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

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

Ultrasonic anemometer and doppler lidar wind and gust data products during FESSTVAL 2021

Abstract: dlidcsm_level1: This data set contains the level-1 data of the Doppler LIDAR measurements at the three supersites (Falkenberg, Lindenberg, Birkholz) operated during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) during the period May 17 to August 31, 2021 (please also see the "Additional Notes" further down on this web page). dlidcsm_level2: This data set contains two wind products: (i) vertical profiles of the mean wind vector and (ii) vertical profiles of wind gusts derived with two different kinds of processing (Level2dwd and Level2uzk, see "Methods") from the Doppler LIDAR level 1 data dlidcsm_level1. sonic: This data set contains level 2 data of the mean wind vector and of the maximum gust wind speed derived from ultrasonic anemometer measurements at heights of 2.4 m, 50.3 m and 90.3 m at the Grenzschichtmessfeld (GM) Falkenberg during the Field Experiment on Sub-mesoscale Spatio-Temporal Variability in Lindenberg (FESSTVaL) over the period May 17 to 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). TableOfContents: dlidcsm_level1: sensor azimuth angle; attenuated backscatter coefficient; radial velocity of scatterers away from instrument (doppler velocity); error of doppler velocity; backscatter intensity; range bands; zenith angle dlidcsm_level2: Level2dwd: wind speed; wind direction; eastward wind component u; northward wind component v; upward air velocity w; wind speed of gust; wind direction of gust; eastward wind component of gust u_max; northward wind component of gust v_max; upward air velocity of gust w_max; condition number of gust; coefficient of determination of gust; number of radial velocities of gust; index of gust; autocorrelation function; condition number; coefficient of determination; number of radial velocities; wind quality flag; relative number of good circulations; relative number of good radial velocities Level2uzk: eastward wind component u; northward wind component v; upward air velocity w; eastward wind gust u_max; eastward weakest wind u_min; northward wind gust v_max; northward weakest wind v_min; upward air velocity of weakest wind w_min; wind speed; wind speed of gust; wind speed of weakest wind; wind direction; wind direction of gust; wind direction of weakest wind; covariance of wind; covariance of wind gust; covariance of weakest wind; standard deviation of wind speed; standard deviation of gust wind speed; standard deviation of weakest wind speed sonic: eastward wind component u; northward wind component v; upward air velocity w; quality flag for eastward wind component qc_u; quality flag for northward wind component qc_v; quality flag for upward air velocity qc_w; quality flag for wind speed and direction qc_wind; wind speed; wind direction; wind speed of gust; wind speed of weakest wind; gust factor Technical Info: (see also "Additional Notes" further down) dlidcsm_level1: dimension01: 259200 (nominal maximum number of timesteps per day) x 63 (Stream Line XR); dimension02: 259200 (nominal maximum number of timesteps per day) x 100 (Stream Line); temporalExtent_startDate: 2021-05-18 00:00:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 1/3; temporalResolutionUnit: seconds; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; rangeResolution01: 48 (Stream Line XR); rangeResolutionUnit01: meters; rangeResolution02: 30 (Stream Line); rangeResolutionUnit02: meters; verticalResolution01: 26.5; verticalResolutionUnit01: meters; verticalResolution02: 42.4; verticalResolutionUnit02: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd01: 2600; verticalEndUnit01: meters; verticalEnd02: 4200; verticalEndUnit02: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd. dlidcsm_level2: Level2dwd: dimension01: 144 timesteps x 100 (Stream Line); dimension02: 144 timesteps x 63 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. Level2uzk: dimension01: 144 timesteps x 101 (Stream Line); dimension02: 144 timesteps x 64 (Stream Line XR); temporalExtent_startDate: 2021-05-18 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: 42.4.; verticalResolutionUnit: meters; verticalStart: 90; verticalStartUnit: meters; verticalEnd: 4250; verticalEndUnit: meters; instrumentNames: Stream Line S/N 78, Stream Line S/N 172, Stream Line S/N 178; Stream Line XR S/N 44, Stream Line XR S/N 161; instrumentType: Doppler LIDAR; instrumentLocation: Birkholz, Grenzschichtmessfeld Falkenberg, Lindenberg; instrumentProvider: Halo Photonics Ltd.. sonic: dimension: 144 timesteps per day x 3 heights; temporalExtent_startDate: 2021-05-17 00:10:00; temporalExtent_endDate: 2021-09-01 00:00:00; temporalResolution: 10; temporalResolutionUnit: minutes; spatialResolution: none; spatialResolutionUnit: none; horizontalResolutionXdirection: none; horizontalResolutionXdirectionUnit: none; horizontalResolutionYdirection: none; horizontalResolutionYdirectionUnit: none; verticalResolution: none; verticalResolutionUnit: none; verticalStart: 2.4; verticalStartUnit: meters; verticalEnd: 90.3; verticalEndUnit: meters; instrumentName: usa1_standard_1; instrumentType: Ultrasonic anemometer; instrumentLocation: Grenzschichtmessfeld Falkenberg at 2.4, 50.3 and 90.3 meters above ground; instrumentProvider: Metek GmbH. Methods: dlidcsm_level1: Doppler LIDAR profiles extend throughout the lower atmospheric boundary layer from 90 m up to a maximum height typically above 1500 m dependent on the atmospheric backscatter conditions. The Doppler LIDAR measurements were based on a conically Doppler lidar scanning geometry with high temporal resolution (~3.4s for one full scan, azimuth resolution of approx. ~33 deg) and a constant zenith angle of 28 deg. The realization of such a scanning strategy was possible via the continuous scan mode option of the Doppler LIDAR system with a number of accumulated pulses per beam Npa = 3000. Two different types of Halo Photonics DL systems were used at the three sites during the campaign: (i) a Halo Photonics Streamline XR with a range gate length of 48 m and Halo Photonics Streamline with a range gate length of 30 m. For the non-XR systems the focus was set to 500 m, for XR systems it is set to infinity per default. For more details concerning the scan configuration see also Steinheuer et al. (2022). These level 1 data are provided by DWD using the dl_toolbox (https://github.com/mkay-atm/dl_toolbox) and include both the instantaneous Doppler LIDAR measurements and related values (e.g. radial velocity and signal-to-noise ratio as function of range gate, time, and azimuth direction) and relevant information concerning 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). Due to the short sampling time per ray, regular time synchronization vs. a reference at prescribed intervals occasionally resulted in a jump back of the time stamp assigned to each vector of radial velocity data. We did not correct that since we wanted to keep the original level-1 data as they were provided from the instrument. Note that the physical range resolution depends on the pulse length (see, e.g., Frehlich, R., 1997, https://doi.org/10.1175/1520-0426(1997)0142.0.CO;2) which is set at a fixed value by the manufacturer, this value is different for each system. To harmonize the output, we configured the Streamline and Streamline XR systems each in the same way. This may imply that the range and height bounds given in the level 1 and level 2 data, respectively, may show a positive or negative overlap between neighbouring range / height gates. dlidcsm_level2: Level-2 data represent 10-min averages of the derived mean wind vector and of wind gust speeds. Usually, gusts are defined as a 3s moving average (WMO). We try to match this by calculating first for each scan (sampling time 3.4 s) a wind estimate and then we search for the maximum value (= gust) within a pre-defined 10min interval. Reliability of both the derived mean wind and the gust wind speeds has been assessed for both methods (see below) by comparison with the sonic wind and gust product data at a reference level of 90 m for a several-months data set. RMSD values are in the order of 0.3 m/s for the mean wind speed, and 0.7 m/s for the maximum gust speed, respectively. The Level2dwd product contains results based on the DWD processing (publication is in preparation and will be added to this description, for first information see Detring, C. et al., 2022). The quality control procedures implemented for the level2dwd product include an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), various statistical tests to remove outliers (acf, r2), and completeness tests concerning the availability of both single beam data per scan and single-scan wind values per 10-minute interval (n_good_data, n_good_circulation). Each lidar-based value is accompanied by a quality flag (qwind) where 0 = bad, and 1 = good. The Level2uzk product contains results based on the Uni Cologne processing (see Steinheuer et al., 2022) and https://github.com/JSteinheuer/DWL_retrieval). Here, the gust is provided if at least 50% of the individual scans within the 10 minutes have been processed. The quality control procedures implemented for the level2uzk product omits an assessment of the signal-to-noise ratio of the backscattered lidar signal (snr), but are instead based on statistical coherence. This involves fitting a wind vector to the radial observations and iteratively eliminating outliers that are inconsistent with the fit. The iteration stops when the fit has small uncertainties (wind is returned) or too many rejected observations (no wind is returned). The amount of included observations and the quality of the fit is combined to a covariance matrix for the wind vector describing the uncertainty of each estimate. Please check Steinheuer et al. (2022) for more details. sonic: The sonic measurements are recorded at 20Hz and are quality checked. After quality control, both the 10min mean wind and the maximum gust speed for each interval are calculated. Here, the maximum of the 3s moving average is chosen as the maximum gust speed. This goes with the definition of wind gusts according to the WMO standard. Quality control of the sonic raw data follows Vickers and Mahrt (1997). It includes tests for non-physical values, constant values, and spikes. Constant values are eliminated, and spikes are detected and replaced by linearly interpolated values. Note that this spike-detection does not affect the identification of wind gusts, since it only removes significant outliers of at maximum three consecutive data values (corresponding to a duration of less than 0.2 s which is more than one magnitude shorter than the duration of a gust). Each sonic value is accompanied by a quality flag (qc_wind) where 0 = bad, and 1 = good. Units: Units for all variables (see TableOfContents): dlidcsm_level1: degrees; 1/ (m sr), m/s; m/s; 1; m; degrees dlidcsm_level2: Level2dwd: m/s; degrees; m/s; m/s; m/s; m/s; degrees; m/s; m/s; m/s; 1; 1; 1; 1; 1; 1; 1; 1; 1; percent; percent Level2uzk: m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; m/s; degrees; degrees; degrees; m²/s²; m²/s²; m²/s²; m/s; m/s; m/s sonic: m/s; m/s; m/s; 1; 1; 1; 1; m/s; degrees; m/s; m/s; 1 geoLocations: BoundingBox: westBoundLongitude: 14.122 degrees East; eastBoundLongitude: 14.192 degrees East; southBoundLatidude: 52.167 degrees North; northBoundLatitude: 52.209 degrees North; geoLocationPlace: Germany, UTM zone 33U Locations: Birkholz: 52.200 degrees North, 14.192 degrees East, 70 meters above mean sea level Falkenberg: 52.167 degrees North, 14.123 degrees East, 73 meters above mean sea level Lindenberg: 52.209 degrees North, 14.122 degrees East, 115 meters above mean sea level Size: All data are organized in daily files. For the ease of downloading all sonic, all Doppler LIDAR level-2, and Doppler LIDAR level-1 data of the three supersites are packed into one tar archive each; the total number of tar archives is hence 1 + 1 + 3. Files sizes of these archives are: sonic: ~3.6 MByte, Doppler LIDAR level-2: ~0.5 GByte, Doppler LIDAR level-1 Birkholz: ~26.1 GByte, Doppler LIDAR level-1 Falkenberg: ~34.4 GByte, Doppler LIDAR level-1 Lindenberg: ~13.1 GByte; the total amount is about 74 GByte. Format: netCDF DataSources: dlidcsm: Single site ground-based remote sensing, see "Technical Info" for instruments sonic: Single site tower-based in situ observations, see "Technical Info" for instruments Contact: general: carola.detring (at) dwd.de sonic: carola.detring (at) dwd.de Doppler LIDAR DWD processing: frank.beyrich (at) dwd.de Doppler LIDAR UzK processing: julian.steinheuer (at) uni-koeln.de Web page: LIDAR: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/fval-dlidcsm-wind-and-gust.html and SONIC: https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-st-datasets/samd-st-fesstval/sups-rao-turb-l2-wind-and-gust.html see also: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.htm

FESSTVaL: the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg

Numerical weather prediction models operate on grid spacings of a few kilometers, where deep convection begins to become resolvable. Around this scale, the emergence of coherent structures in the planetary boundary layer, often hypothesized to be caused by cold pools, forces the transition from shallow to deep convection. Yet, the kilometer-scale range is typically not resolved by standard surface operational measurement networks. The measurement campaign FESSTVaL aimed at addressing this gap by observing atmospheric variability at the hectometer to kilometer scale, with a particular emphasis on cold pools, wind gusts and coherent patterns in the planetary boundary layer during summer. A unique feature was the distribution of 150 self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper air observations were provided by 8 Doppler lidars and 4 microwave radiometers distributed at 3 supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters and a small radiometer network carried out additional measurements during a four-week period. In this paper, we present FESSTVaL’s measurement strategy and show first observational results including unprecedented highly-resolved spatio-temporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems