Application of TRMM PR and TMI Measurements to Assess Cloud Microphysical Schemes in the MM5 for a Winter Storm

Mei Han Goddard Earth Sciences and Technology Center, University of Maryland, Baltimore County, Baltimore, and Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Mei Han in
Current site
Google Scholar
PubMed
Close
,
Scott A. Braun Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Scott A. Braun in
Current site
Google Scholar
PubMed
Close
,
William S. Olson Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, and Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by William S. Olson in
Current site
Google Scholar
PubMed
Close
,
P. Ola G. Persson Cooperative Institute for Research in Environmental Sciences, University of Colorado, and NOAA/Earth System Research Laboratory, Boulder, Colorado

Search for other papers by P. Ola G. Persson in
Current site
Google Scholar
PubMed
Close
, and
Jian-Wen Bao NOAA/Earth System Research Laboratory, Boulder, Colorado

Search for other papers by Jian-Wen Bao in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This paper uses observations from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; version 3.7.4) for a wintertime frontal precipitation system over the eastern Pacific Ocean. By incorporating a forward radiative transfer model, the radar reflectivity and brightness temperatures are simulated and compared with the observations at PR and TMI frequencies. The main purpose of this study is to identify key differences among the five schemes [including Simple ice, Reisner1, Reisner2, Schultz, and Goddard Space Flight Center (GSFC) microphysics scheme] in the MM5 that may lead to significant departures of simulated precipitation properties from both active (PR) and passive (TMI) microwave observations. Radiative properties, including radar reflectivity, attenuation, and scattering in precipitation liquid and ice layers are investigated. In the rain layer, most schemes are capable of reproducing the observed radiative properties to a reasonable degree; the Reisner2 simulation, however, produces weaker reflectivity and stronger attenuation than the observations, which is possibly attributable to the larger intercept parameter (N0r) applied in this run. In the precipitation ice layer, strong evidence regarding the differences in the microphysical and radiative properties between a narrow cold-frontal rainband (NCFR) and a wide cold-frontal rainband (WCFR) within this frontal precipitation system is found. The performances of these schemes vary significantly on simulating the microphysical and radiative properties of the frontal rainband. The GSFC scheme shows the least bias, while the Reisner1 scheme has the largest bias in the reflectivity comparison. It appears more challenging for the model to replicate the scattering signatures obtained by the passive sensor (TMI). Despite the common problem of excessive scattering in the WCFR (stratiform precipitation) region in every simulation, the magnitude of the scattering maximum seems better represented in the Reisner2 scheme. The different types of precipitation ice, snow, and graupel are found to behave differently in the relationship of scattering versus reflectivity. The determinative role of the precipitation ice particle size distribution (intercept parameters) is extensively discussed through sensitivity tests and a single-layer radiative transfer model.

Corresponding author address: Mei Han, Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA/GSFC, Code 613.1, Greenbelt, MD 20771. Email: mei.han@nasa.gov

Abstract

This paper uses observations from Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and microwave imager (TMI) to evaluate the cloud microphysical schemes in the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5; version 3.7.4) for a wintertime frontal precipitation system over the eastern Pacific Ocean. By incorporating a forward radiative transfer model, the radar reflectivity and brightness temperatures are simulated and compared with the observations at PR and TMI frequencies. The main purpose of this study is to identify key differences among the five schemes [including Simple ice, Reisner1, Reisner2, Schultz, and Goddard Space Flight Center (GSFC) microphysics scheme] in the MM5 that may lead to significant departures of simulated precipitation properties from both active (PR) and passive (TMI) microwave observations. Radiative properties, including radar reflectivity, attenuation, and scattering in precipitation liquid and ice layers are investigated. In the rain layer, most schemes are capable of reproducing the observed radiative properties to a reasonable degree; the Reisner2 simulation, however, produces weaker reflectivity and stronger attenuation than the observations, which is possibly attributable to the larger intercept parameter (N0r) applied in this run. In the precipitation ice layer, strong evidence regarding the differences in the microphysical and radiative properties between a narrow cold-frontal rainband (NCFR) and a wide cold-frontal rainband (WCFR) within this frontal precipitation system is found. The performances of these schemes vary significantly on simulating the microphysical and radiative properties of the frontal rainband. The GSFC scheme shows the least bias, while the Reisner1 scheme has the largest bias in the reflectivity comparison. It appears more challenging for the model to replicate the scattering signatures obtained by the passive sensor (TMI). Despite the common problem of excessive scattering in the WCFR (stratiform precipitation) region in every simulation, the magnitude of the scattering maximum seems better represented in the Reisner2 scheme. The different types of precipitation ice, snow, and graupel are found to behave differently in the relationship of scattering versus reflectivity. The determinative role of the precipitation ice particle size distribution (intercept parameters) is extensively discussed through sensitivity tests and a single-layer radiative transfer model.

Corresponding author address: Mei Han, Mesoscale Atmospheric Process Branch, Laboratory for Atmospheres, NASA/GSFC, Code 613.1, Greenbelt, MD 20771. Email: mei.han@nasa.gov

Save
  • Anthes, R. A., and T. T. Warner, 1978: Development of hydrodynamic models suitable for air pollution and other mesometeorological studies. Mon. Wea. Rev., 106 , 10451078.

    • Search Google Scholar
    • Export Citation
  • Battan, L. J., 1973: Radar Observation of the Atmosphere. University of Chicago Press, 324 pp.

  • Braun, S. A., 2006: High-resolution simulation of Hurricane Bonnie (1998). Part II: Water budget. J. Atmos. Sci., 63 , 4364.

  • Braun, S. A., and W-K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128 , 39413961.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46 , 30773107.

    • Search Google Scholar
    • Export Citation
  • Fovell, R. G., and Y. Ogura, 1988: Numerical simulation of a midlatitude squall line in two dimensions. J. Atmos. Sci., 45 , 38463879.

    • Search Google Scholar
    • Export Citation
  • Garvert, M. F., C. P. Woods, B. A. Colle, C. F. Mass, P. V. Hobbs, M. T. Stoelinga, and J. B. Wolfe, 2005: The 13–14 December 2001 IMPROVE-2 event. Part II: Comparison of MM5 model simulations of clouds and precipitation with observations. J. Atmos. Sci., 62 , 35203534.

    • Search Google Scholar
    • Export Citation
  • Gilmore, M. S., J. M. Straka, and E. N. Rasmussen, 2004: Precipitation uncertainty due to variations in precipitation particle parameters within a simple microphysics scheme. Mon. Wea. Rev., 132 , 26102627.

    • Search Google Scholar
    • Export Citation
  • Han, M., S. A. Braun, P. O. G. Persson, and J-W. Bao, 2009: Along-front variability of precipitation associated with a midlatitude frontal zone: TRMM observation and MM5 simulation. Mon. Wea. Rev., 137 , 10081028.

    • Search Google Scholar
    • Export Citation
  • Kessler, E., 1969: On the Distribution and Continuity of Water Substance in Atmospheric Circulations. Meteor. Monogr., No. 21, Amer. Meteor. Soc., 84 pp.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., 1993: On the accuracy of the Eddington approximation for radiative transfer in the microwave frequencies. J. Geophys. Res., 98 , 27572765.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2001: The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40 , 18011820.

    • Search Google Scholar
    • Export Citation
  • Lang, S., W-K. Tao, R. Cifelli, W. Olson, J. Halverson, S. Rutledge, and J. Simpson, 2007: Improving simulations of convective systems from TRMM LBA: Easterly and westerly regimes. J. Atmos. Sci., 64 , 11411164.

    • Search Google Scholar
    • Export Citation
  • Li, J-L., J. H. Jiang, D. E. Waliser, and A. M. Tompkins, 2007: Assessing consistency between EOS MSL and ECMWF analyzed and forecast estimates of cloud ice. Geophys. Res. Lett., 34 , L08701. doi:10.1029/2006GL029022.

    • Search Google Scholar
    • Export Citation
  • Li, X., W-K. Tao, T. Matsui, C. Liu, and H. Masunaga, 2009: Improving a spectral bin microphysical scheme using TRMM satellite observations. Quart. J. Roy. Meteor. Soc., 136 , 382399.

    • Search Google Scholar
    • Export Citation
  • 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 , 10651092.

    • Search Google Scholar
    • Export Citation
  • Liu, C., and M. W. Moncrieff, 2007: Sensitivity of cloud-resolving simulations of warm-season convection to cloud microphysics parameterizations. Mon. Wea. Rev., 135 , 28542868.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. M. Palmer, 1948: The distribution of raindrops with size. J. Atmos. Sci., 5 , 165166.

  • Matsui, T., X. Zeng, W-K. Tao, H. Masunaga, W. S. Olson, and S. Lang, 2009: Evaluation of long-term cloud-resolving model simulations using satellite radiance observations and multifrequency satellite simulators. J. Atmos. Oceanic Technol., 26 , 12611274.

    • Search Google Scholar
    • Export Citation
  • McCumber, M., W-K. Tao, J. Simpson, R. Penc, and S-T. Soong, 1991: Comparison of ice-phase microphysical parameterization schemes using numerical simulations of tropical convection. J. Appl. Meteor., 30 , 9851004.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., H. Zhang, G. Heymsfield, R. Hood, J. Dudhia, J. B. Halverson, and F. Marks Jr., 2006: Factors affecting the evolution of Hurricane Erin (2001) and the distribution of hydrometeors: Role of microphysical processes. J. Atmos. Sci., 63 , 127150.

    • Search Google Scholar
    • Export Citation
  • Mugnai, A., E. A. Smith, G. J. Tripoli, S. Dietrich, V. Kotroni, K. Lagouvardos, and C. M. Medaglia, 2008: Explaining discrepancies in passive microwave cloud-radiation databases in microphysical context from two different cloud-resolving models. Meteor. Atmos. Phys., 101 , 127145.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., P. Bauer, N. F. Viltard, D. E. Johnson, W-K. Tao, R. Meneghini, and L. Liao, 2001a: A melting-layer model for passive/active microwave remote sensing applications. Part I: Model formulation and comparison with observations. J. Appl. Meteor., 40 , 11451163.

    • Search Google Scholar
    • Export Citation
  • Olson, W. S., P. Bauer, C. D. Kummerow, Y. Hong, and W-K. Tao, 2001b: A melting-layer model for passive/active microwave remote sensing applications. Part II: Simulations of TRMM observations. J. Appl. Meteor., 40 , 11641179.

    • Search Google Scholar
    • Export Citation
  • Petty, G. W., 1994: Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part I: Theoretical characteristics of normalized polarization and scattering indices. Meteor. Atmos. Phys., 54 , 7999.

    • Search Google Scholar
    • Export Citation
  • Petty, G. W., and K. B. Katsaros, 1990: Precipitation observed over the South China Sea by the Nimbus-7 Scanning Multichannel Microwave Radiometer during winter MONEX. J. Appl. Meteor., 29 , 273287.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124 , 10711107.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and P. V. Hobbs, 1984: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold frontal rainbands. J. Atmos. Sci., 41 , 29492972.

    • Search Google Scholar
    • Export Citation
  • Schultz, P., 1995: An explicit cloud physics parameterization for operational numerical weather prediction. Mon. Wea. Rev., 123 , 33313343.

    • Search Google Scholar
    • Export Citation
  • Tao, W-K., and J. Simpson, 1993: Goddard cumulus ensemble model. Part I: Model description. Terr. Atmos. Ocean Sci., 4 , 3572.

  • Tao, W-K., J. Simpson, and M. McCumber, 1989: An ice–water saturation adjustment. Mon. Wea. Rev., 117 , 231235.

  • Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev., 132 , 519542.

    • Search Google Scholar
    • Export Citation
  • Yuter, S. E., and R. A. Houze Jr., 1995: Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon. Wea. Rev., 123 , 19411963.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 929 809 339
PDF Downloads 106 28 0