Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF-ARW Using Synthetic and Observed GOES-13 Imagery

Lewis Grasso * Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Search for other papers by Lewis Grasso in
Current site
Google Scholar
PubMed
Close
,
Daniel T. Lindsey NOAA/Center for Satellite Applications and Research, and Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Search for other papers by Daniel T. Lindsey in
Current site
Google Scholar
PubMed
Close
,
Kyo-Sun Sunny Lim Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington

Search for other papers by Kyo-Sun Sunny Lim in
Current site
Google Scholar
PubMed
Close
,
Adam Clark National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Adam Clark in
Current site
Google Scholar
PubMed
Close
,
Dan Bikos * Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Search for other papers by Dan Bikos in
Current site
Google Scholar
PubMed
Close
, and
Scott R. Dembek Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma

Search for other papers by Scott R. Dembek in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) single-moment 6-class (WSM6) microphysics scheme in the Advanced Research WRF (WRF-ARW) produces less upper-level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite-13 (GOES-13) imagery at 10.7 μm of simulated cloud fields from the 4-km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper-level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the 4-km NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

Corresponding author address: Lewis Grasso, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523-1375. E-mail: lewis.grasso@colostate.edu

Abstract

Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) single-moment 6-class (WSM6) microphysics scheme in the Advanced Research WRF (WRF-ARW) produces less upper-level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite-13 (GOES-13) imagery at 10.7 μm of simulated cloud fields from the 4-km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper-level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the 4-km NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

Corresponding author address: Lewis Grasso, Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO 80523-1375. E-mail: lewis.grasso@colostate.edu
Save
  • Bikos, D., and Coauthors, 2012: Synthetic satellite imagery for real-time high-resolution model evaluation. Wea. Forecasting, 27, 784795, doi:10.1175/WAF-D-11-00130.1.

    • Search Google Scholar
    • Export Citation
  • Bryan, G. H., and H. Morrison, 2012: Sensitivity of a simulated squall line to horizontal resolution and parameterization of microphysics. Mon. Wea. Rev., 140, 202225, doi:10.1175/MWR-D-11-00046.1.

    • Search Google Scholar
    • Export Citation
  • Cintineo, R., J. A. Otkin, M. Xue, and F. Kong, 2014: Evaluating the performance of planetary boundary layer and cloud microphysical parameterization schemes in convection-permitting ensemble forecasts using synthetic GOES-13 satellite observations. Mon. Wea. Rev., 142, 163182, doi:10.1175/MWR-D-13-00143.1.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., and Coauthors, 2012: An overview of the 2010 Hazardous Weather Testbed Experimental Forecast Program Spring Experiment. Bull. Amer. Meteor. Soc., 93, 5574, doi:10.1175/BAMS-D-11-00040.1.

    • Search Google Scholar
    • Export Citation
  • Clark, A. J., R. G. Bullock, T. L. Jensen, M. Xue, and F. Kong, 2014: Application of object-based time-domain diagnostics for tracking precipitation systems in convection-allowing models. Wea. Forecasting,29, 517–542, doi:10.1175/WAF-D-13-00098.1.

  • Cotton, W. R., and Coauthors, 2003: RAMS 2001: Current status and future direction. Meteor. Atmos. Phys., 82, 529, doi:10.1007/s00703-001-0584-9.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., S.-Y. Hong, and K.-S. Lim, 2008: A new method for representing mixed-phase particle fall speeds in bulk microphysics parameterizations. J. Meteor. Soc. Japan, 86A, 3344, doi:10.2151/jmsj.86A.33.

    • Search Google Scholar
    • Export Citation
  • Goodman, S. J., and Coauthors, 2012: The GOES-R Proving Ground: Accelerating user readiness for the Next-Generation Geostationary Environmental Satellite System. Bull. Amer. Meteor. Soc., 93, 10291040, doi:10.1175/BAMS-D-11-00175.1.

    • Search Google Scholar
    • Export Citation
  • Grasso, L. D., and T. Greenwald, 2004: Analysis of 10.7-μm brightness temperatures of a simulated thunderstorm with two-moment microphysics. Mon. Wea. Rev., 132, 815825, doi:10.1175/1520-0493(2004)132<0815:AOMBTO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Grasso, L. D., and D. Lindsey, 2011: An example of the use of synthetic 3.9 μm GOES-12 imagery for two-moment microphysical evaluation. Int. J. Remote Sens., 32, 23372350, doi:10.1080/01431161003698294.

    • Search Google Scholar
    • Export Citation
  • Grasso, L. D., M. Sengupta, and M. DeMaria, 2010: Comparison between observed and synthetic 6.5 and 10.7 μm GOES-12 imagery of thunderstorms that occurred on 8 May 2003. Int. J. Remote Sens., 31, 647663, doi:10.1080/01431160902894483.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129151.

  • Jankov, I., and Coauthors, 2011: An evaluation of five WRF-ARW microphysics schemes using synthetic GOES imagery for an atmospheric river event affecting the California coast. J. Hydrometeor., 12, 618633, doi:10.1175/2010JHM1282.1.

    • Search Google Scholar
    • Export Citation
  • Jung, Y., M. Xue, and M. Tong, 2012: Ensemble Kalman filter analyses of the 29–30 May 2004 Oklahoma tornadic thunderstorm using one- and two-moment bulk microphysics schemes, with verification against polarimetric radar data. Mon. Wea. Rev., 140, 14571475, doi:10.1175/MWR-D-11-00032.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., S. J. Weiss, J. J. Levit, M. E. Baldwin, and D. R. Bright, 2006: Examination of convection-allowing configurations of the WRF model for the prediction of severe convective weather: The SPC/NSSL Spring Program 2004. Wea. Forecasting, 21, 167181, doi:10.1175/WAF906.1.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., S. R. Dembek, S. J. Weiss, J. L. Case, J. J. Levit, and R. A. Sobash, 2010: Extracting unique information from high-resolution forecast models: Monitoring selected fields and phenomena every time step. Wea. Forecasting, 25, 15361542, doi:10.1175/2010WAF2222430.1.

    • Search Google Scholar
    • Export Citation
  • Kong, F., and Coauthors, 2007: Preliminary analysis on the real-time storm-scale ensemble forecasts produced as part of the NOAA hazardous weather testbed 2007 spring experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.2. [Available online at https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124667.htm.]

  • Lazzara, M. A., and Coauthors, 1999: The Man computer Interactive Data Access System: 25 years of interactive processing. Bull. Amer. Meteor. Soc., 80, 271284, doi:10.1175/1520-0477(1999)080<0271:TMCIDA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 15871612, doi:10.1175/2009MWR2968.1.

    • Search Google Scholar
    • Export Citation
  • Lim, K.-S. S., and S.-Y. Hong, 2012: Investigation of aerosol indirect effects on simulated flash-flood heavy rainfall over Korea. Meteor. Atmos. Phys., 118, 199214, doi:10.1007/s00703-012-0216-6.

    • Search Google Scholar
    • Export Citation
  • Lin, Y.-L., R. D. Farley, and H. D. Orville, 1983: Bulk scheme of the snow field in a cloud model. J. Climate Appl. Meteor., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys., 20, 851875, doi:10.1029/RG020i004p00851.

    • Search Google Scholar
    • Export Citation
  • Oberthaler, A. J., and P. M. Markowski, 2013: A numerical simulation study of the effects of anvil shading on quasi-linear convective systems. J. Atmos. Sci., 70, 767793, doi:10.1175/JAS-D-12-0123.1.

    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., T. J. Greenwald, J. Sieglaff, and H.-L. Huang, 2009: Validation of a large-scale high-resolution WRF model simulation using SEVIRI satellite observations. J. Appl. Meteor. Climatol., 48, 16131626, doi:10.1175/2009JAMC2142.1.

    • 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, doi:10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seemann, S. W., E. E. Borbas, R. O. Knuteson, G. R. Stephenson, and H.-L. Huang, 2008: Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements. J. Appl. Meteor. Climatol., 47, 108123, doi:10.1175/2007JAMC1590.1.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the advanced research WRF version 2. NCAR Tech. Note NCAR/TN-468+STR, 88 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v2.pdf.]

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Van Weverberg, K., and Coauthors, 2013: The role of cloud microphysics parameterization in the simulation of mesoscale convective system clouds and precipitation in the tropical western Pacific. J. Atmos. Sci., 70, 11041128, doi:10.1175/JAS-D-12-0104.1.

    • Search Google Scholar
    • Export Citation
  • Xue, M., and Coauthors, 2007: CAPS realtime storm-scale ensemble and high-resolution forecast as part of the NOAA Hazardous Weather Testbed 2007 spring experiment. 22nd Conf. on Weather Analysis and Forecasting/18th Conf. on Numerical Weather Prediction, Salt Lake City, UT, Amer. Meteor. Soc., 3B.1. [Available online at https://ams.confex.com/ams/22WAF18NWP/techprogram/paper_124587.htm.]

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 669 204 14
PDF Downloads 514 152 17