. Houze Jr., 1982: Mesoscale air motions associated with a tropical squall line. Mon. Wea. Rev.,110, 118–135.

  • Greco, S., J. Scala, J. B. Halverson, H. L. Massie Jr., W.-K. Tao, and M. Garstang, 1994: Amazon coastal squall lines. Part II: Heat and moisture transports. Mon. Wea. Rev.,122, 623–635.

  • Halverson, J., M. Garstang, J. Scala, and W.-K. Tao, 1996: Water and energy budgets of a Florida mesoscale convective system: A combined observational and modeling study. Mon. Wea. Rev.,124, 1161–1180.

  • Houze, R. A., Jr., 1973: A climatological study of vertical transports by cumulus-scale convection. J. Atmos. Sci.,30, 1112–1123.

  • ——, 1982: Cloud clusters and large-scale vertical motions in the Tropics. J. Meteor. Soc. Japan,60, 851–859.

  • ——, 1989: Observed structure of mesoscale convective systems and implications for large-scale heating. Quart. J. Roy. Meteor. Soc.,115, 425–461.

  • ——, 1993: Cloud Dynamics. Academic Press, 573 pp.

  • Johnson, R. H., and G. S. Young, 1983: Heat and moisture budgets of tropical mesoscale anvil clouds. J. Atmos. Sci.,40, 2138–2147.

  • ——, and J. F. Bresch, 1991: Diagnosed characteristics of precipitation systems over Taiwan during the May–June 1987 TAMEX. Mon. Wea. Rev.,119, 2540–2557.

  • Joliffe, I. T., 1986: Principal Component Analysis. Springer-Verlag, 271 pp.

  • Krueger, S. K., 1988: Numerical simulation of tropical cumulus clouds and their interaction with the subcloud layer. J. Atmos. Sci.,45, 2221–2250.

  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Climate Appl. Meteor.,22, 1065–1092.

  • Riehl, H., and J. S. Malkus, 1958: On the heat balance in the equatorial trough zone. Geophysica,6, 503–535.

  • ——, and J. Simpson, 1979: The heat balance of the equatorial trough zone, revisited. Beitr. Phys. Atmos.,52, 287–305.

  • Simpson, J., 1988: Tropical Rainfall Measuring Mission (TRMM): A satellite mission to measure Tropical rainfall. Report of the Science Steering Group, 94 pp. [Available from NASA Publications, Government Printing Office, Washington, DC 20402.].

  • ——, R. F. Adler, and G. R. North, 1988: A proposed satellite tropical rainfall measuring mission (TRMM). Bull. Amer. Meteor. Soc.,69, 278–295.

  • Smith, P. L., 1984: Equivalent radar reflectivity factors for snow and ice particles. J. Climate Appl. Meteor.,23, 1258–1260.

  • Smith, P. L., Jr., C. G. Myers, and H. D. Orville, 1975: Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation. J. Appl. Meteor.,14, 1156–1165.

  • Soong, S.-T., and W.-K. Tao, 1980: Response of deep tropical clouds to mesoscale processes. J. Atmos. Sci.,37, 2035–2050.

  • Steiner, M., and R. A. Houze Jr., 1993: Three-dimensional validation at TRMM ground truth sites: Some early results from Darwin, Australia. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 417–423.

  • ——, ——, and S. E. Yuter, 1995: Climatological characterization of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteor.34, 1978–2007.

  • Tao, W.-K., 1983: A numerical study of the structure and vertical transport properties of a tropical convective system. Ph. D. dissertation, University of Illinois, 228 pp. [Available from Dept. of Atmospheric Sciences, University of Illinois, 105 S. Gregory St., Urbana, IL 61801.].

  • ——, and S.-T. Soong, 1986: A study of the response of deep tropical clouds to mesoscale processes: Three-dimensional numerical experiments. J. Atmos. Sci.,43, 2653–2676.

  • ——, and J. Simpson, 1989: Modeling study of a tropical squall-type convective line. J. Atmos. Sci.,46, 177–202.

  • ——, and ——, 1993: The Goddard Cumulus Ensemble Model. Part I: Model description. Terr. Atmos. Oceanic Sci.,4, 35–72.

  • ——, ——, S. Lang, M. McCumber, R. Adler, and R. Penc, 1990: An algorithm to estimate the heating budget from vertical hydrometeor profiles. J. Appl. Meteor.,29, 1232–1244.

  • ——, S. Lang, J. Simpson, and R. Adler, 1993: Retrieval algorithms for estimating the vertical profiles of latent heat release: Their applications for TRMM. J. Meteor. Soc. Japan,71, 685–700.

  • Witt, A., and J. T. Johnson, 1993: An enhanced storm cell identification and tracking algorithm. Preprints, 26th Int. Conf. on Radar Meteorology, Norman, OK, Amer. Meteor. Soc., 417–423.

  • Yanai, M., S. Esbensen, and J. H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci.,30, 611–627.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 82 23 3
PDF Downloads 57 11 3