Editorial Type:
Article
Article Type:
Research Article

Dynamical and Microphysical Retrievals from Doppler Radar Observations of a Deep Convective Cloud

Bing Wu Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Bing Wu in
Current site
Google Scholar
PubMed Close
,
Johannes Verlinde Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania

Search for other papers by Johannes Verlinde in
Current site
Google Scholar
PubMed Close
, and
Juanzhen Sun National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by Juanzhen Sun in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jan 2000
DOI:
https://doi.org/10.1175/1520-0469(2000)057<0262:DAMRFD>2.0.CO;2
Page(s):
262–283
Restricted access

Abstract

A four-dimensional variational data assimilation system consisting of a three-dimensional time-dependent cloud model with both liquid and ice phase microphysics parameterization was used to assimilate radar data into a cloud model. Data of a severe thunderstorm observed during the Cooperative Huntsville Meteorological Experiment project were assimilated and results compared to a conventional analysis. The analysis system was able to retrieve all the prominent features of the storm, but differed in some of the details. However, the consistency of this retrieval dataset lent credence to the results.

It was found that the algorithm was very sensitive to several coefficients in the microphysical and turbulence parameterizations. Simulations proved to be unable to reproduce the evolution of the observed storm even with parameterization coefficients set at values that produce reasonable storm evolutions. This result has implications for short-range forecasting of convective events. Such forecasts require initial fields that currently can only be derived from observations such as used in this study. The problems with assimilating radar observations point to additional work to design parameterizations that allow models to more accurately simulate actual observed storms.

Corresponding author address: Dr. Hans Verlinde, Department of Meteorology, 503 Walker Building, The Pennsylvania State University, University Park, PA 16802-5013.

Email: verlinde@essc.psu.edu.

Abstract

A four-dimensional variational data assimilation system consisting of a three-dimensional time-dependent cloud model with both liquid and ice phase microphysics parameterization was used to assimilate radar data into a cloud model. Data of a severe thunderstorm observed during the Cooperative Huntsville Meteorological Experiment project were assimilated and results compared to a conventional analysis. The analysis system was able to retrieve all the prominent features of the storm, but differed in some of the details. However, the consistency of this retrieval dataset lent credence to the results.

It was found that the algorithm was very sensitive to several coefficients in the microphysical and turbulence parameterizations. Simulations proved to be unable to reproduce the evolution of the observed storm even with parameterization coefficients set at values that produce reasonable storm evolutions. This result has implications for short-range forecasting of convective events. Such forecasts require initial fields that currently can only be derived from observations such as used in this study. The problems with assimilating radar observations point to additional work to design parameterizations that allow models to more accurately simulate actual observed storms.

Corresponding author address: Dr. Hans Verlinde, Department of Meteorology, 503 Walker Building, The Pennsylvania State University, University Park, PA 16802-5013.

Email: verlinde@essc.psu.edu.

Save
Cover Journal of the Atmospheric Sciences
Journal of the Atmospheric Sciences

  • Brandes, E. A., J. Vivekanandan, J. D. Tuttle, and C. J. Kessinger, 1995: A study of thunderstorm microphysics with multiparameter radar and aircraft observations. Mon. Wea. Rev.,123, 3129–3143.

  • Bringi, V. N., and A. Hendry, 1990: Technology of polarization diversity radars in meteorology. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 153–190.

  • Byers, H. R., and R. R. Braham Jr., 1949: The Thunderstorm. U.S. Government Printing Office, 287 pp. [Available from U.S. Government Printing Office, Washington, DC 20402.].

  • Dodge, J., J. Arnold, G. Wilson, J. Evans, and T. T. Fujita, 1986: The Cooperative Huntsville Meteorological Experiment (COHMEX). Bull. Amer. Meteor. Sci.,67, 417–419.

  • Fujita, T. T., and P. G. Black, 1988: Monrovia microburst of 20 July 1986: A study of “SST.” Preprints, 15th Conf. on Severe Local Storms, Baltimore, MD, Amer. Meteor. Soc., 380–383.

  • Gal-Chen, T., 1978: A method for the initialization of the anelastic equations: Implications for matching models with observations. Mon. Wea. Rev.,106, 587–606.

  • Hane, C. E., and P. S. Ray, 1985: Pressure and buoyancy fields derived from Doppler radar data in a tornadic thunderstorm. J. Atmos. Sci.,42, 18–35.

  • Hauser, D., F. Roux, and P. Amayenc, 1988: Comparison of two methods for the retrieval of thermodynamic and microphysical variables from Doppler radar measurements: Application to the case of a tropical squall line. J. Atmos. Sci.,45, 1285–1303.

  • Jameson, A. R., and D. B. Johnson, 1990: Cloud microphysics and radar. Radar in Meteorology, D. Atlas, Ed., Amer. Meteor. Soc., 323–340.

  • Kapitza, H., 1991: Numerical experiments with the adjoint of a nonhydrostatic mesoscale model. Mon. Wea. Rev.,119, 2993–3011.

  • Kingsmill, D. E., and R. M. Wakimoto, 1991: Kinematic, dynamic, and thermodynamic analysis of a weakly sheared severe thunderstorm over northern Alabama. Mon. Wea. Rev.,119, 262–297.

  • Knupp, K. R., and W. R. Cotton, 1987: Internal structure of a small mesoscale convective system. Mon. Wea. Rev.,115, 629–645.

  • Laroche, S., and I. Zawadzki, 1994: A variational analysis method for retrieval of three-dimensional wind field from single-Doppler radar data. J. Atmos. Sci.,51, 2664–2684.

  • Lewis, J. M., and J. C. Derber, 1985: The use of adjoint equations to solve a variational adjustment problem with advective constraints. Tellus,37A, 309–322.

  • 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, 1065–1089.

  • Liu, D. C., and J. Nocedal, 1989: On the limited memory BFGS method for large-scale optimization. Math. Programming,45, 503–528.

  • Navon, I. M., X. Zou, J. Derber, and J. Sela, 1992: Variational data assimilation with an adiabatic version of the NMC spectral model. Mon. Wea. Rev.,120, 1433–1446.

  • Orville, H. D., and F. J. Kopp, 1977: Numerical simulation of the life history of a hailstorm. J. Atmos. Sci.,34, 1596–1618.

  • Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in Fortran 77: The Art of Scientific Computing. Cambridge University Press, 933 pp.

  • Proctor, F. H., 1989: Numerical simulations of an isolated microburst. Part II: Sensitivity experiments. J. Atmos. Sci.,46, 2143–2165.

  • Qiu, C.-J., and Q. Xu, 1992: A simple adjoint method of wind analysis for single-Doppler data. J. Atmos. Oceanic Technol.,9, 588–598.

  • Roberts, R. D., and J. W. Wilson, 1989: A proposed microburst nowcasting procedure using single-Doppler radar. J. Appl. Meteor.,28, 285–303.

  • Seliga, T. A., K. Aydin, and H. Direskeneli, 1986: Disdrometer measurements during an intense rainfall event in central Illinois: Implications for differential reflectivity radar observations. J. Climate Appl. Meteor.,25, 835–846.

  • Shapiro, A., S. Ellis, and J. Shaw, 1995: Single-Doppler velocity retrievals with Phoenix II data: Clear air and microburst wind retrievals in the planetary boundary layer. J. Atmos. Sci.,52, 1265–1287.

  • Straka, J. M., and J. R. Anderson, 1993: Numerical simulations of microburst-producing storms: Some results from storms observed during COHMEX. J. Atmos. Sci.,50, 1329–1348.

  • Sun, J., and A. Crook, 1994: Wind and thermodynamic retrieval from single-Doppler measurements of a gust front observed during Phoenix II. Mon. Wea. Rev.,122, 1075–1091.

  • ——, and ——, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci.,54, 1642–1661.

  • ——, and ——, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci.,55, 835–852.

  • ——, D. Flicker, and D. Lilly, 1991: Recovery of three-dimensional wind and temperature fields from simulated single-Doppler radar data. J. Atmos. Sci,48, 876–890.

  • Talagrand, O., and P. Courtier, 1987: Variational assimilation of meteorological observations with the adjoint vorticity equation. I: Theory. Quart. J. Roy. Meteor. Soc.,113, 1311–1328.

  • Tripoli, G. J., and W. R. Cotton, 1981: The use of ice–liquid water potential temperature as a thermodynamic variable in deep atmospheric models. Mon. Wea. Rev.,109, 1094–1102.

  • Tuttle, J. D., V. N. Bringi, H. D. Orville, and F. J. Kopp, 1989: Multiparameter radar study of a microburst: Comparisons with model results. J. Atmos. Sci.,46, 601–620.

  • Verlinde, J., and W. R. Cotton, 1993: Fitting microphysical observations of nonsteady convective clouds to a numerical model: An application of the adjoint technique of data assimilation to a kinematic model. Mon. Wea. Rev.,121, 2776–2793.

  • Vivekanandan, J., R. Raghavan, and V. N. Bringi, 1993: Polimetric radar modeling of mixtures of precipitation particles. IEEE Trans. Geosci. Remote Sens.,31, 1017–1034.

  • Vukicevic, T., and R. M. Errico, 1993: Linearization and adjoint of parameterized moist diabatic processes. Tellus,45A, 493–510.

  • Wakimoto, R. M., and V. N. Bringi, 1988: Dual-polarization observations of microbursts associated with intense convection: The 20 July storm during the MIST project. Mon. Wea. Rev.,116, 1521–1539.

  • Wolfsberg, D. G., 1987: Retrieval of three-dimensional wind and temperature fields from single-Doppler radar data. Ph.D. thesis, University of Oklahoma, 91 pp. [Available from Cooperative Institute for Mesoscale Meteorological Studies, 815 Jenkins, Norman, OK 73019.].

  • Xu, Q., 1996: Generalized adjoint for physical processes with parameterized discontinuities. Part I: Basic issues and heuristic examples. J. Atmos. Sci.,53, 1123–1142.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 290 73 9
PDF Downloads 146 50 6