Measurement of Clear-Air Gradients and Turbulence Properties with Radar Wind Profilers

Gossard, Earl E.; Wolfe, D. E.; Moran, K. P.; Paulus, R. A.; Anderson, Kenneth D.; Rogers, L.T.

doi:10.1175/1520-0426(1998)015<0321:mocaga>2.0.co;2

Cited by 59 publications

(49 citation statements)

References 37 publications

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“…(6) (Tsuda et al, 2001). Some authors (Gossard et al, 1998;Stankov et al, 2003) assume that the partial derivatives in Eqs. (4) and (5) are constant and can be estimated from standard atmosphere profiles.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Over the last 30 years, several authors (Gossard et al, 1982(Gossard et al, , 1998Tsuda et al, 2001;Bianco et al, 2005;Stankov et al, 1996Stankov et al, , 2003Furumoto et al, 2003Furumoto et al, , 2007Klaus et al, 2006;Imura et al, 2007) discussed the possibility of determining the magnitude of the humidity gradient profiles from measurements of zeroth, first and second moments of wind profiler radars (WPR) Doppler spectra in either the ultra high frequency (UHF) or the very high frequency (VHF) range. The method exploits the clear-air scattering properties of electromagnetic waves, depending on the refractive index and consequently on the thermodynamic properties (pressure, temperature, humidity) of clear air.…”

Section: Introductionmentioning

confidence: 99%

“…The method exploits the clear-air scattering properties of electromagnetic waves, depending on the refractive index and consequently on the thermodynamic properties (pressure, temperature, humidity) of clear air. In addition, most of these authors demonstrated the possibility to retrieve humidity profiles by combining radar measurements with simultaneous measurements from other sensors, i.e., in situ radiosonde observations at a poorer time resolution (Tsuda et al, 2001;Stankov et al, 2003;Furumoto et al, 2006) or remote measurements, such as temperature profiles from a radio acoustic sounding system (RASS; Tsuda et al, 2001), observations from a microwave radiometer profiler (Stankov et al, 1996;Gossard et al, 1998;Bianco et al, 2005;Klaus et al, 2006), precipitable water vapor measurements from a global positioning system (GPS) receiver (Gossard et al, 1999;Imura et al, 2007) or a combination of these measurements. The method was considered to be promising for an operational implementation, providing the benefit of a finer time resolution, compared to the conventional radiosonde observations.…”

Section: Introductionmentioning

confidence: 99%

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High-resolution humidity profiles retrieved from wind profiler radar measurements

Saïd

Campistron²,

Girolamo

2018

Atmos. Meas. Tech.

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Abstract. The retrieval of humidity profiles from wind profiler radars has already been documented in the past 30 years and is known to be neither as straightforward and nor as robust as the retrieval of the wind velocity. The main constraint to retrieve the humidity profile is the necessity to combine measurements from the wind profiler and additional measurements (such as observations from radiosoundings at a coarser time resolution). Furthermore, the method relies on some assumptions and simplifications that restrict the scope of its application. The first objective of this paper is to identify the obstacles and limitations and solve them, or at least define the field of applicability. To improve the method, we propose using the radar capacity to detect transition levels, such as the top level of the boundary layer, marked by a maximum in the radar reflectivity. This forces the humidity profile from the free troposphere and from the boundary layer to coincide at this level, after an optimization of the calibration coefficients, and reduces the error. The resulting mean bias affecting the specific humidity profile never exceeds 0.25 g kg −1 . The second objective is to explore the capability of the algorithm to retrieve the humidity vertical profiles for an operational purpose by comparing the results with observations from a Raman lidar.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-resolution humidity profiles retrieved from wind profiler radar measurements

Saïd

Campistron²,

Girolamo

2018

Atmos. Meas. Tech.

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“…The 449 MHz WPR, with a narrower beam and higher SNR, is not as impacted by this issue, except at the lowest range gate, where ground clutter causes noisier spectra. The work by Frisch and Clifford (1974), Labbitt (1981), and Gossard et al (1998) led White et al (1999) to an expression for the relationship between the contribution to the spectral width due to turbulence and the dissipation rate, in a spectral form:…”

Section: ε From Radar Spectral Widthsmentioning

confidence: 99%

Improved observations of turbulence dissipation rates from wind profiling radars

2017

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Abstract. Observations of turbulence dissipation rates in the planetary boundary layer are crucial for validation of parameterizations in numerical weather prediction models. However, because dissipation rates are difficult to obtain, they are infrequently measured through the depth of the boundary layer. For this reason, demonstrating the ability of commonly used wind profiling radars (WPRs) to estimate this quantity would be greatly beneficial. During the XPIA field campaign at the Boulder Atmospheric Observatory, two WPRs operated in an optimized configuration, using high spectral resolution for increased accuracy of Doppler spectral width, specifically chosen to estimate turbulence from a vertically pointing beam. Multiple post-processing techniques, including different numbers of spectral averages and peak processing algorithms for calculating spectral moments, were evaluated to determine the most accurate procedures for estimating turbulence dissipation rates using the information contained in the Doppler spectral width, using sonic anemometers mounted on a 300 m tower for validation. The optimal settings were determined, producing a low bias, which was later corrected. Resulting estimations of turbulence dissipation rates correlated well (R 2 = 0.54 and 0.41) with the sonic anemometers, and profiles up to 2 km from the 449 MHz WPR and 1 km from the 915 MHz WPR were observed.

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“…A variety of instruments have been employed for this task, such as UHF wind profilers (e.g. Gossard et al, 1998;Jacoby-Koaly et al, 2002), SODARs (e.g. Kouznetsov et al, 2007), Doppler cloud radars (e.g.…”

Section: Introductionmentioning

confidence: 99%

Turbulent structure and scaling of the inertial subrange in a stratocumulus-topped boundary layer observed by a Doppler lidar

et al. 2015

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Abstract. The turbulent structure of a stratocumulus-topped marine boundary layer over a 2-day period is observed with a Doppler lidar at Mace Head in Ireland. Using profiles of vertical velocity statistics, the bulk of the mixing is identified as cloud driven. This is supported by the pertinent feature of negative vertical velocity skewness in the sub-cloud layer which extends, on occasion, almost to the surface. Both coupled and decoupled turbulence characteristics are observed. The length and timescales related to the cloud-driven mixing are investigated and shown to provide additional information about the structure and the source of the mixing inside the boundary layer. They are also shown to place constraints on the length of the sampling periods used to derive products, such as the turbulent dissipation rate, from lidar measurements. For this, the maximum wavelengths that belong to the inertial subrange are studied through spectral analysis of the vertical velocity. The maximum wavelength of the inertial subrange in the cloud-driven layer scales relatively well with the corresponding layer depth during pronounced decoupled structure identified from the vertical velocity skewness. However, on many occasions, combining the analysis of the inertial subrange and vertical velocity statistics suggests higher decoupling height than expected from the skewness profiles. Our results show that investigation of the length scales related to the inertial subrange significantly complements the analysis of the vertical velocity statistics and enables a more confident interpretation of complex boundary layer structures using measurements from a Doppler lidar.

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Measurement of Clear-Air Gradients and Turbulence Properties with Radar Wind Profilers

Cited by 59 publications

References 37 publications

High-resolution humidity profiles retrieved from wind profiler radar measurements

High-resolution humidity profiles retrieved from wind profiler radar measurements

Improved observations of turbulence dissipation rates from wind profiling radars

Turbulent structure and scaling of the inertial subrange in a stratocumulus-topped boundary layer observed by a Doppler lidar

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