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497 results for “campaign”
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Calibrated Red/Near Infrared orthomosaic imagery from UAV campaign at Niwot Ridge, 2017.
Red/Near Infrared data were collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. The purpose of the project was to investigate snow depth variability and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
Twitter analysis of the five main political leaders during the 2019 UK electoral campaign: from 12 October to 16 December 2019
<p>The analysis was conducted from 12 October to 16 December 2019 on Twitter through the study of the five main political leaders— Boris Johnson, Jeremy Corbyn, Jo Swinson, Nicola Sturgeon, and Nigel Farage — during the 2019 UK electoral campaign.</p>
Awareness campaign video for local communities: Understanding and Preventing Rabies in the Menabe Region, Madagascar
<p>Cette courte vidéo est un outil utilisé pour la sensibilisation contre la rage. </p><p>Elle a été produite dans le cadre d'une recherche opération dans le sud ouest de Madagascar dans la région Menabe.</p><p>Elle présente en langue Malagasy, sous-titrée en Français:</p><p>- Ce qu'est la rage: la rage est une maladie due par une morsure d'un animal enragé ou le léchage de la plaie d'une personne par un animal enragé. Le chien est responsable de la rage dans près de 9 cas sur 10.</p><p>- actions à entreprendre à si une personne a été mordue par un chien</p><p>- les gestes à effectuer si une personne a été mordue par un chien</p><p>- elle insiste sur le fait que chez l'homme, la rage est traitable à 100% avant l'apparition du premier symptôme.</p><p>La vidéo est aussi disponible sur Youtube : </p><p><i>https://www.youtube.com/watch?v=RBK3Uywxszo&ab_channel=DaoudaKassi%C3%A9</i></p>
Resistance, utilization factor curve and polarization curve of test campaign
<p>The datesets refer to the article "Experimental Procedures & First Results of an Innovative Solid Oxide Fuel Cell Test Rig: Parametric Analysis and Stability Test" (https://zenodo.org/badge/DOI/10.5281/zenodo.5763507.svg).</p> <p>Solid Oxide Fuel Cells are a promising technology for Solid Oxide Fuel Cells (SOFC) are a promising technology For high-efficiency electrochemical conversion of a vast range of fuel gas mixtures, thigh operating temperature conditions (650–900 ◦C) represent a challenge both at system level and at laboratory testing level, in terms of material properties and performance dynamics. In this work a detailed procedural analysis is presented for an innovative all-ceramic compact SOFC test rig and first experimental testing results are reported in terms of polarization curves obtained under parametric variation of operating conditions (H2 content, air ratio λ and temperature) and short-term voltage stability test under load (140 h at 0.3 A/cm2 ). The electrochemical characterization results confirm the validity of the used all-ceramic cell holder, showing excellent cell performances in terms of polarization. H2 content has the most impact on SOFC performance, followed by temperature and finally air ratio, whose impact in the analyzed range is hardly seen. From the short-term stability test, the test bench setup reliability is demonstrated, showing no significant performance degradation after 140 continuous hours under load, which confirms the high quality and reproducibility of the results.</p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>This particular data set was used in a publication by Studinger <em>et al.</em>, 2022 (<a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>) that developed methods for estimating water depths of supraglacial lakes on the Greenland Ice Sheet.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM waveform data from the T6 and T7 lidars, as well as the true ranges and a MATLAB® function to read ground test waveform data.</p> <p><strong><strong>See also:</strong> </strong></p> <p>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Time seqUential theRmal inFrared - Turbulence campaign 1 - TURF-T1 Experiment data
<p>This dataset is a thermal infrared dataset collected alongside with sonic anemometer and thermocouple data. The experiment took place on the 12/01/2019 starting at 16:21:11 NZDT and ending at 16:31:11 NZDT. The collection of the infrared data was done via uncrewed aerial vehicle with an Optris PI 450 camera flying above a TURF Sportsground located at N -43.29186830137823, E 172.6008992376682.</p>
UrbanOccupationsOETR_1532_Ottoman_campaign_1840s_settlements_dataset
<p>This dataset contains the data used for the below-mentioned publication of our transport network and least-cost path model. It analyzes 16th- and 19th-century transport networks between Istanbul and Sofia. The historical settlement data is based on work by the UrbanOccupationsOETR (https://urbanoccupations.ku.edu.tr/) and <a href="https://popgeo.ku.edu.tr/">POPGEO_BG</a> projects.</p> <p>If you would like to use the dataset for further publication, please use the credentials specified below:</p> <p>Kabadayi, M Erdem, Piet Gerrits, and Grigor Boykov. “Geospatial Mapping of a 16th Century Transport Corridor for Southeast Europe.” <em>Digital Scholarship in the Humanities</em> 37, no. 3 (2022): 788–812. <a href="https://doi.org/10.1093/llc/fqab084">https://doi.org/10.1093/llc/fqab084</a>.</p> <p> </p>
A lost campaign
<p>"A lost campaign? New evidence of Roman temporary camps in northern Arabia". Remote sensing survey in southern Jordan has identified at least three Roman temporary camps that indicate a probable undocumented military campaign into what is today Saudi Arabia, and which we conjecture is linked to the Roman annexation of the Nabataean kingdom in AD 106.</p>
urbisphere-Berlin campaign BAMS data repository
<p>This data set accompanies Fenner et al. (2024) and contains the data (and references to data sources) of the plots and tables therein.</p> <p>Data are organized by Figure and Table in the article, each located in a separate (zip-)folder.</p> <p>See README.pdf for additional information and data descriptions.</p> <p>Detailed data processing details are given in the Appendices of the article.</p> <p>RAW measurement data are accessible via the <a title="Zenodo &ldquo;urbisphere&rdquo; community" href="../communities/urbisphere/" target="_blank" rel="noopener">Zenodo “urbisphere” community</a>.</p> <p> </p> <p>Fenner, D., Christen, A., Grimmond, S., Meier, F., Morrison, W., Zeeman, M., Barlow, J., Birkmann, J., Blunn, L., Chrysoulakis, N., Clements, M., Glazer, R., Hertwig, D., Kotthaus, S., König, K., Looschelders, D., Mitraka, Z., Poursanidis, D., Tsirantonakis, D., Bechtel, B., Benjamin, K., Beyrich, F., Briegel, F., Feigel, G., Gertsen, C., Iqbal, N., Kittner, J., Lean, H., Liu, Y., Luo, Z., McGrory, M., Metzger, S., Paskin, M., Ravan, M., Ruhtz, T., Saunders, B., Scherer, D., Smith, S. T., Stretton, M., Trachte, K. and Van Hove, M., 2024: urbisphere-Berlin campaign: Investigating multi-scale urban impacts on the atmospheric boundary layer. <em>Bull. Am. Meteorol. Soc. </em>DOI: <a href="https://doi.org/10.1175/BAMS-D-23-0030.1">10.1175/BAMS-D-23-0030.1</a><em><br></em></p> <p> </p>
Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign
<p>This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.</p>
Doppler lidar datasets of UDINE measurement campaign at TROPOS Leipzig, Germany
<p>The Doppler lidar dataset of the Up- and Downdraft in Drop and Ice Nucleation Experiment (UDINE, 2010-2013) is published. This dataset is part of the publication Bühl et al., "Impact of vertical air motions on ice formation rate in mixed-phase cloud layers", NPJ Climate and Atmospheric Science, 2019.</p> <p>Time-height resolved measurements of mean vertical Doppler velocity of aerosol and cloud particles over the measurement site. Most data is recorded in vertical stare with 2s measurement time. Files are in NetCDF-format and contain the following variables:</p> <p>amp(time,height): The signal strength (SNR) recorded by the data acquisition.<br> data(time,height): The first moment of the main peak in the Doppler spectrum<br> heightresolution: The resolution of the data acquisition in nanoseconds<br> measurement_time(time,datetime): Time of recording in human readable format [YYYYMMDD,hhmmss]<br> scanposition(time,scanposition): Two element array with position of the scanner in [azimuth,off-zenith-angle]<br> shots(time): The number of shots used for averaging. Spectra are averaged at a rate of 750 shots/s so this variables indicates if the system has functioned nominally.</p>
Datasets Cured and Enriched with Provenance from the National Vaccination Campaign Against COVID-19
<p>The COVID-19 pandemic is a global threat. If, on the one hand, weaccount for many losses, on the other hand, the generation of datasets and ur-gent analytical demands has accelerated. Among the combat strategies, vacci-nation and data-centered epidemiological investigations stand out. This datasetpaper presents the process of building cured and annotated datasets with prove-nance metadata. The main dataset is based on the registration data of the Vacci-nation Campaign against COVID-19 in Brazil. The dataset contains thousandsof records processed up to March 2021. The data were analyzed, investigated,treated and cross-checked with other sources, in order to correct and comple-ment them, resulting in cured datasets and aligned to the FAIR principles.</p>
LiftWEC deliverable D4.3: Dataset from 2D experimental test campaign
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. "LiftWEC Deliverable D4.3 Report on 2D experimental testing dataset" describes this dataset and for a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Seaglider (SG537) dataset collected during the ROAM-MIZ field campaign in the Southern Ocean
<p>This dataset is a part of the Robotic Observations And Modelling in the Marginal Ice Zone (ROAM-MIZ, <a href="http://www.roammiz.com">www.roammiz.com</a>) project, and contains the temperature and salinity profiles from a Seaglider (SG537), which was deployed at the Prime Meridian in the northeastern Weddell Sea (0.00W and 55S) from 18th October 2019 to 18th February 2020 and obtained a total of five repeated crossings of the Southern Boundary.</p>
Doppler spectra collected by a transect of three MRR-PRO during the POPE 2020 campaign at Princess Elisabeth Antarctica
<p>This repository contain the datasets of Doppler spectra collected by three K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the Sør Rondane Mountains, in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA).</p> <p>The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the École Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF).</p> <p>The datasets are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> .</p> <p>This repository complements "Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica", uploaded on Zenodo at: <a href="https://doi.org/10.5281/zenodo.7428690">https://doi.org/10.5281/zenodo.7428690</a> . The radar variables in the MRR-PRO data files contained in the latter have been computed from the raw spectra stored in the current repository.</p> <p> </p> <p><strong>Content of the archives</strong></p> <p>- <em>MRR_PRO_23_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 23, deployed at the lowest altitude in the transect, at1543 m above mean sea level (a.m.s.l.), at the following coordinates: latitude 72° 6’ 50.4” S, longitude 23° 30’ 50.4” E.</p> <p>- <em>MRR_PRO_06_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 06, deployed at approximately 2000 m a.m.s.l. of altitude, at the following coordinates: latitude 72° 7’ 4.8” S, longitude 23° 20’ 49.2” E.</p> <p>- <em>MRR_PRO_22_raw_spectra.zip,</em><br> This archive contains the Doppler spectra collected by the MRR-PRO 22, deployed at the highest location in the transect (2360 m a.m.s.l.), at the following coordinates: latitude 72° 13’ 37.2” S, longitude 23° 11’ 27.6” E.</p> <p> </p> <p><strong>Content of the NetCDF4 files</strong></p> <p>A “short name” is associated to each variables in the NetCDF4 files stored in the three archives.</p> <p>The relevant variables in each data file are:<br> – the raw spectral power, identified in the files by the short name “spectrum_raw”;<br> – the transfer function, used to convert the raw spectral power to spectral reflectivity, as described in Ferrone et al. (2022), and identified in the files by the short name “transfer_function”.</p> <p> </p> <p><strong>References</strong></p> <p>Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022</p> <p>Ferrone, A., and Berne, A., Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7428690, 2023</p>
Radar and ground-level measurements collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
<p>This repository contain the datasets of radar and ground-level measurements collected in the vicinity of the Belgian research base Princess Elisabeth Antarctica (PEA).</p> <p>The measurement campaign has been conducted by the Environmental Remote Sensing Laboratory (LTE) of the Scole Polytechnique Fédérale de Lausanne (EPFL), with the logistical support of the International Polar Foundation (IPF).</p> <p>The datasets and their processing are described in the article “Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica”, by Alfonso Ferrone and Alexis Berne. The article was submitted to Earth System Science Data in August 2022, and is available at the following URL: <a href="https://doi.org/10.5194/essd-2022-295">https://doi.org/10.5194/essd-2022-295</a> .</p> <p><br> <br> <strong>Content </strong><strong>of the archives</strong><br> The datasets have been divided into compressed archives. Each of them contains a series of data files, all saved in NetCDF4 format.</p> <p>The content of each archives is listed below.</p> <p>- <em>WProf.zip</em><br> This archive contains the radar variables collected by the W-band Doppler profiling cloud radar (WProf) deployed at PEA.<br> The liquid water path and integrated water vapor (retrieved thanks to the 89 GHz radiometer included in the instrument) has also been included in the files.</p> <p>- <em>MXPol_PPI.zip</em><br> This archive contains the polarimetric radar variables collected by the X-band Doppler dual-polarization scanning weather radar (MXPol) during the nearly-vertical PPI scans.</p> <p>- <em>MXPol_sector_scans_2019.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the sector scans (PPI scans limited to a sector of the full azimuth circle) scans performed in December 2019. Sector scans collected in the following months have been stored separately, due to a limitation on the maximum number of files in input to the creation of the zip archive.</p> <p>- <em>MXPol_sector_scans_2020.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the sector scans scans performed in January and February 2020.</p> <p>- <em>MXPol_hydrometeor_types.zip</em><br> This archive contains the polarimetric radar variables collected by MXPol during the RHI scans. The files also contain information on the proportion of hydrometeor classes and the dominant hydrometeor type computed from the measurements of MXPol.</p> <p>- <em>MRR_PRO_06.zip, MRR_PRO_22.zip, and MRR_PRO_23.zip</em><br> The three archives contain the radar variables collected by the K-band Doppler profiling radars (MRR-PRO) deployed in a transect across the mountain range south of PEA.</p> <p>- <em>aws_radiometers.zip</em><br> This archive contains the measurements collected by the Automated Weather Station (AWS) installed alongside each of the three MRR-PRO. The data from the two radiometers deployed at the MRR-PRO 06 site have also been included in the archive.</p> <p> </p> <p><strong>Content of the NetCDF4 files</strong></p> <p>A “short name” is associated to each variables in the NetCDF4 files. This section provides a list of all the relevant variables collected by each instruments, alongside their short name.</p> <p> </p> <p>The following polarimetric variables are available for all the scans performed by MXPol, stored in the archives <em>MXPol_PPI.zip</em>, <em>MXPol_hydrometeor_types.zip</em>, <em>MXPol_sector_scans_2019.zip</em>, and <em>MXPol_sector_scans_2020.zip</em>:</p> <p>– the horizontal reflectivity factors (Z<sub>H</sub>), identified in the files by the short name “Zh”;</p> <p>– the vertical reflectivity factors (Z<sub>V</sub>), identified by the short name “Zv”;</p> <p>– the differential reflectivity (Z<sub>DR</sub>), identified by the short name “Zdr”;</p> <p>– the signal-to-noise ratio on the horizontal polarization channel (SNR<sub>H</sub>), identified by the short name “SNRh”;</p> <p>– the signal-to-noise ratio on the vertical polarization channel (SNR<sub>V</sub>), identified by the short name “SNRv”;</p> <p>– the mean Doppler radial velocity (V), identified by the short name “RVel”;</p> <p>– the spectral width (SW), identified by the short name “Sw”;</p> <p>– the total differential phase shift (Ψ<sub>DP</sub>), identified by the short name “Psidp”;</p> <p>– the differential phase shift (Φ<sub>DP</sub>), identified by the short name “Phidp”;</p> <p>– the specific differential phase on propagation (K<sub>DP</sub>), identified by the short name “Kdp”;</p> <p>– the co-polar correlation coefficient (ρ<sub>hv</sub>), identified by the short name “Rhohv”.</p> <p> </p> <p>The hydrometeor classification (Besic et al., 2016) and the retrieval of the proportion of each hydrometeor category in the radar volume (Besic et al., 2018) has been applied to all RHI scans of MXPol (archive <em>MXPol_hydrometeor_types.zip</em>), producing the following variables:</p> <p>– the dominant hydrometeor type, identified by the short name “hydro”,</p> <p>– the entropy computed by the classification algorithm, which provides an estimate of the confidence on the label assigned to the volume, identified by the short name “hydroclass_entropy”;</p> <p>– the proportion of each hydrometeor type in the volume, stored in the variables “proportion_AG” (aggregates), “proportion_CR” (ice crystals), “proportion_LR” (light rain), “proportion_RP” (rimed particles), “proportion_RN” (rain), “proportion_VI” (vertically-aligned ice), “proportion_WS” (wet snow), “proportion_MH” (melting hail);</p> <p>– the entropy computed by the demixing algorithm, identified by the short name “entropy”.</p> <p> </p> <p>The following radar variables are available for all the profiles collected by WProf, stored in the archive <em>WProf.zip</em>:</p> <p>– the equivalent reflectivity factor (Z<sub>e</sub>), identified in the files by the short name “Ze”</p> <p>– the signal-to-noise ratio (SNR), identified in the files by the short name “SnR”;</p> <p>– the mean Doppler radial velocity, identified in the files by the short name “Mean-velocity”;</p> <p>– the spectral width, identified in the files by the short name “Spectral-width”;</p> <p>– the skewness, identified in the files by the short name “Spectral-skewness”;</p> <p>– the kurtosis, identified in the files by the short name “Spectral-kurtosis”;</p> <p>– the noise level at each range gate gate, identified in the files by the short name “Noise_level”;</p> <p>– the noise floor at each range gate gate, identified in the files by the short name “Noise_floor”.</p> <p> </p> <p>The following retrievals (Billault-Roux et al., 2021) have been included in the WProf data files, stored in the archive <em>WProf.zip</em>:</p> <p>– the Integrated Water Vapor (IWV), identified in the files by the short name “Integrated-water-vapor”;</p> <p>– the Liquid Water Path (LWP), identified in the files by the short name “Liquid-water-path”.</p> <p> </p> <p>The following atmospheric variables, recorded by the automated weather station integrated in the radar, have been included in the WProf data files, stored in the archive <em>WProf.zip</em>:</p> <p>– the atmospheric pressure, identified in the files by the short name “Barometric-pressure”;</p> <p>– the air temperature, identified in the files by the short name “Environment-temp”;</p> <p>– the relative humidity with respect to liquid water, identified in the files by the short name “Rel-humidity”;</p> <p>– the horizontal wind direction, identified in the files by the short name “Wind-direction”;</p> <p>– the horizontal wind speed, identified in the files by the short name “Wind-speed”.</p> <p> </p> <p>The following variables are available for all the profiles collected by the three MRR-PRO, stored in the archives <em>MRR_PRO_06.zip, MRR_PRO_22.zip,</em><em> and</em><em> MRR_PRO_23.zip</em>:</p> <p>– the attenuated equivalent reflectivity factor (Z<sub>ea</sub>), identified in the files by the short name “Zea”;</p> <p>– the mean Doppler radial velocity, identified in the files by the short name “VEL”;</p> <p>– the spectral width, identified in the files by the short name “SW”;</p> <p>– the signal-to-noise ratio, identified in the files by the short name “SNR”;</p> <p>– the noise level computed by ERUO (Ferrone et al., 2022) at each range gate gate, identified in the files by the short name “noise_level”;</p> <p>– the noise floor computed by ERUO at each range gate gate, identified in the files by the short name “noise_floor”.</p> <p> </p> <p>The following variables are available for all the measurements collected by the three weather stations, stored in the archive <em>aws_radiometers.zip</em>:</p> <p>– the atmospheric pressure, identified in the files by the short name “pressure”;</p> <p>– the air temperature, identified in the files by the short name “temperature”;</p> <p>– the relative humidity with respect to liquid water, identified in the files by the short name “relative_humidity”;</p> <p>– the horizontal wind direction, identified in the files by the short name “wind_speed”;</p> <p>– the horizontal wind speed, identified in the files by the short name “wind_direction”.</p> <p> </p> <p>The following variables are available for all the measurements collected by the pyrgeometer and pyranometer, stored in the archive <em>aws_radiometers.zip</em>::</p> <p>– the total downwelling irradiance in the shortwave, identified in the files by the short name “shortwave_irradiance”;</p> <p>– the total downwelling irradiance in the longwave, identified in the files by the short name “longwave_irradiance”.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Besic, N., Figueras i Venturra, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmospheric Measurement Techniques, 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016.</p> <p>Besic, N., Gehring, J., Praz, C., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Unraveling hydrometeor mixtures in polarimetric radar measurements, Atmospheric Measurement Techniques, 11, 4847–4866, https://doi.org/10.5194/amt-11-4847-2018, 2018<strong> </strong></p> <p>Billault-Roux, A.-C. and Berne, A.: Integrated water vapor and liquid water path retrieval using a single-channel radiometer, Atmospheric Measurement Techniques, 14, 2749–2769, https://doi.org/10.5194/amt-14-2749-2021, 2021</p> <p>Ferrone, A., Billault-Roux, A.-C., and Berne, A.: ERUO: a spectral processing routine for the Micro Rain Radar PRO (MRR-PRO), Atmospheric Measurement Techniques, 15, 3569–3592, https://doi.org/10.5194/amt-15-3569-2022, 2022</p>
Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign
<p>We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.</p> <p> </p>
TEAMx-PC22 (TEAMx pre-campaign 2022) - ACINN Doppler wind lidar data sets (SL88, SLXR142)
<p><strong>ABSTRACT</strong></p> <p>The data sets found here were collected with <a href="http://acinn.uibk.ac.at/">ACINN</a>'s Doppler wind lidars SL88 and SLXR142 in Innsbruck, Austria, in summer 2022 in the framework of the TEAMx pre-campaign 2022 (TEAMx-PC22). 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 <a href="http://www.teamx-programme.org">http://www.teamx-programme.org</a> as well as in Serafin et al. (2020) and in Rotach et al. (2022).</p> <p><strong>DATA SET DESCRIPTION</strong></p> <p><strong>1. Spatial coverage and locations</strong></p> <p>Measurements with the SL88 and SLXR142 lidar were collected during TEAMx-PC22 in Innsbruck, Austria, at the Campus Innrain of the University of Innsbruck. More specifically, the SLXR142 lidar was located on the rooftop of one of the university buildings (Bruno-Sander-Haus) at Innrain 52f. The SL88 lidar was located in the forecourt of the Campus Innrain, the so-called GEIWI-Forum, next to the Bruno-Sander-Haus. The exact lidar locations are:</p> <ul> <li>SL88: 47.264083°N / 11.384986°E / 575 m MSL</li> <li>SLXR142: 47.26431°N / 11.38529°E / 613 m MSL</li> </ul> <p><strong>2. Temporal coverage</strong></p> <p>The TEAMx-PC22 lasted from mid-May 2022 to early October 2022. However, the SL88 data set contains a shorter period from 11 August to 02 October 2022 (1 Hz data, vertical stares). The SLXR142 data set covers an extended period from 01 May to 31 October 2022 (VAD products, 10-min averages) as this lidar was operated in a semi-permanent mode.</p> <p><strong>3. Instrument details</strong></p> <p><em><strong>General</strong></em></p> <p>Measurements were taken with two scanning Doppler wind lidars, model Stream Line (SL88) and Stream Line XR (SLXR142), manufactured by HALO Photonics. The SL88 and SLXR142 are part of the Innsbruck Atmospheric Observatory (IAO; Karl et al. 2020). Available here are vertical profiles of radial velocity and backscatter data based on vertical stares at 1 Hz for the SL88 lidar and vertical profiles of horizontal winds (10-min averages) derived from plan position indicator (PPI) scans by applying the VAD method for the SLXR142 lidar. PPI scans were performed as continuous motion scans (CSM mode) at an azimuth angle of 70°. For continuous motion scans, the scanner moves continuously (changing its azimuth angle) while data is being acquired.</p> <p><em><strong>Data correction</strong></em></p> <p>No corrections were applied to the data (level0 data).</p> <p><strong>4. Data file structure</strong></p> <p><em><strong>File format</strong></em></p> <p>Provided are data in netCDF format. File names contain date and time information in UTC. The following wildcard characters are used in the file examples below: yyyy - year; mm - month, dd - day; HH - hour, MM - minute, `SS` - second. NetCDF data files are zipped together into the following zip files.</p> <p><em><strong>Zip files</strong></em></p> <p>SL88.zip contains netCDF files of SL88 data structured into subdirectories (one subdirectory for each month, yyyymm, and one for each day, yyyymmdd).</p> <p>SLXR142.zip contains netCDF files of SLXR142 data structured into subdirectories (one subdirectory for each month, yyyymm).</p> <p><em><strong>NetCDF files for uncorrected SL88 data</strong></em></p> <p>Stare_88_yyyymmdd_HH_l0.nc contains vertical stare measurements aggregated together in one netCDF file for each hour (uncorrected level0 data).</p> <p><em><strong>NetCDF files for SLXR142 data products</strong></em></p> <p>yyyymmdd.nc contains vertical profiles of the horizontal wind vector derived from PPI scans by applying the VAD technique. Each vertical profile is based on several PPI scans conducted at an elevation angle of 70° within 10 minutes. Hence, each profile represents a 10-min average. Profiles are aggregated together for each day in a separate netCDF file.</p> <p><strong>6. Contact</strong></p> <p>Contact alexander.gohm(at)uibk.ac.at for any questions regarding the data set.</p> <p><strong>7. References</strong></p> <p>Karl, T., A. Gohm, M.W. Rotach, H.C. Ward, M. Graus, A. Cede, G. Wohlfahrt, A. Hammerle, M. Haid, M. Tiefengraber, C. Lamprecht, J. Vergeiner, A. Kreuter, J. Wagner, M. Staudinger, 2020: Studying urban climate and air quality in the Alps: The Innsbruck Atmospheric Observatory. <em>Bulletin of the American Meteorological Society,</em> <strong>101,</strong> E488–E507, <a href="https://doi.org/10.1175/bams-d-19-0270.1">https://doi.org/10.1175/bams-d-19-0270.1</a></p> <p>Serafin, S., M. W. Rotach, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. De Wekker, M. Evans, V. Grubišić, 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: <em>Multi-scale transport and exchange processes in the atmosphere over mountains: Programme and experiment.</em> Innsbruck University Press. <a href="https://doi.org/10.15203/99106-003-1">https://doi.org/10.15203/99106-003-1</a></p> <p>Rotach, M. W., S. Serafin, H. C. Ward, M. Arpagaus, I. Colfescu, J. Cuxart, S. F. J. D. Wekker, V. Grubišic, 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. <em>Bulletin of the American Meteorological Society,</em> <strong>103,</strong> E1282–E1295. <a href="https://doi.org/10.1175/bams-d-21-0232.1">https://doi.org/10.1175/bams-d-21-0232.1</a></p>
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