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coherent Doppler lidars were operated by the Arizona State University (ASU) and by the Institute of Atmospheric Physics of the German Aerospace Center (DLR), Oberpfaffenhofen, respectively. A proven algorithm for the 3D wind retrieval from multiple Doppler radars was applied to the dual-lidar observations. We chose the Multiple Doppler Synthesis and Continuity Adjustment Technique (MUSCAT) since it provides stable solutions and can be used over complex terrain. A brief description of MUSCAT will be
coherent Doppler lidars were operated by the Arizona State University (ASU) and by the Institute of Atmospheric Physics of the German Aerospace Center (DLR), Oberpfaffenhofen, respectively. A proven algorithm for the 3D wind retrieval from multiple Doppler radars was applied to the dual-lidar observations. We chose the Multiple Doppler Synthesis and Continuity Adjustment Technique (MUSCAT) since it provides stable solutions and can be used over complex terrain. A brief description of MUSCAT will be
Abstract
The conceptual model of an atmospheric rotor is reexamined in the context of a valley, using data from the Terrain-Induced Rotor Experiment (T-REX) conducted in 2006 in the southern Sierra Nevada and Owens Valley, California. All T-REX cases with strong mountain-wave activity have been investigated, and four of them (IOPs 1, 4, 6, and 13) are presented in detail. Their analysis reveals a rich variety of rotorlike turbulent flow structures that may form in the valley during periods of strong cross-mountain winds. Typical flow scenarios in the valley include elevated turbulence zones, downslope flow separation at a valley inversion, turbulent interaction of in-valley westerlies and along-valley flows, and highly transient mountain waves and rotors. The scenarios can be related to different stages of the passage of midlatitude frontal systems across the region. The observations from Owens Valley show that the elements of the classic rotor concept are modulated and, at times, almost completely offset by dynamically and thermally driven processes in the valley. Strong lee-side pressure perturbations induced by large-amplitude waves, commonly regarded as the prerequisite for flow separation, are found to be only one of the factors controlling rotor formation and severe turbulence generation in the valley. Buoyancy perturbations in the thermally layered valley atmosphere appear to play a role in many of the observed cases. Based on observational evidence from T-REX, extensions to the classic rotor concept, appropriate for a long deep valley, are proposed.
Abstract
The conceptual model of an atmospheric rotor is reexamined in the context of a valley, using data from the Terrain-Induced Rotor Experiment (T-REX) conducted in 2006 in the southern Sierra Nevada and Owens Valley, California. All T-REX cases with strong mountain-wave activity have been investigated, and four of them (IOPs 1, 4, 6, and 13) are presented in detail. Their analysis reveals a rich variety of rotorlike turbulent flow structures that may form in the valley during periods of strong cross-mountain winds. Typical flow scenarios in the valley include elevated turbulence zones, downslope flow separation at a valley inversion, turbulent interaction of in-valley westerlies and along-valley flows, and highly transient mountain waves and rotors. The scenarios can be related to different stages of the passage of midlatitude frontal systems across the region. The observations from Owens Valley show that the elements of the classic rotor concept are modulated and, at times, almost completely offset by dynamically and thermally driven processes in the valley. Strong lee-side pressure perturbations induced by large-amplitude waves, commonly regarded as the prerequisite for flow separation, are found to be only one of the factors controlling rotor formation and severe turbulence generation in the valley. Buoyancy perturbations in the thermally layered valley atmosphere appear to play a role in many of the observed cases. Based on observational evidence from T-REX, extensions to the classic rotor concept, appropriate for a long deep valley, are proposed.
of the wave over the valley, a least squares sinusoidal fit algorithm was developed. At each time and altitude level, the vertical velocities from the three wind profilers were fit to the function where x is eastward distance relative to ISS2 and A , λλ , and φφ are the amplitude, wavelength, and phase (also relative to ISS2) of the best fit wave, respectively. This fit is of vertical velocity, not vertical displacement, which would be shifted in phase by ππ /2. The algorithm for finding
of the wave over the valley, a least squares sinusoidal fit algorithm was developed. At each time and altitude level, the vertical velocities from the three wind profilers were fit to the function where x is eastward distance relative to ISS2 and A , λλ , and φφ are the amplitude, wavelength, and phase (also relative to ISS2) of the best fit wave, respectively. This fit is of vertical velocity, not vertical displacement, which would be shifted in phase by ππ /2. The algorithm for finding
-barrier” direction; Fig. 2 shows the relative locations of the lidars and their ranges on the plane of interest.) This allowed velocity vectors to be retrieved using the least squares algorithm described below. The scanned plane was well positioned to sample vortices with horizontal axes parallel to the north–south mountain range; that is, an expected orientation for vortices formed due to topographically induced shear layers (see Calhoun and Street 2001 ; Calhoun et al. 2001 ). The retrieved two
-barrier” direction; Fig. 2 shows the relative locations of the lidars and their ranges on the plane of interest.) This allowed velocity vectors to be retrieved using the least squares algorithm described below. The scanned plane was well positioned to sample vortices with horizontal axes parallel to the north–south mountain range; that is, an expected orientation for vortices formed due to topographically induced shear layers (see Calhoun and Street 2001 ; Calhoun et al. 2001 ). The retrieved two
improvements are achieved with a smoothing algorithm that includes the second level of range gates (not shown). This smoothing algorithm is expected to reduce errors that arise as a result of the numerical approximations in Eqs. (2) and (3) and as a result of the small-scale variability of the flow. However, the smoothing procedure does not remove errors arising from large-scale lateral flow divergence. Without the along-beam smoothing, the method produces spurious structures of V ϕ in some locations
improvements are achieved with a smoothing algorithm that includes the second level of range gates (not shown). This smoothing algorithm is expected to reduce errors that arise as a result of the numerical approximations in Eqs. (2) and (3) and as a result of the small-scale variability of the flow. However, the smoothing procedure does not remove errors arising from large-scale lateral flow divergence. Without the along-beam smoothing, the method produces spurious structures of V ϕ in some locations
long T-REX field experiment. The program then searched for rises on these days in three time windows: 1700–2010, 1700–2055, and 1700–2140 PST. For each time window, the absolute minimum was selected from the relative minima and then the largest relative maximum occurring after the absolute minimum was identified. The use of three time windows improved the performance of the algorithm for finding the large, singular rises of interest, especially when weak temperature oscillations accompanied
long T-REX field experiment. The program then searched for rises on these days in three time windows: 1700–2010, 1700–2055, and 1700–2140 PST. For each time window, the absolute minimum was selected from the relative minima and then the largest relative maximum occurring after the absolute minimum was identified. The use of three time windows improved the performance of the algorithm for finding the large, singular rises of interest, especially when weak temperature oscillations accompanied
by echoes from migrating birds (e.g., Wilczak et al. 1995 ), and there was some evidence of similar contamination here. Three filtering techinques were applied to mitigate these signals. First, the raw Doppler spectra signals were filtered during collection with the widely used statistical averaging method algorithm (SAM; Merritt 1995 ); second, NCAR Improved Moments Algorithm (NIMA) processing ( Morse et al. 2002 ) removed additional contaminated data; third, a final filter was applied to the
by echoes from migrating birds (e.g., Wilczak et al. 1995 ), and there was some evidence of similar contamination here. Three filtering techinques were applied to mitigate these signals. First, the raw Doppler spectra signals were filtered during collection with the widely used statistical averaging method algorithm (SAM; Merritt 1995 ); second, NCAR Improved Moments Algorithm (NIMA) processing ( Morse et al. 2002 ) removed additional contaminated data; third, a final filter was applied to the
models are nonhydrostatic with one making use of the anelastic equation set (EULAG), and the others solving the fully compressible equations. The vertical coordinates are terrain following. Significant differences among the models exist in the type of schemes used for the dynamics and physics. A brief description of some main characteristics of the models is given in Tables 1 and 2 . Table 1. Model formulation for dynamics and mixing. The finite difference algorithms are referred to as centered
models are nonhydrostatic with one making use of the anelastic equation set (EULAG), and the others solving the fully compressible equations. The vertical coordinates are terrain following. Significant differences among the models exist in the type of schemes used for the dynamics and physics. A brief description of some main characteristics of the models is given in Tables 1 and 2 . Table 1. Model formulation for dynamics and mixing. The finite difference algorithms are referred to as centered
. The pressure measurements from the car reduced to a common altitude with the slantwise algorithm of Mayr et al. (2002) documenting the resulting decrease of pressure toward the sierra as the radiation-induced driving force behind the circulation. A larger airmass difference with colder upstream potential temperatures (cf. Figs. 4a,b ) and an increase of cross-barrier wind speed let the flow reach Owens Valley floor (15 April 2004). The car measurements of pressure as an integral value of
. The pressure measurements from the car reduced to a common altitude with the slantwise algorithm of Mayr et al. (2002) documenting the resulting decrease of pressure toward the sierra as the radiation-induced driving force behind the circulation. A larger airmass difference with colder upstream potential temperatures (cf. Figs. 4a,b ) and an increase of cross-barrier wind speed let the flow reach Owens Valley floor (15 April 2004). The car measurements of pressure as an integral value of
between different variables. To do this, we interpolate the 1-s flight-level data onto a 200-m spatial grid with approximately 900 points. We remove the mean, taper the ends of the record, and apply a Fourier transform to compute the power and cross spectra [NCAR Command Language (NCL) algorithm specxy_anal]. For the vertical velocity, the Fourier transform is or the equivalent discrete transform for equally spaced data points The power spectrum is and cross-spectrum between, say, vertical velocity
between different variables. To do this, we interpolate the 1-s flight-level data onto a 200-m spatial grid with approximately 900 points. We remove the mean, taper the ends of the record, and apply a Fourier transform to compute the power and cross spectra [NCAR Command Language (NCL) algorithm specxy_anal]. For the vertical velocity, the Fourier transform is or the equivalent discrete transform for equally spaced data points The power spectrum is and cross-spectrum between, say, vertical velocity
