Spatial Variability of Winds and HRRR–NCEP Model Error Statistics at Three Doppler-Lidar Sites in the Wind-Energy Generation Region of the Columbia River Basin

Y. L. Pichugina Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
NOAA/Earth System Research Laboratory, Boulder, Colorado

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R. M. Banta NOAA/Earth System Research Laboratory, Boulder, Colorado

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T. Bonin Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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W. A. Brewer NOAA/Earth System Research Laboratory, Boulder, Colorado

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A. Choukulkar Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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B. J. McCarty Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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S. Baidar Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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C. Draxl National Renewable Energy Laboratory, Golden, Colorado

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H. J. S. Fernando University of Notre Dame, Notre Dame, Indiana

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J. Kenyon Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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R. Krishnamurthy University of Notre Dame, Notre Dame, Indiana

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M. Marquis NOAA/Earth System Research Laboratory, Boulder, Colorado

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J. Olson Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
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J. Sharp Sharply Focused, LLC, Portland, Oregon

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Abstract

Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s−1 within the wind-turbine rotor layer, and 2 m s−1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s−1) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s−1. The RMSE between measured and modeled winds at all sites was about 3 m s−1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yelena L. Pichugina, yelena.pichugina@noaa.gov

Abstract

Annually and seasonally averaged wind profiles from three Doppler lidars were obtained from sites in the Columbia River basin of east-central Oregon and Washington, a major region of wind-energy production, for the Second Wind Forecast Improvement Project (WFIP2) experiment. The profile data are used to quantify the spatial variability of wind flows in this area of complex terrain, to assess the HRRR–NCEP model’s ability to capture spatial and temporal variability of wind profiles, and to evaluate model errors. Annually averaged measured wind speed differences over the 70-km extent of the lidar measurements reached 1 m s−1 within the wind-turbine rotor layer, and 2 m s−1 for 200–500 m AGL. Stronger wind speeds in the lowest 500 m occurred at sites higher in elevation, farther from the river, and farther west—closer to the Cascade Mountain barrier. Validating against the lidar data, the HRRR model underestimated strong wind speeds (>12 m s−1) and, consequently, their frequency of occurrence, especially at the two lowest-elevation sites, producing annual low biases in rotor-layer wind speed of 0.5 m s−1. The RMSE between measured and modeled winds at all sites was about 3 m s−1 and did not degrade significantly with forecast lead time. The nature of the model errors was different for different seasons. Moreover, although the three sites were located in the same basin terrain, the nature of the model errors was different at each site. Thus, if only one of the sites had been instrumented, different conclusions would have been drawn as to the major sources of model error, depending on where the measurements were made.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yelena L. Pichugina, yelena.pichugina@noaa.gov
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  • Ahlstrom, M., and Coauthors, 2013: Knowledge is power. IEEE Power Energy Mag., 11, 4552, https://doi.org/10.1109/MPE.2013.2277999.

  • Ascione, A., A. Cinque, E. Miccadei, F. Villani, and C. Berti, 2008: The Plio-Quaternary uplift of the Apennine chain: New data from the analysis of topography and river valleys in central Italy. Geomorphology, 102, 105118, https://doi.org/10.1016/j.geomorph.2007.07.022.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banakh, V. A., W. A. Brewer, Y. L. Pichugina, and I. N. Smalikho, 2010: Measurements of wind velocity and direction with coherent Doppler lidar in conditions of a weak echo signal. Atmos. Oceanic Opt., 23, 381388, https://doi.org/10.1134/S1024856010050076.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., R. K. Newsom, J. K. Lundquist, Y. L. Pichugina, R. L. Coulter, and L. Mahrt, 2002: Nocturnal low-level jet characteristics over Kansas during CASES-99. Bound.-Layer Meteor., 105, 221252, https://doi.org/10.1023/A:1019992330866.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., Y. L. Pichugina, N. D. Kelley, R. M. Hardesty, and W. A. Brewer, 2013a: Wind energy meteorology: Insight into wind properties in the turbine-rotor layer of the atmosphere from high-resolution Doppler lidar. Bull. Amer. Meteor. Soc., 94, 883902, https://doi.org/10.1175/BAMS-D-11-00057.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., and Coauthors, 2013b: Observational techniques: Sampling the mountain atmosphere. Mountain Weather Research and Forecasting, F. K. Chow, S. De Wekker, and B. Snyder, Eds., Springer, 409–530, https://doi.org/10.1007/978-94-007-4098-3.

    • Crossref
    • Export Citation
  • Banta, R. M., and Coauthors, 2015: 3D volumetric analysis of wind-turbine wake properties in the atmosphere using high-resolution Doppler lidar. J. Atmos. Oceanic Technol., 32, 904914, https://doi.org/10.1175/JTECH-D-14-00078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., and Coauthors, 2018a: Evaluating and improving NWP forecast models for the future: How the needs of offshore wind energy can point the way. Bull. Amer. Meteor. Soc., 99, 11551176, https://doi.org/10.1175/BAMS-D-16-0310.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banta, R. M., and Coauthors, 2018b: Observing and modeling recurrent diurnal summertime wind systems in the complex terrain of the Columbia River Basin during the Second Wind Forecast Improvement Program. 18th Conf. on Mountain Meteorology, Santa Fe, NM, Amer. Meteor. Soc., 16.3, https://ams.confex.com/ams/18Mountain/webprogram/Paper346156.html.

  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, https://doi.org/10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berg, J., N. Vasiljevíc, M. Kelly, G. Lea, and M. Courtney, 2015: Addressing spatial variability of surface-layer wind with long-range WindScanners. J. Atmos. Oceanic Technol., 32, 518527, https://doi.org/10.1175/JTECH-D-14-00123.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bingöl, F., J. Mann, and D. Foussekis, 2009: Conically scanning lidar error in complex terrain. Meteor. Z., 18, 189195, https://doi.org/10.1127/0941-2948/2009/0368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonin, T. A., and W. A. Brewer, 2017: Detection of range-folded returns in Doppler lidar observations. IEEE Geosci. Remote Sens. Lett., 14, 514518, https://doi.org/10.1109/LGRS.2017.2652360.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonin, T. A., and Coauthors, 2017: Evaluation of turbulence measurement techniques from a single Doppler lidar. Atmos. Meas. Tech., 10, 30213039, https://doi.org/10.5194/amt-10-3021-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonin, T. A., B. J. Carroll, R. M. Hardesty, W. A. Brewer, K. Hajny, O. E. Salmon, and P. B. Shepson, 2018: Doppler lidar observations of the mixing height in Indianapolis using an automated composite fuzzy logic approach. J. Atmos. Oceanic Technol., 35, 473490, https://doi.org/10.1175/JTECH-D-17-0159.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Browning, K. A., and R. Wexler, 1968: The determination of kinematic properties of a wind field using Doppler radar. J. Appl. Meteor., 7, 105113, https://doi.org/10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Djalalova, I. V., and Coauthors, 2016: The POWER experiment: Impact of assimilation of a network of coastal wind profiling radars on simulating offshore winds in and above the wind turbine layer. Wea. Forecasting, 31, 10711091, https://doi.org/10.1175/WAF-D-15-0104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Draxl, C., A. N. Hahmann, A. Pena, and G. Giebel, 2014: Evaluating winds and vertical shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes. Wind Energy, 17, 3955, https://doi.org/10.1002/we.1555.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drechsel, S., G. J. Mayr, J. W. Messner, and R. Stauffer, 2012: Lower boundary wind speeds: Measurements and verification of forecasts. J. Appl. Meteor. Climatol., 51, 16021617, https://doi.org/10.1175/JAMC-D-11-0247.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fast, J. D., and L. S. Darby, 2004: An evaluation of mesoscale model predictions of down-valley and canyon flows and their consequences using Doppler lidar measurements during VTMX 2000. J. Appl. Meteor., 43, 420436, https://doi.org/10.1175/1520-0450(2004)043<0420:AEOMMP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernández-González, S., M. L. Martín, E. García-Ortega, A. Merino, J. Lorenzana, J. L. Sánchez, F. Valero, and J. S. Rodrigo, 2018: Sensitivity analysis of the WRF model: Wind-resource assessment for complex terrain. J. Appl. Meteor. Climatol., 57, 733753, https://doi.org/10.1175/JAMC-D-17-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fernando, H. J. S., and J. C. Weil, 2010: Whither the stable boundary layer? A shift in research agenda. Bull. Amer. Meteor. Soc., 91, 14751484, https://doi.org/10.1175/2010BAMS2770.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • GE Energy, 2009: 1.5MW wind turbine. General Electric Rep., 12 pp., http://geosci.uchicago.edu/~moyer/GEOS24705/Readings/GEA14954C15-MW-Broch.pdf.

  • Kelly, M., I. Troen, and H. E. Jørgensen, 2014: Weibull-k revisited: “Tall” profiles and height variation of wind statistics. Bound.-Layer Meteor., 152, 107124, https://doi.org/10.1007/s10546-014-9915-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klaas, T., L. Pauscher, and D. Callies, 2015: LiDAR-mast deviations in complex terrain and their simulation using CFD. Meteor. Z., 24, 591603, https://doi.org/10.1127/metz/2015/0637.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, R., R. Calhoun, B. Billings, and J. Doyle, 2011: Wind turbulence estimates in a valley by coherent Doppler lidar. Meteor. Appl., 18, 361371, https://doi.org/10.1002/met.263.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krogsæter, O., and J. Reuder, 2015: Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications—Part I: Average wind speed and wind shear. Wind Energy, 18, 769782, https://doi.org/10.1002/we.1727.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lhermitte, R., and D. Atlas, 1961: Precipitation motion by pulse Doppler radar. Preprints, Ninth Radar Meteorology Conf., Kansas City, MO, Amer. Meteor. Soc., 218–223.

  • Mann, J., and Coauthors, 2017: Complex terrain experiments in the New European Wind Atlas. Philos. Trans. Roy. Soc., A375, 20160101, https://doi.org/10.1098/rsta.2016.0101.

    • Search Google Scholar
    • Export Citation
  • Mann, J., R. Menke, N. Vasiljević, J. Berg, and N. Troldborg, 2018: Challenges in using scanning lidars to estimate wind resources in complex terrain. J. Phys.: Conf. Ser., 1037, 072017, https://doi.org/10.1088/1742-6596/1037/7/072017.

    • Search Google Scholar
    • Export Citation
  • Marquis, M., J. Wilczak, M. Ahlstrom, J. Sharp, A. Stern, J. C. Smith, and S. Calvert, 2011: Forecasting the wind to reach significant penetration levels of wind energy. Bull. Amer. Meteor. Soc., 92, 11591171, https://doi.org/10.1175/2011BAMS3033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., W. A. Brewer, J. M. Wilczak, D. E. Wolfe, S. P. Oncley, and J. K. Lundquist, 2017: Validating precision estimates in horizontal wind measurements from a Doppler lidar. Atmos. Meas. Tech., 10, 12291240, https://doi.org/10.5194/amt-10-1229-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olson, J., and Coauthors, 2019: Improving wind energy forecasting through numerical weather prediction model development. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-18-0040.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pauscher, L., and Coauthors, 2016: An inter-comparison study of multi- and DBS lidar measurements in complex terrain. Remote Sens., 8, 782, https://doi.org/10.3390/rs8090782.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Peckham, S. E., T. G. Smirnova, S. G. Benjamin, J. M. Brown, and J. S. Kenyon, 2016: Implementation of a digital filter initialization in the WRF model and its application in the Rapid Refresh. Mon. Wea. Rev., 144, 99106, https://doi.org/10.1175/MWR-D-15-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., and R. M. Banta, 2010: Stable boundary-layer depth from high-resolution measurements of the mean wind profile. J. Appl. Meteor. Climatol., 49, 2035, https://doi.org/10.1175/2009JAMC2168.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., R. M. Banta, N. D. Kelley, B. J. Jonkman, S. C. Tucker, R. K. Newsom, and W. A. Brewer, 2008: Horizontal-velocity and variance measurements in the stable boundary layer using Doppler lidar: Sensitivity to averaging procedures. J. Atmos. Oceanic Technol., 25, 13071327, https://doi.org/10.1175/2008JTECHA988.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., R. M. Banta, W. A. Brewer, S. P. Sandberg, and R. M. Hardesty, 2012: Doppler lidar–based wind-profile measurement system for offshore wind-energy and other marine boundary layer applications. J. Appl. Meteor. Climatol., 51, 327349, https://doi.org/10.1175/JAMC-D-11-040.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., and Coauthors, 2017a: Properties of offshore low-level jet and rotor layer wind shear as measured by scanning Doppler lidar. Wind Energy, 20, 9871002, https://doi.org/10.1002/we.2075.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., and Coauthors, 2017b: Assessment of NWP forecast models in simulating offshore winds through the lower boundary layer by measurements from a ship-based scanning Doppler lidar. Mon. Wea. Rev., 145, 42774301, https://doi.org/10.1175/MWR-D-16-0442.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Post, M. J., C. J. Grund, D. Wang, and T. Deschler, 1997: Evolution of Mount Pinatubo’s aerosol size distributions over the continental United States: Two-wavelength lidar retrievals and in situ measurements. J. Geophys. Res., 102, 13 53513 542, https://doi.org/10.1029/97JD00644.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Risan, A., J. A. Lund, C.-Y. Chang, and L. Sætran, 2018: Wind in complex terrain—Lidar measurements for evaluation of CFD simulations. Remote Sens., 10, 59, https://doi.org/10.3390/rs10010059.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rye, B. J., and R. M. Hardesty, 1993: Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I. Spectral accumulation and the Cramer–Rao lower bound. IEEE Trans. Geosci. Remote Sens., 31, 1627, https://doi.org/10.1109/36.210440.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schreck, S., J. K. Lundquist, and W. Shaw, 2008: Research needs for wind resource characterization. U.S. Department of Energy Workshop/NREL Rep. TP-500-43521, 116 pp.

  • Seaman, N., 2000: Meteorological modeling for air-quality assessments. Atmos. Environ., 34, 22312259, https://doi.org/10.1016/S1352-2310(99)00466-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharp, J., and C. Mass, 2002: Columbia Gorge gap flow: Insights from observational analysis and ultra-high-resolution simulation. Bull. Amer. Meteor. Soc., 83, 17571762, https://doi.org/10.1175/BAMS-83-12-1757.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sharp, J., and C. Mass, 2004: Columbia Gorge gap winds: Their climatological influence and synoptic evolution. Wea. Forecasting, 19, 970992, https://doi.org/10.1175/826.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, W. J., J. K. Lundquist, and S. J. Schreck, 2009: Research needs for wind resource characterization. Bull. Amer. Meteor. Soc., 90, 535538, https://doi.org/10.1175/2008BAMS2729.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, W. J., and Coauthors, 2019: The Second Wind Forecast Improvement Project (WFIP2): General overview. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-18-0036.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., 2004: Evaluating mesoscale NWP models using kinetic energy spectra. Mon. Wea. Rev., 132, 30193032, https://doi.org/10.1175/MWR2830.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smalikho, I. N., V. A. Banakh, Y. L. Pichugina, and W. A. Brewer, 2013: Accuracy of estimation of the turbulent energy dissipation rate from wind measurements with a conically scanning pulsed coherent Doppler lidar. Part II. Numerical and atmospheric experiments. Atmos. Oceanic Opt., 26, 411416, https://doi.org/10.1134/S1024856013050151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Troen, I., and E. L. Petersen, 1989: European Wind Atlas. Risø National Laboratory, 656 pp.

  • Whiteman, C. D., S. Zhong, W. J. Shaw, J. M. Hubbe, X. Bian, and J. Mittelstadt, 2001: Cold pools in the Columbia basin. Wea. Forecasting, 16, 432447, https://doi.org/10.1175/1520-0434(2001)016<0432:CPITCB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., and Coauthors, 2015: The Wind Forecast Improvement Project (WFIP): A public–private partnership addressing wind energy forecast needs. Bull. Amer. Meteor. Soc., 96, 16991718, https://doi.org/10.1175/BAMS-D-14-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., and Coauthors, 2019: The Second Wind Forecast Improvement Project (WFIP2): Observational field campaign. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-18-0035.1, in press.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, B., and Coauthors, 2017: Sensitivity of turbine-height wind speeds to parameters in planetary boundary-layer and surface-layer schemes in the weather research and forecasting model. Bound.-Layer Meteor., 162, 117142, https://doi.org/10.1007/s10546-016-0185-2.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhong, S., C. D. Whiteman, X. Bian, W. J. Shaw, and J. M. Hubbe, 2001: Meteorological processes affecting the evolution of a wintertime cold-air pool in the Columbia basin. Mon. Wea. Rev., 129, 26002613, https://doi.org/10.1175/1520-0493(2001)129<2600:MPATEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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