Verification of Convection-Allowing WRF Model Forecasts of the Planetary Boundary Layer Using Sounding Observations

Michael C. Coniglio NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Michael C. Coniglio in
Current site
Google Scholar
PubMed
Close
,
James Correia Jr. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, and NOAA/NWS/NCEP/Storm Prediction Center, Norman, Oklahoma

Search for other papers by James Correia Jr. in
Current site
Google Scholar
PubMed
Close
,
Patrick T. Marsh NOAA/OAR/National Severe Storms Laboratory, and Cooperative Institute for Mesoscale Meteorological Studies, and School of Meteorology, University of Oklahoma, Norman, Oklahoma

Search for other papers by Patrick T. Marsh in
Current site
Google Scholar
PubMed
Close
, and
Fanyou Kong Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma

Search for other papers by Fanyou Kong in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).

Corresponding author address: Michael C. Coniglio, National Weather Center, NSSL/FRDD, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: michael.coniglio@noaa.gov

Abstract

This study evaluates forecasts of thermodynamic variables from five convection-allowing configurations of the Weather Research and Forecasting Model (WRF) with the Advanced Research core (WRF-ARW). The forecasts vary only in their planetary boundary layer (PBL) scheme, including three “local” schemes [Mellor–Yamada–Janjić (MYJ), quasi-normal scale elimination (QNSE), and Mellor–Yamada–Nakanishi–Niino (MYNN)] and two schemes that include “nonlocal” mixing [the asymmetric cloud model version 2 (ACM2) and the Yonei University (YSU) scheme]. The forecasts are compared to springtime radiosonde observations upstream from deep convection to gain a better understanding of the thermodynamic characteristics of these PBL schemes in this regime. The morning PBLs are all too cool and dry despite having little bias in PBL depth (except for YSU). In the evening, the local schemes produce shallower PBLs that are often too shallow and too moist compared to nonlocal schemes. However, MYNN is nearly unbiased in PBL depth, moisture, and potential temperature, which is comparable to the background North American Mesoscale model (NAM) forecasts. This result gives confidence in the use of the MYNN scheme in convection-allowing configurations of WRF-ARW to alleviate the typical cool, moist bias of the MYJ scheme in convective boundary layers upstream from convection. The morning cool and dry biases lead to an underprediction of mixed-layer CAPE (MLCAPE) and an overprediction of mixed-layer convective inhibition (MLCIN) at that time in all schemes. MLCAPE and MLCIN forecasts improve in the evening, with MYJ, QNSE, and MYNN having small mean errors, but ACM2 and YSU having a somewhat low bias. Strong observed capping inversions tend to be associated with an underprediction of MLCIN in the evening, as the model profiles are too smooth. MLCAPE tends to be overpredicted (underpredicted) by MYJ and QNSE (MYNN, ACM2, and YSU) when the observed MLCAPE is relatively small (large).

Corresponding author address: Michael C. Coniglio, National Weather Center, NSSL/FRDD, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: michael.coniglio@noaa.gov
Save
  • Bright, D. R., and Mullen S. L. , 2002: The sensitivity of the numerical simulation of the Southwest monsoon boundary layer to the choice of PBL turbulence parameterization in MM5. Wea. Forecasting, 17, 99114.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and Dudhia J. , 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569585.

    • Search Google Scholar
    • Export Citation
  • Chou, M.-D., and Suarez M. J. , 1994: An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606, 3, 85 pp.

  • 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.

    • Search Google Scholar
    • Export Citation
  • Davies, J. M., 2004: Estimations of CIN and LFC associated with tornadic and nontornadic supercells. Wea. Forecasting, 19, 714726.

  • Dean, A. R., and Schneider R. S. , 2008: Forecast challenges at the NWS Storm Prediction Center relating to the frequency of favorable severe storm environments. Preprints, 24th Conf. on Severe Local Storms, Savannah, GA, Amer. Meteor. Soc., 9A.2. [Available online at https://ams.confex.com/ams/pdfpapers/141743.pdf.]

  • Ek, M. B., Mitchell K. E. , Lin Y. , Rogers E. , Grunmann P. , Koren V. , Gayno G. , and Tarpley J. D. , 2003: Implementation of Noah Land Surface Model advances in the National Centers for Environmental Prediction operational mesoscale Eta Model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Elmore, K. L., Baldwin M. E. , and Schultz D. M. , 2006: Field significance revisited: Spatial bias errors in forecasts as applied to the Eta Model. Mon. Wea. Rev., 134, 519531.

    • Search Google Scholar
    • Export Citation
  • Fabry, F., 2006: The spatial variability of moisture in the boundary layer and its effect on convection initiation: Project-long characterization. Mon. Wea. Rev., 134, 7991.

    • Search Google Scholar
    • Export Citation
  • Gao, J., Xue M. , Brewster K. , and Droegemeier K. K. , 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457469.

    • Search Google Scholar
    • Export Citation
  • Hacker, J. P., 2010: Spatial and temporal scales of boundary layer wind predictability in response to small-amplitude land surface uncertainty. J. Atmos. Sci., 67, 217233.

    • Search Google Scholar
    • Export Citation
  • Hill, K. A., and Lackmann G. M. , 2009: Analysis of idealized tropical cyclone simulations using the Weather Research and Forecasting model: Sensitivity to turbulence parameterization and grid spacing. Mon. Wea. Rev., 137, 745765.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and Pan H.-L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 23222339.

    • Search Google Scholar
    • Export Citation
  • Hu, M., Xue M. , and Brewster K. , 2006: 3DVAR and cloud analysis with WSR-88D level-II data for the prediction of Fort Worth tornadic thunderstorms. Part I: Cloud analysis and its impact. Mon. Wea. Rev., 134, 675698.

    • Search Google Scholar
    • Export Citation
  • Hu, X.-M., Nielsen-Gammon J. W. , and Zhang F. , 2010: Evaluation of three planetary boundary layer schemes in the WRF model. J. Appl. Meteor. Climatol., 49, 18311843.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain Eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 2001: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP Meso Model. NCEP Office Note 437, 61 pp.

  • Janjić, Z. I., 2003: A nonhydrostatic model based on a new approach. Meteor. Atmos. Phys., 82, 271285.

  • Jankov, I., Gallus W. A. , Segal M. , Shaw B. , and Koch S. E. , 2005: The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall. Wea. Forecasting, 20, 10481060.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., Weiss S. J. , Baldwin M. E. , Carbin G. W. , Bright D. A. , Levit J. J. , and Hart J. A. , 2005: Evaluating high-resolution configurations of the WRF model that are used to forecast severe convective weather: The 2005 SPC/NSSL Spring Program. Preprints, 21st Conf. on Weather Analysis and Forecasting/17th Conf. on Numerical Weather Prediction, Washington, DC, Amer. Meteor. Soc., 2A.5. [Available online at http://ams.confex.com/ams/pdfpapers/94843.pdf.]

  • Kain, J. S., and Coauthors, 2013: A feasibility study for probabilistic convection initiation forecasts based on explicit numerical guidance. Bull. Amer. Meteor. Soc., in press.

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

    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., Taubman S. J. , Brown P. D. , Iacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the long-wave. J. Geophys. Res., 102 (D14), 16 66316 682.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., 2001: Improvement of the Mellor–Yamada turbulence closure model based on large-eddy simulation data. Bound.-Layer Meteor., 99, 349378.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and Niino H. , 2004: An improved Mellor–Yamada level-3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 131.

    • Search Google Scholar
    • Export Citation
  • Nakanishi, M., and Niino H. , 2009: Development of an improved turbulence closure model for the atmospheric boundary layer. J. Meteor. Soc. Japan, 87, 895912.

    • Search Google Scholar
    • Export Citation
  • Nielsen-Gammon, J. W., Hu X.-M. , Zhang F. , and Pleim J. E. , 2010: Evaluation of planetary boundary layer scheme sensitivities for the purpose of parameter estimation. Mon. Wea. Rev., 138, 34003417.

    • Search Google Scholar
    • Export Citation
  • NOAA, 2011: Technical implementation notice 11-16. National Weather Service Headquarters, Washington, DC. [Available online at http://www.nws.noaa.gov/os/notification/tin11-16nam_changes_aad.txt.]

  • Noh, Y., Cheon W. G. , Hong S.-Y. , and Raasch S. , 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401427.

    • Search Google Scholar
    • Export Citation
  • Nowotarski, C. J., Markowski P. M. , and Richardson Y. P. , 2011: The characteristics of numerically simulated supercell storms situated over statically stable boundary layers. Mon. Wea. Rev., 139, 31393162.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 13831395.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. N., and Blanchard D. O. , 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 11481164.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-475+STR, 113 pp. [Available online at http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf.]

  • Stensrud, D. J., 2007: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models. Cambridge University Press, 459 pp.

  • Stensrud, D. J., and Weiss S. J. , 2002: Mesoscale model ensemble forecasts of the 3 May 1999 tornado outbreak. Wea. Forecasting, 17, 526543.

    • Search Google Scholar
    • Export Citation
  • Stratman, D. R., Coniglio M. C. , Koch S. E. , and Xue M. , 2013: Use of multiple verification methods to evaluate forecasts of convection from hot- and cold-start convection-allowing models. Wea. Forecasting, 28, 119138.

    • Search Google Scholar
    • Export Citation
  • Sukoriansky, S., Galperian B. , and Perov V. , 2005: Application of a new spectral theory of stable stratified turbulence to the atmospheric boundary layer over sea ice. Bound.-Layer Meteor., 117, 231257.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., Field P. R. , Rasmussen R. M. , and Hall W. D. , 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.

    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., Wilson J. W. , and Wakimoto R. M. , 1996: Thermodynamic variability within the convective boundary layer due to horizontal convective rolls. Mon. Wea. Rev., 124, 769784.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and Roberts R. D. , 2006: Summary of convective storm initiation and evolution during IHOP: Observational and modeling perspective. Mon. Wea. Rev., 134, 2347.

    • Search Google Scholar
    • Export Citation
  • Xue, M., Wang D.-H. , Gao J.-D. , Brewster K. , and Droegemeier K. K. , 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139170.

    • Search Google Scholar
    • Export Citation
  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 13211338.

    • Search Google Scholar
    • Export Citation
  • Ziegler, C. L., Mansell E. R. , Straka J. M. , MacGorman D. R. , and Burgess D. W. , 2010: The impact of spatial variations of low-level stability on the life cycle of a simulated supercell storm. Mon. Wea. Rev., 138, 17381766.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 5509 2096 26
PDF Downloads 1387 331 8