Improved Representation of Horizontal Variability and Turbulence in Mesoscale Simulations of an Extended Cold-Air Pool Event

Journal Article · Wed Jun 01 00:00:00 EDT 2022 · Journal of Applied Meteorology and Climatology

DOI:https://doi.org/10.1175/JAMC-D-21-0138.1· OSTI ID:1962971

Arthur, Robert S. ^[1]; Juliano, Timothy W. ^[2]; Adler, Bianca ^[3]; Krishnamurthy, Raghavendra ^[4]; Lundquist, Julie K. ^[5]; Kosović, Branko ^[2]; Jiménez, Pedro A. ^[2]

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1. a Lawrence Livermore National Laboratory, Livermore, California
2. b Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
3. c Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, d NOAA/Physical Sciences Laboratory, Boulder, Colorado
4. e Pacific Northwest National Laboratory, Richland, Washington
5. f Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado, g National Renewable Energy Laboratory, Golden, Colorado, h Renewable and Sustainable Energy Institute, Boulder, Colorado

Abstract

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models in part because CAPs occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effects of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) Model. Model runs with a one-dimensional (1D) PBL scheme and Smagorinsky-like horizontal diffusion are compared with runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3-km/750-m horizontal grid spacing over a 10-day case study in the Columbia River basin, and results are compared with observations from the Second Wind Forecast Improvement Project. Using event-averaged error metrics, potential temperature and wind speed errors are shown to decrease both with increased horizontal grid resolution and with improved treatment of horizontal diffusion over steep terrain. The 3D PBL scheme further reduces errors relative to a standard 1D PBL approach. Error reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Last, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event. The sensitivity of turbulence predictions to the master length-scale formulation in the 3D PBL parameterization is also explored.

Significance Statement

In this article, we demonstrate how a new framework for modeling atmospheric turbulence improves cold pool predictions, using a case study from January 2017 in the Columbia River basin (U.S. Pacific Northwest). Cold pools are regions of cold, stagnant air that form within valleys or basins, and improved forecasts could help to mitigate the risks they pose to air quality, transportation, and wind energy production. For the chosen case study, our tests show a reduction in temperature and wind speed errors by up to a factor of 2–3 relative to standard model options. These results strongly motivate continued development of the framework as well as its application to other complex weather events.

• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
• OSTI ID: 1962971
• Alternate ID(s): OSTI ID: 1871790; OSTI ID: 1872314; OSTI ID: 1876011
• Journal Information: Journal of Applied Meteorology and Climatology, Journal Name: Journal of Applied Meteorology and Climatology Journal Issue: 6 Vol. 61; ISSN 1558-8424
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English