• Beesley, J. A., C. S. Bretherton, C. Jakob, E. L Andreas, J. M. Intrieri, and T. A. Uttal, 2000: A comparison of cloud and boundary layer variables in the ECMWF forecast model with observations at Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp. J. Geophys. Res., 105, 12 33712 349, https://doi.org/10.1029/2000JD900079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and et al. , 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
  • de Boer, G., W. Chapman, J. E. Kay, B. Medeiros, M. D. Shupe, S. Vavrus, and J. Walsh, 2012: A characterization of the present-day Arctic atmosphere in CCSM4. J. Climate, 25, 26762695, https://doi.org/10.1175/JCLI-D-11-00228.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., M. D. Shupe, P. M. Caldwell, S. E. Bauer, O. Persson, J. S. Boyle, S. A. Klein, and M. Tjernström, 2014: Near-surface meteorology during the Arctic Summer Cloud Ocean Study (ASCOS): Evaluation of reanalysis and global climate models. Atmos. Chem. Phys., 14, 427445, https://doi.org/10.5194/acp-14-427-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., and et al. , 2018: A bird’s-eye view: Development of an operational ARM unmanned aerial capability for atmospheric research in Arctic Alaska. Bull. Amer. Meteor. Soc., 99, 11971212, https://doi.org/10.1175/BAMS-D-17-0156.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Boer, G., and et al. , 2019: Atmospheric observations made at Oliktok point, Alaska, as part of the Profiling at Oliktok Point to Enhance YOPP Experiments (POPEYE) campaign. Earth Syst. Sci. Data, 11, 13491362, https://doi.org/10.5194/essd-11-1349-2019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and S. Freitas, 2014: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmos. Chem. Phys., 14, 52335250, https://doi.org/10.5194/acp-14-5233-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hogan, R., and et al. , 2017: Radiation in numerical weather prediction. ECMWF Tech. Memo. 816, 51 pp., https://doi.org/10.21957/2bd5dkj8x.

    • Crossref
    • Export Citation
  • Holtslag, A. A. M., and et al. , 2013: Stable atmospheric boundary layers and diurnal cycles: Challenges for weather and climate models. Bull. Amer. Meteor. Soc., 94, 16911706, https://doi.org/10.1175/BAMS-D-11-00187.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keeler, E., R. Coulter, J. Kyrouac, and D. Holdridge, 2015: Balloon-Borne Sounding System (SONDEWNPN). Atmospheric Radiation Measurement (ARM) user facility, accessed 30 May 2019, https://doi.org/10.5439/1021460.

    • Crossref
    • Export Citation
  • Kleczek, M. A., G. J. Steeneveld, and A. A. M. Holtslag, 2014: Evaluation of the weather research and forecasting mesoscale model for GABLS3: Impact of boundary-layer schemes, boundary conditions and spin-up. Bound.-Layer Meteor., 152, 213243, https://doi.org/10.1007/s10546-014-9925-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, S., and et al. , 2009: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: Single-layered cloud. Quart. J. Roy. Meteor. Soc., 135, 9791002, https://doi.org/10.1002/qj.416.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, and S. Lord, 2009: Introduction of the GSI into the NCEP global data assimilation system. Wea. Forecasting, 24, 16911705, https://doi.org/10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Komurcu, M., T. Storelvmo, I. Tan, U. Lohmann, Y. Yun, J. E. Penner, and T. Takemura, 2014: Intercomparison of the cloud water phase among global climate models. J. Geophys. Res. Atmos., 119, 33723400, https://doi.org/10.1002/2013JD021119.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kyrouac, J., and D. Holdridge, 2015: Surface Meteorological Instrumentation (MET). Atmospheric Radiation Measurement (ARM) user facility, accessed 30 May 2019, https://doi.org/10.5439/1025220.

    • Crossref
    • Export Citation
  • Lindsay, R., M. Wensnahan, A. Schweiger, and J. Zhang, 2014: Evaluation of seven different atmospheric reanalysis products in the Arctic. J. Climate, 27, 25882606, https://doi.org/10.1175/JCLI-D-13-00014.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Long, C. N. and Y. Shi, 2006: The QCRad value-added product: Surface radiation measurement quality control testing, including climatologically configurable limits. Atmospheric Radiation Measurement Tech. Rep., 69 pp.

    • Crossref
    • Export Citation
  • McCorkle, T. A., J. D. Horel, A. A. Jacques, and T. Alcott, 2018: Evaluating the experimental high-resolution rapid refresh-Alaska modeling system using USArray pressure observations. Wea. Forecasting, 33, 933953, https://doi.org/10.1175/WAF-D-17-0155.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, N. B., D. D. Turner, R. Bennartz, M. D. Shupe, M. S. Kulie, M. P. Cadeddu, and V. P. Walden, 2013: Surface-based inversions above central Greenland. J. Geophys. Res. Atmos., 118, 495506, https://doi.org/10.1029/2012JD018867.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Traubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Morris, V., and B. Ermold, 2015: Ceilometer (CEIL). Atmospheric Radiation Measurement (ARM) user facility, accessed 17 June 2019, https://doi.org/10.5439/1181954.

    • Crossref
    • Export Citation
  • Olson, J. B., J. S. Kenyon, W. A. Angevine, J. M. Brown, M. Pagowski, and K. Siuselj, 2019: A description of the MYNN-EDMF scheme and coupling to other components in WRF-ARW. NOAA Tech. Memo. OAR GSD-61, 42 pp., https://doi.org/10.25923/n9wm-be49.

    • Crossref
    • 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
  • Pinto, J. O., D. L. Megenhardt, T. Fowler, and J. Colavito, 2020: Biases in the mesoscale prediction of ceiling and visibility in Alaska and their reduction using quantile matching. Wea. Forecasting, 35, 9971016, https://doi.org/10.1175/WAF-D-19-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riihimaki, L., Y. Shi, D. Zhang, and C. Long, 2019: Data Quality Assessment for ARM Radiation Data (QCRAD1LONG). Atmospheric Radiation Measurement (ARM) user facility, accessed 23 December 2020, https://doi.org/10.5439/1027372.

    • Crossref
    • Export Citation
  • Shupe, M. D., 2011: Clouds at Arctic atmospheric observatories: Part II: Thermodynamic phase characteristics. J. Appl. Meteor. Climatol., 50, 645661, https://doi.org/10.1175/2010JAMC2468.1.

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

    • Crossref
    • Export Citation
  • Skamarock, W. C., C. Snyder, J. B. Klemp, and S. Park, 2019: Vertical resolution requirements in atmospheric simulation. Mon. Wea. Rev., 147, 26412656, https://doi.org/10.1175/MWR-D-19-0043.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smirnova, T. G., J. M. Brown, S. G. Benjamin, and J. S. Kenyon, 2016: Modifications to the Rapid Update Cycle land surface model (RUC LSM) available in the Weather Research and Forecasting (WRF) Model. Mon. Wea. Rev., 144, 18511865, https://doi.org/10.1175/MWR-D-15-0198.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sotiropoulou, G., J. Sedlar, R. Forbes, and M. Tjernström, 2016: Summer Arctic clouds in the ECMWF forecast model: An evaluation of cloud parameterization schemes. Quart. J. Roy. Meteor. Soc., 142, 387400, https://doi.org/10.1002/qj.2658.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steeneveld, G. J., B. J. H. van de Wiel, and A. A. M. Holtslag, 2006: Modelling the Arctic stable boundary layer and its coupling to the surface. Bound.-Layer Meteor., 118, 357378, https://doi.org/10.1007/s10546-005-7771-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, G., and T. Eidhammer, 2014: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J. Atmos. Sci., 71, 36363658, https://doi.org/10.1175/JAS-D-13-0305.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tjernström, M., J. Sedlar, and M. D. Shupe, 2008: How well do regional climate models reproduce radiation and clouds in the Arctic? An evaluation of ARCMIP simulations. J. Appl. Meteor. Climatol., 47, 24052422, https://doi.org/10.1175/2008JAMC1845.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turner, D. D., and et al. , 2020: A verification approach used in developing the Rapid Refresh and other numerical weather prediction models. J. Oper. Meteor., 8, 3953, https://doi.org/10.15191/nwajom.2020.0803.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uttal, T., and et al. , 2016: International Arctic Systems for Observing the Atmosphere: An international polar year legacy consortium. Bull. Amer. Meteor. Soc., 97, 10331056, https://doi.org/10.1175/BAMS-D-14-00145.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verlinde, J., and et al. , 2007: The mixed-phase Arctic cloud experiment. Bull. Amer. Meteor. Soc., 88, 205222, https://doi.org/10.1175/BAMS-88-2-205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verlinde, J., B. D. Zak, M. D. Shupe, M. D. Ivey, and K. Stamnes, 2016: The ARM North Slope of Alaska (NSA) sites. The Atmospheric Radiation Measurement Program: The First 20 Years, Meteor. Monogr., No. 57, Amer. Meteor. Soc., https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0023.1.

    • Crossref
    • Export Citation
  • Wesslén, C., M. Tjernstrom, D. H. Bromwich, G. de Boer, L.-S. Bai, and S.-H. Wang, 2014: The Arctic summer atmosphere: An evaluation of reanalyses using ASCOS data. Atmos. Chem. Phys., 14, 26052624, https://doi.org/10.5194/acp-14-2605-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP global forecast system. Mon. Wea. Rev., 136, 463482, https://doi.org/10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, A. B., D. H. Bromwich, and K. M. Hines, 2011: Evaluation of polar WRF forecasts on the Arctic System Reanalysis domain: Surface and upper air analysis. J. Geophys. Res., 116, D11112, https://doi.org/10.1029/2010JD015013.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Evaluation of the Rapid Refresh Numerical Weather Prediction Model over Arctic Alaska

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  • 1 a School of Meteorology, University of Oklahoma, Norman, Oklahoma
  • | 2 b Global Systems Laboratory, NOAA, Boulder, Colorado
  • | 3 c Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
  • | 4 d Physical Sciences Laboratory, NOAA, Boulder, Colorado
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Abstract

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

Significance Statement

Human activities continue to expand into northern high latitudes, including shipping and other commercial activities, energy exploration, tourism, and defense. Conducting these activities safely requires accurate weather forecasting tools capable of handling a harsh and complex environment. This work evaluates the performance of one of the primary weather forecasting models used in the United States for short-term decision-making, the Rapid Refresh (RAP) model, run operationally by the National Weather Service. This effort illustrates some promising results, while at the same time bringing some shortcomings to light. Importantly, it highlights some areas in which the RAP can improve to support northern communities and commercial entities through the development of accurate weather forecasts.

© 2021 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: Matthew Bray, matthewbray1@ou.edu

Abstract

Despite a need for accurate weather forecasts for societal and economic interests in the U.S. Arctic, thorough evaluations of operational numerical weather prediction in the region have been limited. In particular, the Rapid Refresh Model (RAP), which plays a key role in short-term forecasting and decision-making, has seen very limited assessment in northern Alaska, with most evaluation efforts focused on lower latitudes. In the present study, we verify forecasts from version 4 of the RAP against radiosonde, surface meteorological, and radiative flux observations from two Arctic sites on the northern Alaskan coastline, with a focus on boundary layer thermodynamic and dynamic biases, model representation of surface inversions, and cloud characteristics. We find persistent seasonal thermodynamic biases near the surface that vary with wind direction, and may be related to the RAP’s handling of sea ice and ocean interactions. These biases seem to have diminished in the latest version of the RAP (version 5), which includes refined handling of sea ice, among other improvements. In addition, we find that despite capturing boundary layer temperature profiles well overall, the RAP struggles to consistently represent strong, shallow surface inversions. Further, while the RAP seems to forecast the presence of clouds accurately in most cases, there are errors in the simulated characteristics of these clouds, which we hypothesize may be related to the RAP’s treatment of mixed-phase clouds.

Significance Statement

Human activities continue to expand into northern high latitudes, including shipping and other commercial activities, energy exploration, tourism, and defense. Conducting these activities safely requires accurate weather forecasting tools capable of handling a harsh and complex environment. This work evaluates the performance of one of the primary weather forecasting models used in the United States for short-term decision-making, the Rapid Refresh (RAP) model, run operationally by the National Weather Service. This effort illustrates some promising results, while at the same time bringing some shortcomings to light. Importantly, it highlights some areas in which the RAP can improve to support northern communities and commercial entities through the development of accurate weather forecasts.

© 2021 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: Matthew Bray, matthewbray1@ou.edu
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