Evaluation of PBL Parameterizations for Modeling Surface Wind Speed during Storms in the Northeast United States

Maria E. B. Frediani Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Joshua P. Hacker National Center for Atmospheric Research, Boulder, Colorado

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Emmanouil N. Anagnostou Department of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut

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Thomas Hopson National Center for Atmospheric Research, Boulder, Colorado

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Abstract

This study identifies conditions that determine errors in numerical simulations of 10-m wind speed over moderately complex terrain, emphasizing winds that lead to overhead power-line damage over a subregion of the northeast United States. Simulations with the Mellor–Yamada–Janjić (MYJ) scheme, the Yonsei University (YSU) scheme, and a subgrid-scale topographic drag correction (Topo) applied to YSU are used to investigate error components. The wind speed distribution is dominated by low speeds, which are well depicted by Topo, but are underestimated by the MYJ and YSU schemes. Conversely, moderate and high speeds are underestimated by Topo, and MYJ and YSU perform better across specific ranges. Verification samples are conditioned by season, diurnal cycle, topography, and spatial patterns obtained with a clustering analysis. The systematic error is characterized by a positive bias in low speeds, and as speed increases the biases become more negative. Quantile comparisons, along with systematic and random errors, indicate that beyond the dependence on wind speed itself, errors also depend on seasonal characteristics, indirectly defined by scheme stability profiles. The positive relationship between absolute bias and speed originates in the friction velocity parameterization, and the correction for drag in the Topo scheme exacerbates the effect. The Topo scheme adjusts the total bias and sharpens the bias spread but penalizes moderate and high winds. Clusters reveal that in Topo the bias is primarily driven by wind direction. Excessive correction occurs on terrain-interacting flows, and oceanic flow modulates the adjustment, enhancing the scheme’s performance.

Corresponding author address: Maria E. B. Frediani, Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269. E-mail: maria.frediani@uconn.edu

Abstract

This study identifies conditions that determine errors in numerical simulations of 10-m wind speed over moderately complex terrain, emphasizing winds that lead to overhead power-line damage over a subregion of the northeast United States. Simulations with the Mellor–Yamada–Janjić (MYJ) scheme, the Yonsei University (YSU) scheme, and a subgrid-scale topographic drag correction (Topo) applied to YSU are used to investigate error components. The wind speed distribution is dominated by low speeds, which are well depicted by Topo, but are underestimated by the MYJ and YSU schemes. Conversely, moderate and high speeds are underestimated by Topo, and MYJ and YSU perform better across specific ranges. Verification samples are conditioned by season, diurnal cycle, topography, and spatial patterns obtained with a clustering analysis. The systematic error is characterized by a positive bias in low speeds, and as speed increases the biases become more negative. Quantile comparisons, along with systematic and random errors, indicate that beyond the dependence on wind speed itself, errors also depend on seasonal characteristics, indirectly defined by scheme stability profiles. The positive relationship between absolute bias and speed originates in the friction velocity parameterization, and the correction for drag in the Topo scheme exacerbates the effect. The Topo scheme adjusts the total bias and sharpens the bias spread but penalizes moderate and high winds. Clusters reveal that in Topo the bias is primarily driven by wind direction. Excessive correction occurs on terrain-interacting flows, and oceanic flow modulates the adjustment, enhancing the scheme’s performance.

Corresponding author address: Maria E. B. Frediani, Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269. E-mail: maria.frediani@uconn.edu
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  • Arakawa, A., 2004: The cumulus parameterization problem: Past, present, and future. J. Climate, 17, 24932525, doi:10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bernardet, L., and Coauthors, 2008: The Developmental Testbed Center and its winter forecasting experiment. Bull. Amer. Meteor. Soc., 89, 611627, doi:10.1175/BAMS-89-5-611.

    • Search Google Scholar
    • Export Citation
  • Burk, S. D., and Thompson W. T. , 1989: A vertically nested regional numerical weather prediction model with second-order closure physics. Mon. Wea. Rev., 117, 23052324, doi:10.1175/1520-0493(1989)117<2305:AVNRNW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Cheng, W. Y. Y., and Steenburgh W. J. , 2005: Evaluation of surface sensible weather forecasts by the WRF and the Eta Models over the western United States. Wea. Forecasting, 20, 812821, doi:10.1175/WAF885.1.

    • 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, Vol. 3, 84 pp. [Available online at http://gmao.gsfc.nasa.gov/pubs/docs/Chou128.pdf.]

  • Colle, B. A., Olson J. B. , and Tongue J. S. , 2003a: Multiseason verification of the MM5. Part I: Comparison with the Eta Model over the central and eastern United States and impact of MM5 resolution. Wea. Forecasting, 18, 431457, doi:10.1175/1520-0434(2003)18<431:MVOTMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Colle, B. A., Olson J. B. , and Tongue J. S. , 2003b: Multiseason verification of the MM5. Part II: Evaluation of high-resolution precipitation forecasts over the northeastern United States. Wea. Forecasting, 18, 458480, doi:10.1175/1520-0434(2003)18<458:MVOTMP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Connecticut Light & Power, 2014: Transmission and distribution reliability performance report. Connecticut Department of Energy & Environmental Protection—Public Utilities Regulatory Authority Tech. Rep. 86-12-03, 21 pp. [Available online at http://www.dpuc.state.ct.us/dockcurr.nsf/8e6fc37a54110e3e852576190052b64d/027ee9c77ff179fb85257cac006627be/$FILE/2014%20CL&P%20TDRP%20Report%20Body.docx.]

  • Davis, R. E., and Walker D. R. , 1992: An upper-air synoptic climatology of the western United States. J. Climate, 5, 14491467, doi:10.1175/1520-0442(1992)005<1449:AUASCO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 2014: A history of mesoscale model development. Asia-Pac. J. Atmos. Sci., 50, 121131, doi:10.1007/s13143-014-0031-8.

  • Giovannini, L., Antonacci G. , Zardi D. , Laiti L. , and Panziera L. , 2014: Sensitivity of simulated wind speed to spatial resolution over complex terrain. Energy Procedia, 59, 323329, doi:10.1016/j.egypro.2014.10.384.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and Dévényi D. , 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, doi:10.1029/2002GL015311.

    • Search Google Scholar
    • Export Citation
  • Hanna, S. R., and Yang R. , 2001: Evaluations of mesoscale models’ simulations of near-surface winds, temperature gradients, and mixing depths. J. Appl. Meteor., 40, 10951104, doi:10.1175/1520-0450(2001)040<1095:EOMMSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hari Prasad, K. B. R. R., Srinivas C. V. , Satyanarayana N. V. , Naidu C. V. , Baskaran R. , and Venkatraman B. , 2015: Formulation of stability-dependent empirical relations for turbulent intensities from surface layer turbulence measurements for dispersion parameterization in a Lagrangian particle dispersion model. Meteor. Atmos. Phys., 127, 435450, doi:10.1007/s00703-015-0373-5.

    • Search Google Scholar
    • Export Citation
  • Hart, K. A., Steenburgh W. J. , and Onton D. J. , 2005: Model forecast improvements with decreased horizontal grid spacing over finescale intermountain orography during the 2002 Olympic Winter Games. Wea. Forecasting, 20, 558576, doi:10.1175/WAF865.1.

    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Noh Y. , and Dudhia J. , 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • 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, 18311844, doi:10.1175/2010JAMC2432.1.

    • Search Google Scholar
    • Export Citation
  • Hu, X. M., Klein P. M. , and Xue M. , 2013: Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. J. Geophys. Res. Atmos., 118, 10 49010 505, doi:10.1002/jgrd.50823.

    • 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, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1996: The surface layer parameterization in the NCEP Eta Model. Research Activities in Atmospheric and Oceanic Modeling, H. Ritchie, Ed., World Climate Research Programme, WMO, 4.16–4.17.

  • Janjić, Z. I., 2002: Nonsingular implementation of the Mellor–Yamada level 2.5 scheme in the NCEP Meso model. NCEP Office Note 437, 61 pp. [Available online at http://www.emc.ncep.noaa.gov/officenotes/newernotes/on437.pdf.]

  • Jarraud, M., 2008: Guide to Meteorological Instruments and Methods of Observation. 7th ed. WMO 8, 681 pp. [Available online at https://www.wmo.int/pages/prog/gcos/documents/gruanmanuals/CIMO/CIMO_Guide-7th_Edition-2008.pdf.]

  • Jiménez, P. A., and Dudhia J. , 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF Model. J. Appl. Meteor. Climatol., 51, 300316, doi:10.1175/JAMC-D-11-084.1.

    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., and Dudhia J. , 2013: On the ability of the WRF Model to reproduce the surface wind direction over complex terrain. J. Appl. Meteor. Climatol., 52, 16101617, doi:10.1175/JAMC-D-12-0266.1.

    • Search Google Scholar
    • Export Citation
  • Jiménez, P. A., Dudhia J. , González-Rouco J. F. , Montávez J. P. , García-Bustamante E. , Navarro J. , Vilà-Guerau de Arellano J. , and Muñoz-Roldán A. , 2013: An evaluation of WRF’s ability to reproduce the surface wind over complex terrain based on typical circulation patterns. J. Geophys. Res. Atmos., 118, 76517669, doi:10.1002/jgrd.50585.

    • Search Google Scholar
    • Export Citation
  • Jones, M. S., Colle B. A. , and Tongue J. S. , 2007: Evaluation of a mesoscale short-range ensemble forecast system over the northeast United States. Wea. Forecasting, 22, 3655, doi:10.1175/WAF973.1.

    • Search Google Scholar
    • Export Citation
  • Kaufmann, P., and Weber R. O. , 1996: Classification of mesoscale wind fields in the MISTRAL field experiment. J. Appl. Meteor., 35, 19631979, doi:10.1175/1520-0450(1996)035<1963:COMWFI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kaufmann, P., and Whiteman C. D. , 1999: Cluster-analysis classification of wintertime wind patterns in the Grand Canyon region. J. Appl. Meteor., 38, 11311147, doi:10.1175/1520-0450(1999)038<1131:CACOWW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lee, J., Shin H. H. , Hong S.-y. , Jiménez P. a. , Dudhia J. , and Hong J. , 2014: Impacts of subgrid-scale orography parameterization on simulated surface layer wind and monsoonal precipitation in the high-resolution WRF model. J. Geophys. Res. Atmos., 120, 644653, doi:10.1002/2014JD022747.

    • Search Google Scholar
    • Export Citation
  • Liu, Y., and Coauthors, 2008: The operational mesogamma-scale analysis and forecast system of the U.S. Army Test and Evaluation Command. Part II: Interrange comparison of the accuracy of model analyses and forecasts. J. Appl. Meteor. Climatol., 47, 10931104, doi:10.1175/2007JAMC1654.1.

    • Search Google Scholar
    • Export Citation
  • Lorente-Plazas, R., 2014: Characterization of the wind over the Iberian Peninsula: Observations and regional simulations. Ph.D. thesis, Universidad de Murcia, 208 pp. [Available online at https://digitum.um.es/xmlui/handle/10201/40149?mode=full.]

  • Maliszewski, P. J., and Perrings C. , 2012: Factors in the resilience of electrical power distribution infrastructures. Appl. Geogr., 32, 668679, doi:10.1016/j.apgeog.2011.08.001.

    • Search Google Scholar
    • Export Citation
  • McCaffrey, S. M., 2006: The public and wildland fire management: Social science findings for managers. General Tech. Rep. NRS-1, Northern Research Station, U.S. Forest Service, Newtown Square, PA, 202 pp. [Available online at http://www.fs.fed.us/nrs/pubs/gtr/gtr_nrs1.pdf.]

  • McGee, J., Skiff J. , Carozza P. , Edelstein T. , Hoffman L. , Jackson S. , Robert M. , and Osten C. , 2012: Report of the Two Storm Panel. State of Connecticut Tech. Rep., 39 pp. [Available online at http://portal.ct.gov/Departments_and_Agencies/Office_of_the_Governor/Learn_More/Working_Groups/two_storm_panel_final_report/.]

  • Miller, S. T. K., and Keim B. D. , 2003: Synoptic-scale controls on the sea breeze of the central New England coast. Wea. Forecasting, 18, 236248, doi:10.1175/1520-0434(2003)018<0236:SCOTSB>2.0.CO;2.

    • 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 atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Search Google Scholar
    • Export Citation
  • Murray, J. C., and Colle B. A. , 2011: The spatial and temporal variability of convective storms over the northeast United States during the warm season. Mon. Wea. Rev., 139, 9921012, doi:10.1175/2010MWR3316.1.

    • 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, doi:10.1175/2010MWR3292.1.

    • Search Google Scholar
    • Export Citation
  • Nolan, D. S., Zhang J. A. , and Stern D. P. , 2009: Evaluation of planetary boundary layer parameterizations in tropical cyclones by comparison of in situ observations and high-resolution simulations of Hurricane Isabel (2003). Part I: Initialization, maximum winds, and the outer-core boundary layer. Mon. Wea. Rev., 137, 36513674, doi:10.1175/2009MWR2785.1.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part II: Application and evaluation in a mesoscale meteorological model. J. Appl. Meteor. Climatol., 46, 13961409, doi:10.1175/JAM2534.1.

    • Search Google Scholar
    • Export Citation
  • Radeloff, V. C., Hammer R. B. , Stewart S. I. , Fried J. S. , Holcomb S. S. , and McKeefry J. F. , 2005: The wildland–urban interface in the United States. Ecol. Appl., 15, 799805, doi:10.1890/04-1413.

    • Search Google Scholar
    • Export Citation
  • Roux, G., Liu Y. , Delle Monache L. , Sheu R.-S. , and Warner T. T. , 2009: Verification of high resolution WRF-RTFDDA surface forecasts over mountains and plains. Preprints, 10th WRF Users Workshop, Boulder, CO, NCAR. [Available online at http://www2.mmm.ucar.edu/wrf/users/workshops/WS2009/presentations/5B-05.pdf.]

  • Santos-Alamillos, F. J., Pozo-Vázquez D. , Ruiz-Arias J. A. , Lara-Fanego V. , and Tovar-Pescador J. , 2013: Analysis of WRF Model wind estimate sensitivity to physics parameterization choice and terrain representation in Andalusia (southern Spain). J. Appl. Meteor. Climatol., 52, 15921609, doi:10.1175/JAMC-D-12-0204.1.

    • Search Google Scholar
    • Export Citation
  • Shin, H. H., and Hong S. Y. , 2011: Intercomparison of planetary boundary-layer parametrizations in the WRF Model for a single day from CASES-99. Bound.-Layer Meteor., 139, 261281, doi:10.1007/s10546-010-9583-z.

    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

  • Tewari, M., and Coauthors, 2004: Implementation and verification of the unified Noah land surface model in the WRF Model. Preprints, 20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, 14.2A. [Available online at https://ams.confex.com/ams/84Annual/techprogram/paper_69061.htm.]

  • 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, doi:10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Wanik, D., Anagnostou E. , Hartman B. , Frediani M. , and Astitha M. , 2015: Storm outage modeling for an electric distribution network in northeastern USA. Nat. Hazards, 79, 13591384, doi:10.1007/s11069-015-1908-2.

    • Search Google Scholar
    • Export Citation
  • Weber, R. O., and Kaufmann P. , 1995: Automated classification scheme for wind fields. J. Appl. Meteor., 34, 11331141, doi:10.1175/1520-0450(1995)034<1133:ACSFWF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wood, N., 2000: Wind flow over complex terrain: A historical perspective and the prospect for large-eddy modelling. Bound.-Layer Meteor., 96, 1132, doi:10.1023/A:1002017732694.

    • Search Google Scholar
    • Export Citation
  • Zhang, D.-L., and Anthes R. A. , 1982: A high-resolution model of the planetary boundary layer—Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21, 15941609, doi:10.1175/1520-0450(1982)021<1594:AHRMOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, D.-L., and Zheng W.-Z. , 2004: Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157169, doi:10.1175/1520-0450(2004)043<0157:DCOSWA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, H., Pu Z. , and Zhang X. , 2013: Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Wea. Forecasting, 28, 893914, doi:10.1175/WAF-D-12-00109.1.

    • Search Google Scholar
    • Export Citation
  • Zhong, S., and Fast J. , 2003: An evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake Valley. Mon. Wea. Rev., 131, 13011322, doi:10.1175/1520-0493(2003)131<1301:AEOTMR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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