TEAMx-PC22 (TEAMx pre-campaing 2022) - ACINN Distributed temperature sensing, fluxes from eddy covariance measurements, and auxiliary measurements from and at the i-Box station (VF-0) Kolsass (Q13102)

Introduction During the TEAMx-precampaign (TEAMx-PC22) in summer 2022, the Innsbruck Box (i-Box) station at the valley floor in Kolsass (CS-VF0) was extended by a vertical array with fiber-optic distributed temperature sensing (DTS). The i-Box is a testbed for studying boundary layer processes in highly complex terrain (Rotach et al. (2017) and i-Box WIKI for further information). The mountain boundary layer is investigated using a 17 m high tower with multi-level observations of turbulence, wind speed, and temperature. DTS measurements with a spatio-temporal resolution of 0.127 m and 1 s were added to these profile measurements. The combination of DTS measurements and point observations has the capability of resolving sub-meso scale motions (Pfister et al. 2021) and can reveal processes within the boundary layer during the morning and evening transition (Fritz et al. 2021). The aim of TEAMx-PC22 was to test new instruments, new instrument configurations and new measurement sites to support the planning of the main TEAMx observational campaign (TOC) in 2024/2025. More details about TEAMx can be found at http://www.teamx-programme.org as well as in Serafin et al. (2020) and in Rotach et al. (2022). DATA SET DESCRIPTION 1. Location The i-Box valley-floor site is located on the almost flat floor near the town of Kolsass within the Inn Valley roughly 20 km east-north-east of Innsbruck. The site is characterized by different types of agricultural land. The 17-m high tower is a full energy-balance station and is instrumented with three vertical levels of turbulence measurements. The exact location is 47.305341N, 11.62219E (UTM: 698215.03 E, 5242420.95 N) at 545 m above mean sea level. 2. Temporal coverage The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. The i-Box station is running continuously, however, the provided data only covers the period when DTS data is available. The DTS array was running during the following periods: 08.06.-14.06.2022 28.06.-18.07.2022 3. Instrument details Distributed temperature sensing For spatially continuous measurements of vertical temperature profiles at this tower, a DTS array was installed. Temperatures were measured with two channels at 1~Hz with a spatial resolution of 0.127~m. The used DTS instrument was an Ultima-HS (Silixa Ltd., Hertfordshire, UK) which was combined with a fibre-optic cable (900 m outer diameter; AFL Telecommunications, Spartanburg, SC, USA) consisting of a bend-optimised optical fiber (125 m with 50 m core), buffered with Kevlar in a white plastic jacket. The fiber-optic cable was installed vertically towards the west of the tower such that two temperature profiles could be measured simultaneously. For the full array the approximately 450 m long fiber-optic cable was running from one DTS channel through a warm and cold reference bath towards the tower, then up and down the 17-m tower, and back through the baths towards the second channel. Accordingly, the array could be measured in both directions. For mounting at the top and bottom of the tower PVC pipes (diameter 15 cm) were used. The setup with two channels allows for sampling the array in both directions. Before entering the reference baths roughly 200 m were left on the spool slightly affecting signal-to-noise ratio. The vertical array was mapped by cooling packs. Both reference baths observed at the beginning and end of each fiber-optic cable yielded to four reference sections at two temperatures. The array was a double-ended configuration observed as two single-ended configurations which is different from the manufacturer's provided double-ended mode (des Tombe et al. 2020, Lapo et al. 2022). Reference temperature probes were PT100 from the Ultmia-HS itself. DTS data was calibrated using the weighted-least squares approach described indes Tombe et al. (2020) and implemented in the dtscalibration software package (des Tombe et al. 2022) and all processing was completed using the pyfocs software package (Lapo and Freundorfer 2020) . As the fiber-optic cable runs through both calibration baths before and after the array creating four locations within a temperature controlled environment. Of those locations three are used for calibration (every time step) and the fourth is used for validation. A schematic of the setup is given within the files. After calibration a mean bias of -0.05 K and root mean squared difference of 0.22 K was determined with the validation water bath. Unfortunately the mounting towards the west created an artifact as the tower was partially shading the fiber-optic cable creating unphysical temperature gradients. Accordingly, data from 04.00 - 09.00 UTC should not be used for data analysis. The given data isonly a single fiber of the paired vertical sections on the 17-m tower. Artifacts from the plastic ring holders are removed from the fiber. The DTS experiment was named the Innsbruck DTS Experiment (InnDEX22), hence, data names were chosen accordingly. But keep in mind that InnDEX22 was part of TEAMx-PC22. i-Box tower Full site description and all data exceeding the DTS observations can be found on https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html. Utilized and uploaded data are mainly within three categories: eddy covariance (EC) fluxes at level 1 (4 m agl) and at level 2 (8.7 m agl) and low-frequency data: EC flux level 1:Combination of ultrasonic anemometer CSAT3 (orientation from North: 30) and infrared gas analyzer EC150 from Campbell Scientific EC flux level 2:Ultrasonic anemometer CSAT3 (orientation from North: 30) from Campbell Scientific low-frequency data: pressure: Setra 278 (Setra Systems, Inc., Boxborough, Maine, USA) at 1.4 m agl radiation: ventilated CGR4 pyrgeometers and CMP21 pyranometers (Kipp Zonen, Delft, Netherlands) at 2 m agl temperature profile:Rotronic HC2-S3 actively ventilated at 2, 4, 8.7, and 16.9m wind profile: 2D ultrasonic anemometer Gill Windsonic4 at 2, 4, 6, and 12m For the EC processing further quality criteria can be applied to assure good data quality. More information on processing of data and quality criteria is given here: EC fluxes processing:Averaging interval of 30 min performed by the software EdiReProcessing includes despiking; double-rotation of the wind components; detrending with a recursive filter and a time constant of 200 s; and applying frequency-response corrections, heat-flux corrections for humidity effects, oxygen corrections for KH20, and WPL corrections. The datafile contains several quality flags and added as description within the netcdf files; zero-plane displacement height of 0 m EC flux Quality Criteria (QC) flags: -1: all data 0: excluding instrument malfunction 1: additionally skewness within range (-2 to 2) and kurtosis 8 following Vickers and Mahrt 1997 2: additionally exclude non-stationary data EC flux Flags: 0: data ok 1: data not ok (see description of individual flag for further details) 4. Data file structure Zip folders Different data sets were generated, as measurements had different temporal resolutions or different sets of parameter. Accordingly the following data is given: DTS data (1 s): InnDEX22_distributed_temperature_sensing.zip EC flux level 1 (30 min): InnDEX22_EC_flux_lvl1.zip EC flux level 2 (30 min): InnDEX22_EC_flux_lvl1.zip Low-frequency data (1 min): InnDEX22_low_frequency_data.zip File format Each above mentioned folder is filled with netcdf files. One for each day. Global information contains location, instrument type etc. Parameter description is given as attributes for each parameter. 6. Contact Contact lena.pfister(at)uibk.ac.at for any questions regarding the data set. Acknowledgements Special thanks to the "Institut fr Meteorologie und Klimaforschung Atmosphrische Umweltforschung" (IMK-IFU), KIT-Campus Alpin, Garmisch-Partenkirchen, for lending us the DTS measurement device for TEAMx-PC22. 7. References Fritz, A. M., Lapo, K., Freundorfer, A., Linhardt, T., Thomas, C. K. (2021): Revealing the morning transition in the mountain boundary layer using fiber-optic distributed temperature sensing. Geophysical Research Letters, 48, e2020GL092238. https://doi.org/10.1029/2020GL092238 des Tombe, B., Schilperoort, B., Bakker, M. (2020): Estimation of Temperature and Associated Uncertainty from Fiber-Optic Raman-Spectrum Distributed Temperature Sensing. Sensors, 20, 2235. https://doi.org/10.3390/s20082235 des Tombe, Bas Franois, Schilperoort, Bart. (2022): Dtscalibration Python package for calibrating distributed temperature sensing measurements (v1.1.2). Zenodo. https://doi.org/10.5281/zenodo.7111585 Pfister, L., Lapo, K., Mahrt, L., Thomas, C.K. (2021): Thermal Submesoscale Motions in the Nocturnal Stable Boundary Layer. Part 1: Detection and Mean Statistics. Boundary-Layer Meteorol 180, 187202. https://doi.org/10.1007/s10546-021-00618-0 Lapo, K., Freundorfer, A., (2020): klapo/pyfocs v0.5: Fully-functional python package intended for atmospheric deployments of distributed temperature sensing. Zenodo, https://doi.org/10.5281/zenodo.7111585 Lapo, K., Freundorfer, A., Fritz, A., Schneider, J., Olesch, J., Babel, W., and Thomas, C. K. (2022): The Large eddy Observatory, Voitsumra Experiment 2019 (LOVE19) with high-resolution, spatially distributed observations of air temperature, wind speed, and wind direction from fiber-optic distributed sensing, towers, and ground-based remote sensing, Earth Syst. Sci. Data, 14, 885906 https://doi.org/10.5194/essd-14-885-2022 Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubiić, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Raudzens Bailey, J. Schmidli, G. Wohlfahrt, B. Zardi, (2020): Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment. Innsbruck University Press. https://doi.org/10.15203/99106-003-1 Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubiic, N. Kalthoff, T. Karl, D. J. Kirshbaum, M. Lehner, S. Mobbs, A. Paci, E. Palazzi, A. Bailey, J. Schmidli, C. Wittmann, G. Wohlfahrt, D. Zardi, (2022): A collaborative effort to better understand, measure, and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society, 103, E1282E1295. https://doi.org/10.1175/bams-d-21-0232.1