Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans

The retrieval of turbulence parameters with profiling Doppler wind lidars (DWLs) is of high interest for boundary layer meteorology and its applications. DWLs provide wind measurements above the level of meteorological masts while being easier and less expensive to deploy. Velocity-azimuth display (VAD) scans can be used to retrieve the turbulence kinetic energy (TKE) dissipation rate through a fit of measured azimuth structure functions to a theoretical model. At the elevation angle of 35.3° it is also possible to derive TKE. Modifications to existing retrieval methods are introduced in this study to reduce errors due to advection and enable retrievals with a low number of scans. Data from two experiments are utilized for validation: first, measurements at the Meteorological Observatory Lindenberg–Richard-Aßmann Observatory (MOL-RAO) are used for the validation of the DWL retrieval with sonic anemometers on a meteorological mast. Second, distributed measurements of three DWLs during the CoMet campaign with two different elevation angles are analyzed. For the first time, the ground-based DWL VAD retrievals of TKE and its dissipation rate are compared to in situ measurements of a research aircraft (here: DLR Cessna Grand Caravan 208B), which allows for measurements of turbulence above the altitudes that are in range for sonic anemometers. From the validation against the sonic anemometers we confirm that lidar measurements can be significantly improved by the introduction of the volume-averaging effect into the retrieval. We introduce a correction for advection in the retrieval that only shows minor reductions in the TKE error for 35.3° VAD scans. A significant bias reduction can be achieved with this advection correction for the TKE dissipation rate retrieval from 75° VAD scans at the lowest measurement heights. Successive scans at 35.3 and 75° from the CoMet campaign are shown to provide TKE dissipation rates with a good correlation of R>0.8 if all corrections are applied. The validation against the research aircraft encourages more targeted validation experiments to better understand and quantify the underestimation of lidar measurements in low-turbulence regimes and altitudes above tower heights

Methodology for obtaining wind gusts using Doppler lidar

A new methodology is proposed for scaling Doppler lidar observations of wind gusts to make them comparable with those observed at a meteorological mast. Doppler lidars can then be used to measure wind gusts in regions and heights where traditional meteorological mast measurements are not available. This novel method also provides estimates for wind gusts at arbitrary gust durations, including those shorter than the temporal resolution of the Doppler lidar measurements. The input parameters for the scaling method are the measured wind-gust speed as well as the mean and standard deviation of the horizontal wind speed. The method was tested using WindCube V2 Doppler lidar measurements taken next to a 100 m high meteorological mast. It is shown that the method can provide realistic Doppler lidar estimates of the gust factor, i.e. the ratio of the wind-gust speed to the mean wind speed. The method reduced the bias in the Doppler lidar gust factors from 0.07 to 0.03 and can be improved further to reduce the bias by using a realistic estimate of turbulence. Wind gust measurements are often prone to outliers in the time series, because they represent the maximum of a (moving-averaged) horizontal wind speed. To assure the data quality in this study, we applied a filtering technique based on spike detection to remove possible outliers in the Doppler lidar data. We found that the spike detection-removal method clearly improved the wind-gust measurements, both with and without the scaling method. Spike detection also outperformed the traditional Doppler lidar quality assurance method based on carrier-to-noise ratio, by removing additional unrealistic outliers present in the time serie

Atmospheric Boundary Layer Classification With Doppler Lidar

We present a method using Doppler lidar data for identifying the main sources of turbulent mixing within the atmospheric boundary layer. The method identifies the presence of turbulence and then assigns a turbulent source by combining several lidar quantities: attenuated backscatter coefficient, vertical velocity skewness, dissipation rate of turbulent kinetic energy, and vector wind shear. Both buoyancy-driven and shear-driven situations are identified, and the method operates in both clear-sky and cloud-topped conditions, with some reservations in precipitation. To capture the full seasonal cycle, the classification method was applied to more than 1year of data from two sites, Hyytiala, Finland, and Julich, Germany. Analysis showed seasonal variation in the diurnal cycle at both sites; a clear diurnal cycle was observed in spring, summer, and autumn seasons, but due to their respective latitudes, a weaker cycle in winter at Julich, and almost non-existent at Hyytiala. Additionally, there are significant contributions from sources other than convective mixing, with cloud-driven mixing being observed even within the first 500m above ground. Also evident is the considerable amount of nocturnal mixing within the lowest 500m at both sites, especially during the winter. The presence of a low-level jet was often detected when sources of nocturnal mixing were diagnosed as wind shear. The classification scheme and the climatology extracted from the classification provide insight into the processes responsible for mixing within the atmospheric boundary layer, how variable in space and time these can be, and how they vary with location. Key PointsPeer reviewe

FESSTVaL Falkenberg Doppler lidar 30 minutes mean wind and turbulence profiles

This data set contains profiles of estimates for wind and turbulence variables derived from Doppler lidar measurements at the GM Falkenberg boundary layer field site during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) during the period May 18, 2021, and August 31, 2021 The GM Falkenberg as part of the Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory supersite is operated by the German national meteorological service (Deutscher Wetterdienst, DWD). The product variables are based on a measurement and retrieval approach outlined in Smalikho et. al (2017, DOI:10.5194/amt-2017-140). The measurement approach is based on a conically Doppler lidar (DL) scanning strategy with high spatio-temporal resolution (azimuth resolution of approx. ~1.3 deg; duration of one full scan ~ 72s) and a constant zenith angle of 54.7 deg. The realization of such a scanning strategy was possible via the continuous scan mode option of the DL system with 2000 accumulated pulses per beam. The retrieval approach outlined in Smalikho et. al (2017) allows for a simultaneous derivation of mean wind profiles and a consistent set of turbulence variables, namely the profiles of turbulence kinetic energy (TKE), turbulent energy dissipation rate (EDR), integral scale of turbulence (LV) and momentum fluxes (e.g. ). The TKE retrieval includes additional correction terms with the following purposes: (a) to compensate the typical underestimation of the DL derived TKE by unresolved small-scale wind fluctuations in the measured radial velocity due to the averaging over the DL pulse volume and (b) to reduce the retrieval error due to random errors in the derived radial velocity. Note that in Smalikho et. al (2017) the primary focus is on turbulence. The scanning strategy, however, is also useful to simultaneously retrieve the mean wind. Here, the FSWF (filtered.sine-wave-fit) approach as outlined in Smalikho et. al (2003, https://doi.org/10.1175/1520-0426(2003)0202.0.CO;2) has been used. Two subsets of data are provided: The Level-1 data set includes both the instantaneous DL measurements and related values (e.g. radial velocity and signal-to-noise ratio as function of time, range gate, azimuth) and relevant information on the system’s specific parameters which are either fixed by the manufacturer (e.g. wavelength, pulse repetition frequency, pulse length) or can be configured by the user (e.g. range gate length, number of pulse accumulation, focus). Level-2 data represent 30-min averages of the derived mean wind vector and turbulence variables, respectively. Furthermore, additional quality flags for the derived products are provided. All data are organized in daily files. The original measurements cover the lowermost 500m above ground level. However, depending on the signal quality and the results of the product’s quality assurance, the availability of reliable data can be limited to lower heights. Data Set Quality The success of the retrieval approach by Smalikho et. al (2017) strongly depends on the quality of the estimates for the Doppler velocity. During a routine application with a naturally varying density of backscattering targets in the atmosphere the number of pulse accumulations (Npa = 2000) was not always high enough for reliable Doppler velocity estimates (“good” estimates) and the occurrence of non-reliable “bad” estimates (outlier) was comparatively high from time to time. Such outlier contain no wind information (Stephan et al., 2018, doi: 10.1117/12.2504468) and if not excluded from the measured data set they may contribute to large errors in the retrieved meteorological variables (Dabas, 1999, https://doi.org/10.1175/1520-0426(1999)0162.0.CO;2). For that reason prior to product retrieval a careful pre- filtering of the Doppler velocity measurements was necessary to exclude such “bad” estimates from the Level-1 data set. The wind and turbulence variables stored in the Level-2 data set are the direct result of the retrieval approach. To distinguish between reliable and non-reliable turbulence products, additional quality flags (turb_flag_a, turb_flag_b, cov_flag, wind_flag) are provided in the Level-2 data set (where 0 = bad and 1 = good). These flags are the results of a number of different tests which proof whether the assumptions made for the retrieval were fulfilled or not. Further details concerning their meaning and how they should be applied are given by the corresponding variable name attributes in the NetCDF files. The retrieval algorithm has been validated through inter-comparison of the lidar-based wind and turbulence kinetic energy (TKE) values versus data from sonic measurements at 90 m height on the tower at GM Falkenberg. TKE products declared as reliable based on turb_flag_b (turb_flag_a) show a low systematic overestimation of 2.4% (0.7%) with a high variability of differences over the whole value range with possible overestimation of 41.1% (29%) and underestimation of -36.3% (-27.5%). Here, the availability of turb_flag_a proven TKE products was with about 37% much less than turb_flag_b proven TKE products with about 75% data availability. Variables: wind speed, wind_from_direction, turbulence kinetic energy, turbulent eddy dissipation rate, u and v component of wind vector, covariance uw and v

Regional-scale vertical fluxes from an optical-microwave scintillometer during FESSTVAL 2021

Abstract: This data set contains time series of the regional-scale sensible and latent heat fluxes derived from measurements with an optical-microwave scintillometer over a path length of 4.85 km between the Falkenberg boundary layer field site (GM Falkenberg) and the Lindenberg observatory site during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) from May 18 to August 31, 2021. The Lindenberg Meteorological Observatory – Richard-Aßmann-Observatory and the GM Falkenberg supersites are operated by the German national meteorological service (Deutscher Wetterdienst, DWD). Data are level-2 data as 10-minute averages. TableOfContents: Surface Upward Sensible Heat Flux; Surface Upward Sensible Heat Flux Qualiy Flag; Surface Upward Latent Heat Flux; Surface Upward Latent Heat Flux Quality Flag Technical Info: dimension: 144 x 1; temporalExtent_startDate: 2021-05-18 00:00:00; temporalExtent_endDate: 2021-08-31 23:59:59; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: meters; horizontalStart: 0; horizontalStartUnit: meters; horizontalEnd: 4800; horizontalEndUnit: meters; instrumentNames: BLS-900 optical large aperture scintillometer, MWSC-160 microwave scintillometer; instrumentType: Scintillometer; instrumentLocation: Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Scintec AG, Radiometer Physics GmbH Methods: The fluxes have been derived from simultaneous operation of a BLS-900 large-aperture optical scintillometer and a MWSC-160 microwave scintillometer. Data acquisition, data analysis and flux calculations were performed with the mwsc.exe software package. Structure parameters and the temperature-humidity correlation coefficient (rTq) for each 10min time interval have been calculated twice based on different settings, i.e. using the methods described in Hill (1997, https://doi.org/10.1175/1520-0426(1997)0142.0.CO;2) which assumes a constant rTq = -0.6 at night and rTq = 0.8 during daytime and in Lüdi et al. (2003, https://doi.org/10.1007/s10546-005-1751-1) which calculates rTq from the cross-correlation of the optical and microwave signals. The similarity model proposed by Koijmans and Hartogensis (2016, https://doi.org/10.1007/s10546-016-0152-y) was then used to derive the heat fluxes from the structure parameters. Using temperature and humidity profile measurements at the Falkenberg tower and measurements of the radiation budget, the deduced fluxes have been checked for sign consistency with the mean gradients of temperature and humidity and for a violation of the energy budget. In the end “most plausible” fluxes from the two methods (Hill, Lüdi et al. – see above) have been merged to a composite to ensure a better availability / quality of the fluxes especially around sunrise and sunset when the assumptions of the Hill approach typically fail. Quality flags have been assigned to each flux value, where G = good, D = dubious, B = bad, M = missing. Units: Units for all variables (see TableOfContents): W/m²;1;W/m²;1 geoLocations: BoundingBox: westBoundLongitude: 14.1199 degrees East; eastBoundLongitude: 14.1222 degrees East; southBoundLatidude: 52.1665 degrees North; northBoundLatitude: 52.2096 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Transmitters: 52.1665 °N, 14.1222 °E, 124 m above mean sea level, 51 m above ground Receivers: 52.2096 °N, 14.1199 °E, 129 m above mean sea level, 26 m above ground Size: Data (level 2 only) are packed into one packed tar-archive. Its size is roughly 400 Kbyte. Format: netCDF DataSources: Single site ground-based remote sensing, see "Technical Info" for instruments Contact: eileen.paeschke (at) dwd.de Web page: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-oms-l2-turb.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.htm

An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial

We present the results of a 1-year quasi-operational testing of the 1.5 μm StreamLine Doppler lidar developed by Halo Photonics from 2 October 2012 to 2 October 2013. The system was configured to continuously perform a velocity-azimuth display scan pattern using 24 azimuthal directions with a constant beam elevation angle of 75°. Radial wind estimates were selected using a rather conservative signal-to-noise ratio based threshold of −18.2 dB (0.015). A 30 min average profile of the wind vector was calculated based on the assumption of a horizontally homogeneous wind field through a Moore–Penrose pseudoinverse of the overdetermined linear system. A strategy for the quality control of the retrieved wind vector components is outlined for ensuring consistency between the Doppler lidar wind products and the inherent assumptions employed in the wind vector retrieval. Quality-controlled lidar measurements were compared with independent reference data from a collocated operational 482 MHz radar wind profiler running in a four-beam Doppler beam swinging mode and winds from operational radiosonde measurements. The intercomparison results reveal a particularly good agreement between the Doppler lidar and the radar wind profiler, with root mean square errors ranging between 0.5 and 0.7 m s⁻¹ for wind speed and between 5 and 10° for wind direction. The median of the half-hourly averaged wind speed for the intercomparison data set is 8.2 m s⁻¹, with a lower quartile of 5.4 m s⁻¹ and an upper quartile of 11.6 m s⁻¹