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Cold air drainage transect studies at the Andrews Experimental Forest, 2002 to Present
Temperature data are being collected to investigate the effects of elevation and local topographic position on the patterns of cold air drainage and pooling in the HJ Andrews Forest, and how they vary with synoptic weather patterns. The main focus of the study is cold air drainage in the Lookout Creek area, but others sites have been added to sample temperature signatures at other locations of the forest with topographic positions that need to be better understood. Here, quality controlled 15-minute raw data and aggregated daily minimum and maximum temperatures are available from several stations along 4 separate transects or clusters with some additional single sensors also included. Several of the quality flags indicate that the data failed the test and should not be used in analysis.
Eradication via destratification: whole-lake mixing to selectively remove rainbow smelt, a cold-water invasive species.
Rainbow smelt (Osmerus mordax) are an invasive species associated with several negative changes to lake ecosystems in northern Wisconsin. We combined empirically based bioenergetics models with empirically based hydrodynamic models to assess lake destratification as a potential rainbow smelt eradication method. The dataset reported here is the otolith data from 20 age 1plus individuals.
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 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 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which 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.</p> <h3><strong>WRF Simulation Output</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 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. 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_jan16_nosnow</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_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>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_jan16_nosnow.nc</em>.</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>
Model data from GRL paper: "Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone"
<p>This folder includes monthly model data (experiments using SC-WACCM4 and E3SMv1) of temperature (TEMP) and sea level pressure (SLP) that were used in the Geophysical Research Letters paper "<strong>Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone</strong>", # 2020GL088583. See also for additional information/data: <a href="https://zenodo.org/record/3066448">https://zenodo.org/record/3066448</a></p> <p>Labe, Z., Peings, Y., & Magnusdottir, G. (2020). Warm Arctic , cold Siberia pattern : role of full Arctic amplification versus sea ice loss alone. <em>Geophysical Research Letters</em>, 1–26. <a href="https://doi.org/10.1029/2020GL088583">https://doi.org/10.1029/2020GL088583</a></p> <p><a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020GL088583">[Paper]</a><a href="https://sites.uci.edu/zlabe/arctic-amplification/">[Plain Language Summary]</a><a href="https://github.com/zmlabe/AA">[GitHub]</a></p>
Fiber-optic Distributed Temperature Sensing and Wind Profiler Data during the Shallow Cold Pool Experiment
<p>The <a href="https://www.eol.ucar.edu/field_projects/scp">Shallow Cold Pool (SCP) experiment</a> was an <a href="https://www.eol.ucar.edu/observing_facilities/isfs">Integrated Surface Flux System (ISFS)</a> deployment conducted by the <a href="https://ncar.ucar.edu/">National Center for Atmospheric Research (NCAR)</a>, the <a href="https://ceoas.oregonstate.edu/">College of Earth, Ocean and Atmospheres (CEOAS)</a>, the <a href="https://bee.oregonstate.edu/">Department of Biological & Ecological Engineering (BEE)</a>, and the <a href="https://ctemps.org/">Center for Transformative Environmental Monitoring Programs (CTEMPS)</a> of <a href="https://oregonstate.edu/">Oregon State University</a>, in a shallow gully within the Pawnee Grasslands, Coloradp, USA. The primary goal of SCP was to examine the formation and maintenance of common shallow cold pools. These cold pools had not been previously examined with turbulence measurements and very little was known about their dynamics and interaction with gravity waves and other submesoscale motions.</p> <p>SCP consisted of a dense network of ultrasonic anemometers with 19 units being installed at 1m above ground level (agl) and 8 being mounted at different heights on a 20m high tower. In addition, air temperature, humidity, and carbon dioxide concentrations measurements were taken. This data can be found on <a href="https://data.eol.ucar.edu/project/SCP">https://data.eol.ucar.edu/project/SCP</a>.</p> <p>The unique observational technique featured in SCP was a cross-valley transect of the innovative active and passive fiber-optic distributed sensing technique (FODS) using a Distributed Temperature Sensing (DTS) unit (Model Ultima SR, Silixa, London, UK) as well as a ground-based acoustic wind profiler (SODAR, PCS2000-24, Metek GmbH, Elmshorn, Germany) in addition to the classical sonic anemometer network. The data archived in this submission publishes the FODS data and contains data for nine (9) nights between 16th November until 27th November between the hours of 19:00 and 05:00 MST (Local time). Details of the FODS setup are contained in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a> and <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. (2015</a>).<br> The fiber-optic cross-valley transect was 240m long and stretched from the North to the South shoulder of the gully and contained FODS observations at three heights (0.5m, 1m, 2m agl). By combining passive and active FODS, air temperatures and wind speeds were measured spatially continuously with a temporal and spatial resolution of 5s and 25cm, respectively. Air temperatures were measured with an unheated white-PVC jacketed optical glass fiber cable with an outer diameter of 0.9mm, while for the wind speed measurements an additional actively heated stainless-steel uncoated fiber-optic cable (1.3mm outer diameter) was deployed. Wind speeds were derived from the difference between the heated and unheated fiber-optic pair similar to a hotwire anemometer (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GL066729">Sayde et al. 2015</a>).<br> The acoustic wind profiler (Sound Detection and Ranging, SODAR) was installed at the gully bottom about 200m down the gully from the fiber-optic transect (between station A18 and A19) and measured with a 5-min resolution, a 10-m gate range, and 17000 Hz, see map in <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3508?af=R">Pfister et al. (2019)</a>. The observational range was between 10m to 320m agl. The data provided is the cluster data output of the wind profiler, which is quality-controlled by the internal data processing software. The published data include horizontal wind speed (speed), wind direction (direction), unrotated along-wind component (u_unrot), unrotated cross-wind component (v_unrot), and unrotated vertical-wind component (w_unrot).</p> <p>By combining the fiber-optic distributed sensing, the sensor network, and the wind profiler, we were able to investigate specific class of submeso-scale motions in detail. The submeso-scale motion occurred frequently during SCP, significantly impacted air temperature, wind speed and direction, as well as the near-surface turbulence within less than a few minutes. These motions are not described or categorized by existing boundary layer regimes or concepts. Consequently, further research on submeso-scale motions using continuous FODS measurements is necessary to better understand the stable boundary layer.</p> <p> </p> <p>Pfister, L., Sayde, C., Selker, J., Mahrt, L., & Thomas, C. K. (2019). Classifying the Nocturnal Atmospheric Boundary Layer into Temperature and Flow Regimes. <em>Quart. J. Roy. Meteorol. Soc.</em>, <em>145</em>(721), 1515–1534. <a href="https://doi.org/10.1002/qj.3508">https://doi.org/10.1002/qj.3508</a></p> <p> </p> <p>Sayde, C., Thomas, C. K., Wagner, J., & Selker, J. S. (2015). High-resolution wind speed measurements using actively heated fiber optics. <em>Geophys. Res. Lett.</em>, <em>42</em>(22), 10,064–10,073. <a href="https://doi.org/10.1002/2015GL066729">https://doi.org/10.1002/2015GL066729</a></p>
Storage enhanced nonlinearities in a cold atomic Rydberg ensemble: experimental data
<p>The data show number of input/output photons under different conditions when coherent pulses of light undergo electromagnetically induced transparency (EIT) in a cold cloud of Rubidium 87 atoms via a ladder system connecting the ground state of 87-Rubidium and different Rydberg levels via (see more details in Distante et al. Phys. Rev. Lett. <strong>117</strong>, 113001 (2016) or in the preprint https://arxiv.org/abs/1605.07478)</p> <p>This is the pre-analysed data from which the results in the paper are derived.</p> <p> </p> <ul> <li>The ODS file contains different sheets which correspond to Rydberg states with different principal quantum numbers</li> <li>The PDF contains useful information regarding the conditions of the experiment under which the data was obtained, such as the optical depth (OD) of the cloud, its dimensions, and the Rabi frequency of the coupling beam.</li> </ul>
An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing - supporting data
<p>These are the data presented in figures 3, 4, 5, 6 and 7 of our paper "An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing". The first line of each data file explains the content. The second line labels the columns. The remaining rows give the data.</p>
Identifying South African Marine Protected Areas at risk from marine heatwaves and cold spells
<p>This data reflects information on marine heatwaves (MHWs) and marine cold spells (MCSs) that occurred along the South African coast from January 1982 to April 2022, with special focus on Marine Protected Areas. Thermal metrics for MHW and MCS events were obtained using the HeatwaveR package (Schlegel and Smit, 2018) and the associated Marine Heatwave Tracker (Schlegel, 2020). </p> <p> </p> <p>THis data stems from Courtailac et al (in review) Indentifying South AFrican Marine Protected Areas at risk of marine heatwaves and cold-spells </p>
Influence of the tropical Indian Ocean tripole on summertime cold extremes over central Siberia
<p>These experiments are used to study atmospheric circulation responses to SST forcing related to Indian Ocean tripole mode, including the precipitation, zonal and meridional winds.</p>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Supporting data of: Hydrography and food distribution during a tidal cycle above a cold-water coral mound
<p>This file contains the raw data and data analyses scripts to:</p> <p>Hydrography and food distribution during a tidal cycle above a cold-water coral mound</p> <p>Evert de Froe, Sandra R. Maier, Henriette G. Horn, George A. Wolff, Sabena Blackbird, Christian Mohn, Mads Schultz, Anna-Selma van der Kaaden, Chiu H. Cheng, Evi Wubben, Britt van Haastregt, Eva Friis Moller, Marc Lavaleye, Karline Soetaert, Gert-Jan Reichart, Dick van Oevelen.</p> <p>Deep Sea Research Part I: Oceanographic Research Papers, 2022,<br> ISSN 0967-0637,<br> https://doi.org/10.1016/j.dsr.2022.103854.<br> <strong>Abstract: </strong>Cold-water corals (CWCs) are important ecosystem engineers in the deep sea that provide habitat for numerous species and can form large coral mounds. These mounds influence surrounding currents and induce distinct hydrodynamic features, such as internal waves and episodic downwelling events that accelerate transport of organic matter towards the mounds, supplying the corals with food. To date, research on organic matter distribution at coral mounds has focussed either on seasonal timescales or has provided single point snapshots. Data on food distribution at the timescale of a diurnal tidal cycle is currently limited. Here, we integrate physical, biogeochemical, and biological data throughout the water column and along a transect on the south-eastern slope of Rockall Bank, Northeast Atlantic Ocean. This transect consisted of 24-hour sampling stations at four locations: Bank, Upper slope, Lower slope, and the Oreo coral mound. We investigated how the organic matter distribution in the water column along the transect is affected by tidal activity. Repeated CTD casts indicated that the water column above Oreo mound was more dynamic than above other stations in multiple ways. First, the bottom water showed high variability in physical parameters and nutrient concentrations, possibly due to the interaction of the tide with the mound topography. Second, in the surface water a diurnal tidal wave replenished nutrients in the photic zone, supporting new primary production. Third, above the coral mound an internal wave (200 m amplitude) was recorded at 400 m depth after the turning of the barotropic tide. After this wave passed, high quality organic matter was recorded in bottom waters on the mound coinciding with shallow water physical characteristics such as high oxygen concentration and high temperature. Trophic markers in the benthic community suggest feeding on a variety of food sources, including phytodetritus and zooplankton. We suggest that there are three transport mechanisms that supply food to the CWC ecosystem. First, small phytodetritus particles are transported downwards to the seafloor by advection from internal waves, supplying high quality organic matter to the CWC reef community. Second, the shoaling of deeper nutrient-rich water into the surface water layer above the coral mound could stimulate diatom growth, which form fast-sinking aggregates. Third, evidence from lipid analysis indicates that zooplankton faecal pellets also enhance supply of organic matter to the reef communities. This study is the first to report organic matter quality and composition over a tidal cycle at a coral mound and provides evidence that fresh high-quality organic matter is transported towards a coral reef during a tidal cycle.</p> <p> </p>
Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak
<p>Dataset of model simulations discussed in paper "Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak", <a href="https://doi.org/10.1016/j.atmosres.2022.106302">https://doi.org/10.1016/j.atmosres.2022.106302</a></p>
Marine heatwaves and cold spells events based on ESA-CCI SSTs (experimental product)
<p>This repository contains an extension of the catalogues of marine heatwaves (MHWs) and cold spells (MCSs) prepared by the National Research Council - Institute of Marine Sciences (CNR-ISMAR, Italy) within the ESA-funded CAREHeat project. The catalogues are based on the ESA-CCI sea surface temperature (SST) dataset (available from https://doi.org/10.24381/cds.cf608234) for the period 1982-2022, on a regular 1°x1° longitude-latitude grid.</p> <p>Events are identified for each pixel following the methodology of Hobday et al. (2016) after preprocessing. Event categories are provided as daily maps and metrics are given by event. Results are <strong>experimental</strong> since the post-processing procedure effectively removes interannual variability from the SST record, so please use having consulted the documentation and not for operational purposes. </p> <p><br>Please cite the reference paper "Serva, F., et al.: Detection of Satellite Sea Surface Temperature Extremes: Low Frequency Variability and Climate Change, JGR:Oceans, 10.1029/2025JC022886, 2025" when using the dataset in your work.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 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 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which 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.</p> <h3><strong>WRF Simulation Output</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 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with 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 files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</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_jan16</em> and <em>windout_40m_jan16_sms</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_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</p>
Supplementary material to article "Hygrothermal performance of a brick wall with interior insulation in cold climate: vapour open vs vapour tight approach"
<p>Supplementary material to article "Hygrothermal performance of a brick wall with interior insulation in cold climate: vapour open vs vapour tight approach"</p>
Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters
<p>Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters. The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
Raw data: Local-scale feedbacks influencing cold-water coral growth and subsequent reef formation
<p>Spreadsheets with the raw data of ADV-measured current velocity, coral growth derived from buoyant weight measurements and stress-related protein activities and concentrations.</p>
Supplementary Information and EBSD data for 'Intermetallic phase layers in cold metal transfer aluminium-steel welds with an Al-Si-Mn filler alloy'
<p>Supplementary information and electron backscatter diffraction (EBSD) data for the article entitled 'Intermetallic phase layers in cold metal transfer aluminium-steel joints with an Al-Si-Mn filler alloy'. There are three EBSD datasets, I-III, named "I_EBSD.dat" - "III_EBSD.dat", each with corresponding calibration and background patterns, as well as secondary electron scanning electron microscopy images showing the scanned area and text files containing the acquisition parameters. The data analysis workflow has been published on GitHub, see References.</p>
ScienceDex guides
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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.