Can Lidars compete with sonic anemometers? - Comparison of wind measurements from different Doppler Lidar scan strategies to sonic anemometer data

<p>The technological development of ground-based active remote sensing instruments has reached a point where they have the possibility to drastically increase the temporal and spatial data density compared to conventional instruments, which would allow for a better process understanding and is expected to enhance the forecasting skills of numerical weather prediction systems and reduce its uncertainties. To test the measurement uncertainty and feasibility of Doppler Lidar systems we participated in the FESST@MOL 2020 field campaign, organized by the German Meteorological Service (DWD) in Lindenberg, Germany. During this campaign, eight Doppler Lidars were operated at the boundary layer field site (GM) Falkenberg. We evaluated different scanning strategies for the determination of the wind profile in the Atmospheric Boundary Layer (ABL) using multiple different triple Lidar virtual tower (VT) scan patterns including range height indicator (RHI) and step/stare scan modes. We compared these Lidar-based wind measurements with the data from a sonic anemometer on a 99 m tall instrumented tower also located in Falkenberg over a period of four months. The lidar and the sonic anemometer data were processed to 10- and 30- minute averages and compared to each other. The VT measurements underestimated the mean horizontal wind compared to the sonic anemometer by around 0.2 m s<sup>‑1</sup>. Besides that, we compared the VT data with those from a single fourth nearby Doppler Lidar which was running in a velocity-azimuth display (VAD) mode. The calculated mean horizontal wind values between the two different modes showed a good comparability but differed stronger with increasing height.</p>

Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were carried out with scanning wind lidars and Ka-band radars. Specifically, step–stare measurements were collected simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars carried out simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual-tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the meteorological tower (met-tower) present at the BAO site and a lidar wind profiler. This survey shows that – despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements – a very good accuracy is achieved for both remote-sensing techniques for probing horizontal wind speed and wind direction with the virtual-tower scanning technique.

Influence of atmospheric stratification on the integral scale and fractal dimension of turbulent flows

In this work the relation between integral scale and fractal dimension and the type of stratification in fully developed turbulence is analyzed. The integral scale corresponds to that in which energy from larger scales is incoming into a turbulent regime. One of the aims of this study is the understanding of the relation between the integral scale and the bulk Richardson number, which is one of the most widely used indicators of stability close to the ground in atmospheric studies. This parameter will allow us to verify the influence of the degree of stratification over the integral scale of the turbulent flows in the atmospheric boundary layer (ABL). The influence of the diurnal and night cycles on the relationship between the fractal dimension and integral scale is also analyzed. The fractal dimension of wind components is a turbulent flow characteristic, as has been shown in previous works, where its relation to stability was highlighted. Fractal dimension and integral scale of the horizontal (u′) and vertical (w′) velocity fluctuations have been calculated using the mean wind direction as a framework. The scales are obtained using sonic anemometer data from three elevations 5.8, 13 and 32 m above the ground measured during the SABLES 98 field campaign (Cuxart et al., 2000). In order to estimate the integral scales, a method that combines the normalized autocorrelation function and the best Gaussian fit (R2 ≥ 0.70) has been developed. Finally, by comparing, at the same height, the scales of u′ and w′ velocity components, it is found that the turbulent flows are almost always anisotropic.

Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment

During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign, which was carried out at the Boulder Atmospheric Observatory (BAO) in spring 2015, multiple-Doppler scanning strategies were performed with scanning wind lidars and Ka-band radars. Specifically, step-stare measurements were performed simultaneously with three scanning Doppler lidars, while two scanning Ka-band radars performed simultaneous range height indicator (RHI) scans. The XPIA experiment provided the unique opportunity to compare directly virtual tower measurements performed simultaneously with Ka-band radars and Doppler wind lidars. Furthermore, multiple-Doppler measurements were assessed against sonic anemometer data acquired from the met-tower present at the BAO site and a lidar wind profiler. This survey shows that despite the different technologies, measurement volumes and sampling periods used for the lidar and radar measurements, a great accuracy is achieved for both remote sensing techniques for probing horizontal wind speed and wind direction with the virtual tower scanning technique.

Influence of Atmospheric Stratification on the Integral Scale and Fractal Dimension of Turbulent Flows

In this work the relation between integral scale and fractal dimension and the type of stratification in fully developed turbulence is analyzed. Integral scale corresponds to that in which energy from larger scales is incoming into turbulent regime. One of the aims of this study is the understanding of the relation between the integral scale and the Bulk Richardson number, which is one the most widely used indicators of stability close to the ground in atmospheric studies. This parameter will allow us to verify the influence of the degree of stratification over the integral scale of the turbulent flows in the Atmospheric Boundary Layer (ABL). The influence of the diurnal and night cycle in the relationship between the fractal dimension and integral scale is also analyzed. Fractal dimension of wind components is a turbulent flow characteristic as it has been shown in previous works, where its relation to stability was highlighted. Fractal dimension and integral scale of the horizontal (u') and vertical (w') velocity fluctuations have been calculated using the mean wind direction as framework. The scales are obtained using sonic anemometer data from three elevations 5.8 m, 13 m and 32 m above the ground measured during the SABLES-98 field campaign. In order to estimate the integral scales a method that combines the normalized autocorrelation function and the best gaussian fit (R2 ≥ 0.70) has been developed. Finally, by comparing, at the same height, the scales of u' and w' velocity components it is found that almost always the turbulent flows are anisotropic.

Proof of concept for turbulence measurements with the RPAS SUMO during the BLLAST campaign

The micro-RPAS SUMO (Small Unmanned Meteorological Observer) equipped with a five hole probe (5HP) system for turbulent flow measurements has been operated in 49 flight missions during the BLLAST (Boundary-Layer Late Afternoon and Sunset Turbulence) field campaign in 2011. Based on data sets from these flights we investigate the potential and limitations of airborne velocity variance and TKE (Turbulent Kinetic Energy) estimations by an RPAS system with a take-off weight below 1 kg. The integration of the turbulence probe in the SUMO system was still in an early prototype stage during this campaign. The main shortcomings were the use of two different, unsynchronized data loggers for the 5HP flow measurements and the aircraft’s attitude data required for the motion correction, and the different sampling rate for both data sets. Therefore, extensive post-processing of the data was required in order to calculate the turbulence parameters. In addition, the fine-tuning of the autopilot was not fully optimized, leading to oscillations in the vertical velocity that the motion correction routine was not able to remove. A simple block-filter has been used for the removal of these oscillations. For a filter constant of 0.61 s, the SUMO data show a good agreement to sonic anemometer data for the integral parameter of σω, but there is still a distinct difference in the underlying energy spectrum of the data sets. Resulting estimates of TKE profiles, obtained from consecutive flight legs at different altitudes, show reasonable results, both with respect to the overall TKE level, as well as the temporal variation. A thorough discussion of the methods used and the identified uncertainties and limitations of the system for turbulence measurements is included and should help the developers and users of other systems with similar problems.

Simple estimation of surface roughness parameters from single level sonic anemometer data

The estimation of joint values of both the roughness length z0 and the displacement height d is considered in the context of the MoninObukhov similarity law for the windspeed profile. When focused on single level data sets from one sonic anemometer (i.e. wind velocity, Reynolds stress and sensible heat flux data sets at one height), it is shown that this problem can be reduced to a simpler least squares procedure for one variable only. This procedure is carried out over a proper function of the data, representing the relative uncertainty of the roughness length, σz0/z0. This is minimized with respect to d, giving a direct estimate of d, z0, and their statistical uncertainty. The scheme is tested against a field-experiment data set.