DOI: 10.25592/uhhfdm.10558
title: FESSTVaL Falkenberg Doppler lidar 30 minutes mean wind and turbulence profiles

description: 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. &lt; u‘w ‘&gt; ). 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)020&lt;0276:TOWVEF&gt;2.0.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 (N<sub>pa</sub> = 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)016&lt;0019:SMFTRO&gt;2.0.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 vw
description: Project: FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg), a measurement campaign initiated by the Hans-Ertel-Center for Weather Research.