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because of the relatively weak influence of the Coriolis force here. In addition, the streamwise vertical momentum flux is controlled by wind shear in agreement with the turbulent-viscosity hypothesis ( Pope 2000 ): where the turbulent eddy viscosity is given by ν T . We have used the normal micrometeorological coordinate system convention that x is aligned with the near-surface mean wind direction—that is, U ( z ) is the mean wind with superimposed fluctuations u ′( x , y , z , t ), and
because of the relatively weak influence of the Coriolis force here. In addition, the streamwise vertical momentum flux is controlled by wind shear in agreement with the turbulent-viscosity hypothesis ( Pope 2000 ): where the turbulent eddy viscosity is given by ν T . We have used the normal micrometeorological coordinate system convention that x is aligned with the near-surface mean wind direction—that is, U ( z ) is the mean wind with superimposed fluctuations u ′( x , y , z , t ), and
1. Introduction Katabatic winds are airflows that occur above a cold sloped surface. They are driven by gravity that causes colder and more dense air masses to move downhill. As velocity increases, the Coriolis force declines the flow from the downhill direction. As was done in Vihma et al. (2011) , we define the katabatic wind as a downslope wind initially generated by surface cooling. The katabatic winds occur near a surface in the stably stratified atmospheric boundary layer (ABL) and have
1. Introduction Katabatic winds are airflows that occur above a cold sloped surface. They are driven by gravity that causes colder and more dense air masses to move downhill. As velocity increases, the Coriolis force declines the flow from the downhill direction. As was done in Vihma et al. (2011) , we define the katabatic wind as a downslope wind initially generated by surface cooling. The katabatic winds occur near a surface in the stably stratified atmospheric boundary layer (ABL) and have
-hourly ramp definition (definition 1). Ramps occur most frequently in spring with 21–24 up-ramp events per month as compared with only 11 and 13 per month in January and February, respectively. The down-ramp frequencies follow a similar pattern, with a slightly higher (by 2.6 per month) frequency. The larger ramp frequency in March–June when compared with winter likely results from ramps associated with non-synoptic-scale forcing, such as lower-atmosphere or local-surface thermal heterogeneity ( Kang
-hourly ramp definition (definition 1). Ramps occur most frequently in spring with 21–24 up-ramp events per month as compared with only 11 and 13 per month in January and February, respectively. The down-ramp frequencies follow a similar pattern, with a slightly higher (by 2.6 per month) frequency. The larger ramp frequency in March–June when compared with winter likely results from ramps associated with non-synoptic-scale forcing, such as lower-atmosphere or local-surface thermal heterogeneity ( Kang
appear differently in different azimuthal directions. Visible and infrared satellite imagery (not shown) indicates the presence of thin upper-level clouds across central Oklahoma. Observers from Tinker Air Force Base (AFB) and Oklahoma City at 1650 UTC reported cloud bases at 7.6 and 8.5 km AGL, respectively. These reported cloud bases agree well with the layer between 7.5 and 9.5 km in Fig. 1a , indicating the layer is produced by particulate scattering ( Z DR of about 1 dB) from nonprecipitating
appear differently in different azimuthal directions. Visible and infrared satellite imagery (not shown) indicates the presence of thin upper-level clouds across central Oklahoma. Observers from Tinker Air Force Base (AFB) and Oklahoma City at 1650 UTC reported cloud bases at 7.6 and 8.5 km AGL, respectively. These reported cloud bases agree well with the layer between 7.5 and 9.5 km in Fig. 1a , indicating the layer is produced by particulate scattering ( Z DR of about 1 dB) from nonprecipitating
according to the external forcing parameters, whereas we use purely morphological classification. Fig . 1. Typical shapes of 30-min wind speed profiles under stable conditions obtained by the sodar at ZSS. (top) Weak LLJ, strong stability; (middle) moderate LLJ; (bottom) strong LLJ, weak stability. The types b, f, and j were observed during 68% of winter and 50% of summer nights. They are often, but not always, accompanied by the appearance of distinct wave patterns in the form of inclined stripes of
according to the external forcing parameters, whereas we use purely morphological classification. Fig . 1. Typical shapes of 30-min wind speed profiles under stable conditions obtained by the sodar at ZSS. (top) Weak LLJ, strong stability; (middle) moderate LLJ; (bottom) strong LLJ, weak stability. The types b, f, and j were observed during 68% of winter and 50% of summer nights. They are often, but not always, accompanied by the appearance of distinct wave patterns in the form of inclined stripes of
(black), measured (dark gray), and ± σ confidence bounds (light gray shading). Maximum wavenumber (dashed). 6. Lidar system optimization The proposed model can be used to optimize the configuration or the scanning pattern of a lidar system. As an example the model is used to determine the optimum correlation of a lidar system with three independent beams on a turbine with D = 40 m. For this purpose, the measurement distance x and the scan radius r are varied in a brute force optimization
(black), measured (dark gray), and ± σ confidence bounds (light gray shading). Maximum wavenumber (dashed). 6. Lidar system optimization The proposed model can be used to optimize the configuration or the scanning pattern of a lidar system. As an example the model is used to determine the optimum correlation of a lidar system with three independent beams on a turbine with D = 40 m. For this purpose, the measurement distance x and the scan radius r are varied in a brute force optimization
tool display derived from a prototype ARO deployed in Sonoma County, California, as part of the HMT 2008/09 field season. This display, developed jointly by operational weather forecasters and HMT research scientists, combines observations with numerical weather prediction output to help monitor and forecast the forcings associated with landfalling ARs. Weather forecasters and other end users can use this tool to verify how well the HMT weather forecast model is portraying the AR conditions and the
tool display derived from a prototype ARO deployed in Sonoma County, California, as part of the HMT 2008/09 field season. This display, developed jointly by operational weather forecasters and HMT research scientists, combines observations with numerical weather prediction output to help monitor and forecast the forcings associated with landfalling ARs. Weather forecasters and other end users can use this tool to verify how well the HMT weather forecast model is portraying the AR conditions and the
