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3,018 results for “AIR”
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Influence of Atmospheric Air Plasma Pre-Treatment of Veneers on the Mechanical Properties and Stability of Beech Plywood
<p>Wood-based sheet materials such as plywood, fiberboard, particleboard, and oriented strain board find applications in civil engineering, building technology, furniture manufacturing and many more. All these materials rely strongly on an effective bond formation between the resin and the wood base material, which gives rise to their mechanical performance and stability, as well as their resistance to moisture and liquids. In our study, we present the use of a commercial atmospheric air plasma system, which we used for the pretreatment of veneers of common beech (<em>Fagus sylvatica</em> L.) wood before formation of plywood boards. Plasma treatment parameters were optimized following the change in water contact angle. Two different stacking patterns were used for plasma-treated veneers. The time stability of the plasma modification was investigated by forming a second set of plywood boards 70 hours after plasma treatment of the respective veneers. The influence of the plasma treatment on mechanical properties was studied via bending and shear strength of the four sets of plasma-treated boards in comparison to a plywood out of the same veneer without plasma treatment. Water and moisture resistance were tested through water immersion and surface water resistance tests. Further, confocal laser scanning microscopy was used to determine changes of the surfaces’ morphologies.</p>
Surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios.
<p>Surface maps and basin mean/total of annual mean surface alkalinity, pH (total scale) and CO2 air-sea flux of the Mediterranean Sea under different alkalinisation scenarios and for underlying the baseline projection (RCP4.5).</p> <p>Details on simulations and alkalinisation strategies are given in the reference article below.</p> <p> </p> <p>Reference:</p> <p>Butenschön, M., Lovato, T., Masina, S., Caserini, S., Grosso, M., 2021. Alkalinization Scenarios in the Mediterranean Sea for Efficient Removal of Atmospheric CO2 and the Mitigation of Ocean Acidification. Front. Clim. 3. <a href="https://doi.org/10.3389/fclim.2021.614537">https://doi.org/10.3389/fclim.2021.614537</a></p>
ADS-C Air Traffic Data Collected by the OpenSky Network
<p>ADS-C data collected by the OpenSky Network since 7th July 2023. </p> <p>Data underlying (Version 1.1)</p> <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> <p>https://journals.open.tudelft.nl/joas/article/view/7229</p>
Inter-Chemical Correlation results for the study: HHEARx2017-1729 (Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles)
Title: Air Pollution, Placenta Function, and Birth Outcomes in Los Angeles <br>Species: Homo sapiens <br>Number of samples: 450 <br>Number of named analytes: 14 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=48 <br>
Isoprene mixing ratio in ambient air across the Southern Ocean in the austral summer of 2016/2017, during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the mixing ratios of isoprene (C5H8) in ambient air measured during the Antarctic Circumnavigation Expedition around the Southern Ocean in the austral summer of 2016/2017 using the iDirac, an autonomous gas chromatograph (Bolas et al., 2020). Samples were collected at a temporal resolution of approximately 10 minutes, with sizeable gaps in the dataset during Legs 1 and 3 due to instrument malfunction.</p> <p>Isoprene represents one of the largest biogenic emissions on the planet. While the magnitude and mechanism of its emissions on land are well established, there is still large uncertainty on the drivers of marine isoprene emissions. This dataset, consisting of continuous measurements at a relatively high temporal resolution, represents a unique opportunity to better understand marine isoprene emissions in the Southern Ocean.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_isoprene_mixing_ratio_ambient_air_v1.1.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>more accurate isoprene quantification from improved chromatogram baseline removal</li> </ul> <p>v1.0 - initial release of dataset</p>
Weather and Air Quality data for Ireland as RDF data cube
<p>Weather, Air Pollution and Events data represented as RDF data cube. The original weather data has been downloaded from https://www.met.ie//climate/available-data/historical-data and the Air Quality data from <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm</a> and <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm</a>. The Events data refers to random events within the Republic of Ireland.</p> <p>The data has then been uplifted by running the {eeaMapping, metMapping, eventsMapping}.py scripts, which generate R2RML mappings to convert the CSV data to RDF. The mappings re-use vocabularies and ontologies that are W3C recommendations for dataset descriptions (DCAT, https://www.w3.org/TR/vocab-dcat-2/), statistical data (RDF Data Cube, https://www.w3.org/TR/vocab-data-cube/) and provenance data (PROV-O, https://www.w3.org/TR/prov-o/). The scripts use the R2RML engine from https://github.com/chrdebru/r2rml to execute the mappings which generate a data and metadata files for each of the datasets.</p> <p> </p>
Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"
<p>Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript. </p> <p>Two modifications have been made in module_aerosols_soa_vbs.F:</p> <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> <p>The model data directory includes:</p> <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>
Measurements of benzene and toluene in underway surface seawater and ambient air in the Atlantic sector of the Southern Ocean on cruise ANDREXII/JR18005 between February and April 2019.
<p>Benzene and toluene cycling iin the unpolluted marine environment s poorly understood. Due to a paucity of measurements, the role of the ocean in the atmospheric budgets of atmospheric benzene and toluene is unknown. In order to quantify the air-sea fluxes of these gases and obtain insights to their biogeochemical cycling, we measured their seawater concentrations (surface and depth profiles) and air mixing ratios in the Atlantic sector of the Southern Ocean, along a ~11000 km long transect at approximately 60o S in Feb-Apr 2019. The measurements were made using a Proton Transfer Reaction Mass Spectrometer coupled to a Segmented Flow Coil Equilibrator. Concentrations, oceanic saturations and calculated fluxes benzene and toluene are presented here. </p> <p> </p> <p>The data is further presented and discussed in a manuscript: </p> <p>Marine biogenic benzene and toluene emissions and their impact on secondary organic aerosol in the polar regions. Charel Wohl, Qinyi Li, Carlos A. Cuevas, Rafael P. Fernandez, Mingxi Yang, Alfonso Saiz-Lopez, Rafel Simó<span>, </span>Submitted to Atmospheric Atmospheric Chemistry and Physics, 2022</p> <p> </p> <p>Computation of the air-sea gas fluxes is explained in detail in the linked manuscript about benzene and toluene. <br> Positive values indicate oceanic outgassing, thus sea to air flux.</p> <p> </p> <p>Definitions of acronyms, site abbreviations, or other project-specific designations:<br> deg = degree <br> SW = seawater concentration</p> <p>ATM= atmosphere</p> <p>SAT = saturation</p> <p>flux= air-sea flux in (micro)umol_m^(2)_d^(-1)<br> nM = nano Molar seawater concentration defined as nmol dm^(-3)</p> <p>LAT, LONG = Latitude, Longitude. (negative indicates west and south)</p> <p>The timestamp indicates sampling time in UTC, expressed as DD/MM/YYYY_HH:MM</p> <p>Empty data cells/points are listed as an impossible number of -999. Interruptions in the measurements are due to calibrations and other instrument maintenance.Interruptions in the calculated flux are due to missing auxiliary data at those sampling points e.g. no wind speed or underway auxiliary data.</p> <p>Fluxes and saturations computed using the interpolated air mixing ratio (see linked manuscript) are indicated with the suffix "_2"</p> <p> </p> <p>Negative values correspond to readings below the blank and detection limit. <br> They are effectively zero and are included here as the computed negative concentration to avoid skewing the mean.</p> <p> </p> <p>Data last modified 06.05.2022. Version 1 uploaded on that date. No further maintenance planned. This is the final data.</p> <p> </p>
Battery-less Environment Sensor Using Thermoelectric Energy Harvesting From Soil-Ambient Air Temperature Differences
<p>The data set contains the data collected from experiments sites in Belgium ( Campus Drie Eiken, University of Antwerp, 51.161° N, 4.408° W) and Iceland ( Forhot, 64.008° N, 21.178° W) for the research and evaluation of a battery-less environment sensor powered by energy harvesting. The device uses the temperature difference between soil and air to produce energy with the help of a Thermoelectric Generator (TEG) and powers a wireless sensor node. The data set includes data collected from 2 phases of the study. One during the initial evaluation phase where we collected soil temperatures at 15 cm and air temperature to evaluate the possibilities of producing energy from the temperature differences. Using these data, we estimated the energy production capacity for both sites. Further, a proof-of-concept device was developed, and its performance was evaluated with field experiments. During this process, we collected the voltage level of the storage unit, i.e, the capacitor, air and soil temperatures and the TEG output voltage. During both phases, the same methods were employed to collect data. The voltage values were measured with a 12-bit ADC and the temperature was measured with 1-Wire temperature sensor. Further, the collected data were transferred to cloud storage in real-time for further analysis and evaluation. </p> <ul> <li><strong>cde_mseasurements_oct2020-nov2020.csv</strong> <ul> <li> Soil temperature and air temperature data from the Campus Drie Eiken at the University of Antwerp, Belgium. The data were collected from 2 Oct 2020 to 17 Nov 2020.</li> </ul> </li> <li><strong>cde_teg_measurements.csv</strong> <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG from Campus Drie Eiken at the University Antwerp, Belgium from 21 Apr 2021 to 25 Apr May 2021. Also includes the difference calculated between the two temperature values.</li> </ul> </li> <li><strong>cde_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from Campus Drie Eiken at the University of Antwerp.</li> </ul> </li> <li><strong>aui_measurements_nov-2021.csv</strong> <ul> <li>Soil temperature and air temperature data from the Forhot research site in Iceland for the month of November 2021.</li> </ul> </li> <li><strong>aui_teg_measurements.csv</strong> <ul> <li>Soil temperature, ambient temperature and the open-circuit voltage of TEG collected from the Forhot research site in Iceland. Also includes the difference calculated between the two temperature values. The data were collected from 18 Nov 2021 to 30 Nov 2021</li> </ul> </li> <li><strong>aui_energy_simulated.csv</strong> <ul> <li>Energy production capacity estimated using the temperature data collected from the Forhot research site in Iceland.</li> </ul> </li> <li><strong>cde_capacitor_voltage.csv</strong> <ul> <li>The voltage level of the capacitor used by the battery-less device to buffer the harvested energy. The device was deployed at the Campus Drie Eiken and the data collection was carried out from 1 Mar 2022 to 12 Apr 2022. A 15 mF supercapacitor was used. </li> </ul> </li> </ul>
Marine plastics alter the organic matter composition of the air-sea boundary layer, with influences on CO2 exchange: a large-scale analysis method to explore future ocean scenarios
<p>Microplastics are substrates for microbial activity and can influence biomass production. This has potentially important implications in the sea-surface microlayer, the marine boundary layer that controls gas exchange with the atmosphere and where biologically produced organic compounds can accumulate. In the present study, we used six large scale mesocosms to simulate future ocean scenarios of high plastic concentration. Each mesocosm was filled with 3 m3 of seawater from the oligotrophic Sea of Crete, in the Eastern Mediterranean Sea. A known amount of standard polystyrene microbeads of 30 μm diameter was added to three replicate mesocosms, while maintaining the remaining three as plastic-free controls. Over the course of a 12-day experiment, we explored microbial organic matter dynamics in the sea-surface microlayer in the presence and absence of microplastic contamination of the underlying water. Our study shows that microplastics increased both biomass production and enrichment of carbohydrate-like and proteinaceous marine gel compounds in the sea-surface microlayer. Importantly, this resulted in a 3 % reduction in the concentration of dissolved CO2 in the underlying water. This reduction was associated to both direct and indirect impacts of microplastic pollution on the uptake of CO2 within the marine carbon cycle, by modifying the biogenic composition of the sea's boundary layer with the atmosphere.</p>
Regional Datasets for Air Quality Monitoring in European Cities
<p>The primary environmental health threat in the WHO European Region is air pollution, impacting the daily health and well-being of its citizens significantly. To effectively understand the impact, and dynamics of air quality a detailed investigation of different environmental, weather, and land cover indices is appropriate. To this end, this paper introduces three European cities’ spatiotemporal datasets, customized for air pollution monitoring at a regional level. The datasets are composed of major air quality, weather measurements and land use information. The duration is approximately from 2020 to 2023 with an hourly temporal resolution and a spatial resolution of 0.005◦. The temporal and spatiotemporal datasets are publicly released aiming to provide a solid foundation for researchers, analysts, and practitioners to conduct in-depth analyses of air pollution dynamics.</p>
Air Traffic Management hotspots in Europe with airline cost functions
<p>This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].</p> <p>The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:</p> <p>- the top level key is the id of the airport, the value is list a of all regulations available for this airport.</p> <p>- each item of the list is a dictionary, with keys:</p> <p> -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.</p> <p> -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.</p> <p> -- 'flight_ids': list of flight ids (in the same order than etas)</p> <p> -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].</p> <p> -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.</p> <p>[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 </p> <p>[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.</p> <p>Pre-print available here: <a href="https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms">https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms</a></p> <p>[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2013-2020
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2004-2012
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 1995-2003
<p>SPHERA (High Resolution REAnalysis over Italy) is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>
Simulations of Rising Air Bubbles in Viscoelastic Fluids
<p>Videos, plots and the underlying data, generated from simulations of rising air bubbles in viscoelastic fluids for varying relaxation times and bubble volumes. The underlying model for the viscoelastic fluid is the Giesekus model, which was discretized with the fully implicit log-conformation approach. The free surface is modeled using an interface-tracking method. For more details cf. Knechtges, P. <em>Simulation of Viscoelastic Free-Surface Flows</em> PhD thesis (RWTH Aachen, 2018).</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2019-01-01 to 2019-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2019.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRWRTM_2019_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.