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Numerical Prediction of Submesoscale Flow in the Nocturnal Stable Boundary Layer over Complex Terrain

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  • 1 Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
  • | 2 College of Oceanic and Atmospheric Sciences, Oregon State University, Corvallis, Oregon
  • | 3 Department of Meteorology, The Pennsylvania State University, University Park, Pennsylvania
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Abstract

Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities.

To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

Corresponding author address: Nelson Seaman, Dept. of Meteorology, The Pennsylvania State University, University Park, PA 16802. E-mail: seaman@ems.psu.edu

Abstract

Numerical weather prediction models often perform poorly for weakly forced, highly variable winds in nocturnal stable boundary layers (SBLs). When used as input to air-quality and dispersion models, these wind errors can lead to large errors in subsequent plume forecasts. Finer grid resolution and improved model numerics and physics can help reduce these errors. The Advanced Research Weather Research and Forecasting model (ARW-WRF) has higher-order numerics that may improve predictions of finescale winds (scales <~20 km) in nocturnal SBLs. However, better understanding of the physics controlling SBL flow is needed to take optimal advantage of advanced modeling capabilities.

To facilitate ARW-WRF evaluations, a small network of instrumented towers was deployed in the ridge-and-valley topography of central Pennsylvania (PA). Time series of local observations and model forecasts on 1.333- and 0.444-km grids were filtered to isolate deterministic lower-frequency wind components. The time-filtered SBL winds have substantially reduced root-mean-square errors and biases, compared to raw data. Subkilometer horizontal and very fine vertical resolutions are found to be important for reducing model speed and direction errors. Nonturbulent fluctuations in unfiltered, very finescale winds, parts of which may be resolvable by ARW-WRF, are shown to generate horizontal meandering in stable weakly forced conditions. These submesoscale motions include gravity waves, primarily horizontal 2D motions, and other complex signatures. Vertical structure and low-level biases of SBL variables are shown to be sensitive to parameter settings defining minimum “background” mixing in very stable conditions in two representative turbulence schemes.

Corresponding author address: Nelson Seaman, Dept. of Meteorology, The Pennsylvania State University, University Park, PA 16802. E-mail: seaman@ems.psu.edu
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