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Atmospheric_river_land_hydrology_western_US_HUC8_datasets
<p>Dataset for the manuscript entitled "Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily meteorological and surface hydrological data from western U.S. WRF simulation. Data is aggregated to 8-digit Hydrological Unit (HUC8) watersheds.</p> <p> </p> <p>To use this dataset, please cite the following two publications:</p> <p> </p> <p>Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., & Richmond, M. (2018). Predictability of extreme precipitation in western U.S. watersheds based on atmospheric river occurrence, intensity, and duration. <em>Geophysical Research Letters</em>, 45, 11,693–11,701. <a href="https://doi.org/10.1029/2018GL079831">https://doi.org/10.1029/2018GL079831</a></p> <p> </p> <p>Chen, X., Leung, L. R., Wigmosta, M., & Richmond, M. (2019). Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds. <em>Journal of Geophysical Research: Atmospheres</em>, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Atmospheric, hydrodynamic and water quality observations from environmental-quality stations, water level sensors, acoustic Doppler velocimeters, and meteorological stations located at the Guadalquivir river estuary (2008 - 2010)
<p>The dataset included in this repository was obtained during the project entitled “Propuesta metodológica para diagnósticar las consecuencias de las actuaciones humanas en el estuario del Guadalquivir” funded by the Autoridad Portuaria de Sevilla (APS), by the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía), CTM2011-22580, MedEX (CTM2008-04036-E) and PR11-RNM-7722. The data were collected in real time from 2008 until 2010 with a remote monitoring system installed by the Institute of Marine Sciences of Andalusia (ICMAN-CSIC) (Navarro et al., 2011).</p> <p> </p> <p>The environmental quality station recorded turbidity, temperature, conductivity, normalized turbidity, dissolved oxygen, oxygen, oxygen saturation, percentage of oxygen saturation, fluorescence, normalized fluorescence, and salinity every thirty minutes. Current data were measured every 15 minutes by means of acoustic current profilers. The former datasets were obtained at several depths and different locations along the Guadalquivir estuary. Water level sensors recorded the position of the free water surface every 10 minutes at several locations along the Guadalquivir estuary. Wind velocity and direction and solar radiation were measured every 10 minutes in a meteorological station at the mouth of the Guadalquivir estuary.</p> <p>Brief description of dataset.</p> <ul> <li> <p>velocities.csv (in m/s)</p> </li> <li> <p>Turbidity.csv (in Volts), temperature (in Celsius), conductivity (in Siemens/m), normalized turbidity (in FNU), dissolved oxygen (mg/L), oxygen (in Volts), fluorescence (in Volts), normalized fluorescence (in Volts), oxygen saturation (mg/L), percentage of oxygen saturation (%), salinity (in PSU).</p> </li> <li> <p>qual_Salmedina.csv, R_mean (mean radiative flux in W/m²), R_max (max radiative flux in W/m²), Rel_humidity (relative humidity in %), D_mean (wind mean direction in degrees), D_max (wind maximum direction in degrees), D_sig (standard deviation of the wind direction in degrees), V_mean (mean wind velocity in m/s), V_max (maximum wind velocity in m/s), V_sig (standard deviation of the wind velocity in m/s), P_atm_mean (mean atmospheric pressure in mbar), T_mean (mean air temperature in Celsius), T_max (maximum air temperature in Celsius), T_sig (standard deviation of the air temperature in Celsius).</p> </li> <li> <p>Sealevel.csv (in meters)</p> </li> </ul> <p>A wide description of the datasets can be found in Navarro et al (2011).</p> <p>Contact person: infogdfa@ugr.es (or mcobosb@ugr.es)</p>
Data files for: Gigantic jet discharges evolve stepwise through the middle atmosphere
<p>Original video files and some other data belonging to the article "Gigantic jet discharges evolve stepwise through the middle atmosphere" published in Nature Communications on September 25th, 2019 (https://doi.org/10.1038/s41467-019-12261-y)</p> <p>The high-speed video .cine files can be read by (free) CineViewer and PCC software of Vision Research Inc. which can convert to avi files. For any questions, contact the first author.</p> <p> </p>
HiRISE Image Patches Obscured by Atmospheric Dust
<p><strong>Overview</strong></p> <p>The purpose of this dataset is to train a classifier to detect "dusty" versus "not dusty" patches within browse-resolution HiRISE observations of the Martian surface. Here, "dusty" refers to images in which the view of the surface has been obscured heavily by atmospheric dust.</p> <p>The dataset contains two sets of 20,000 image patches each from EDR (full resolution) and RDR ("browse" resolution) non-map-projected ("nomap") HiRISE images, with balanced classes. The patches have been split into train (n = 10,000), validation (n = 5,000), and test (n = 5,000) sets such that no two patches from the same HiRISE observation appear in more than one of these subsets. There could be some noise in the labels, but a subset of the validation images have been manually vetted so that label noise rates can be estimated. More details on the dataset creation process are described below.</p> <p> </p> <p><strong>Generating Candidate Images and Patches</strong></p> <p>To begin constructing the dataset, the paper "The origin, evolution, and trajectory of large dust storms on Mars during Mars years 24–30 (1999–2011)," by Wang and Richardson (2015), was used to compile a set of time ranges for which global or regional dust storms were known to be occurring on Mars. All HiRISE RDR nomap browse images acquired within these time ranges were then inspected manually to determine sets of images that were (1) almost entirely obscured by dust and (2) almost entirely clear of dust. Then, 10,000 patches from the two subsets of images were extracted to form the "dusty" and "not dusty" classes. The extracted patches are 100-by-100 pixels, which roughly corresponds to the width of one CCD channel within the browse image (the width of the raw EDR data products that are stitched together to form a full RDR image). Some small amount of label noise is introduced in this process, since a patch from a mostly dusty image might happen to contain a clear view of the ground, and a patch from a mostly non-dusty image might contain some dust or regions on the surface that are featureless and appear like dusty patches. A set of "vetting labels" is included, which includes human annotations by the author for a subset of the validation set of patches. These labels can be used to estimate the apparent label noise in the dataset.</p> <p>Corresponding to the RDR patch dataset, a set of patches are extracted from the same set of EDR images for the "dusty" and "not dusty" classes. EDRs are raw images from the instrument that have not been calibrated or stitched together. To provide some form of normalization, EDR patches are only extracted from the lower half of the EDRs, with the upper half being used to perform a basic calibration of the lower half. Basic calibration is done by subtracting the sample (image column) averages from the upper half to remove "striping," then computing the 0.1<sup>th</sup> and 99.9<sup>th</sup> percentiles of the remaining values in the upper half and stretching the image patch to 8-bit integer values [0, 255] within that range. The calibration is meant to implement a process that could be performed onboard the spacecraft as the data is being observed (hence, using the top half of the image acquired first to calibrate the lower half of the image which is acquired later). The full resolution EDRs, which are 1024 pixels wide, are resized down to 100-by-100 pixel patches after being extracted so that they roughly match the resolution of the patches from the RDR browse images.</p> <p> </p> <p><strong>Archive Contents</strong></p> <p>The compressed archive file contains two top-level directories with similar contents, "edr_nomap_full_resized" and "rdr_nomap_browse." The first directory contains the dataset constructed from EDR data and the second contains the dataset constructed from RDR data.</p> <p>Within each directory, there are "dusty" and "not_dusty" directories containing the image patches from each class, "manifest.csv," and "vetting_labels.csv." The vetting labels file contains a list of manually labeled examples, along with the original labels to make it easier to compute label noise rates. The "manifest.csv" file contains a list of every example, its label, and whether it belongs to the train, validation, or test set.</p> <p>An example ID encodes information about where the patch was sampled from the original HiRISE image. As an example from the RDR dataset, the ID "003100_PSP_004440_2125_r4805_c512" can be broken into several parts:</p> <ul> <li>"003100" is a unique numerical ID</li> <li>"PSP_004440_2125" is the HiRISE observation ID</li> <li>"r4805_c512" means the patch's upper left corner starts at the 4805<sup>th</sup> row and 512<sup>th</sup> column of the original observation</li> </ul> <p>For the EDR dataset, the ID "200000_PSP_004530_1030_RED7_1_r9153" is broken down as follows:</p> <ul> <li>"200000" is a unique numerical ID</li> <li>"PSP_004530_1030" is the HiRISE observation ID</li> <li>"RED7" is the CCD ID</li> <li>"1" is the CCD channel (either 0 or 1)</li> <li>"r9153" means that the patch is extracted starting at the 9153<sup>rd</sup> row (since all columns of the 1024-pixel EDR are used, no column is specified; it is implicitly always 0)</li> </ul> <p><strong>Original Data</strong></p> <p>The original HiRISE EDR and RDR data is available via the Planetary Data System (PDS), hosted at <a href="https://hirise-pds.lpl.arizona.edu/PDS/">https://hirise-pds.lpl.arizona.edu/PDS/</a></p>
Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations
<p>This dataset contains the reduced Io spectra used in the paper "Seasonal and longitudinal variability in Io's SO2 atmosphere from 22 years of IRTF/TEXES observations" (doi: 10.1016/j.icarus.2024.116151). There are 150 spectra, spanning from 2001 to 2023. These spectra are described in Table 1 of the paper.</p> <p>The spectra in the data file are listed in date order. For each spectrum, we first provide the date (YYMMDD format) and the mean Io central longitude at the time of the observation. This is then followed by the spectrum. Column 1 is the wavelength, in units of microns. Column 2 is the Io spectrum, which has been divided by a Callisto spectrum, flattened in order to correct for any residual continuum slope, and then normalized such that the continuum level is 1. </p>
GCMS Huygens Dataset and methane mole fraction in Titan atmosphere
<p>This archive contains the dataset relative to the publication "Reanalaysis of the Huygens GCMS dataset: I. High resolution<br>Methane vertical profile in Titan atmosphere" by Gautier et al. in A&A 2024.<br>The entire set of recalibrated GCMS data is provided and includes the GCMS level 3 data product generated during this work.<br>Following the nomenclature of the GCMS data archived on the PDS this file is named GCMS_1US_STG3.TAB. The associated file<br>GCMS_1US_STG3.FMT contains a description of each column following the existing archived format. An additional information<br>in the description using the keyword "CHANGED_STG2_3" indicates whether or not the column was modified between STG2 and<br>STG3 compared to the dataset retrieved on the PDS.<br>The molefractions.csv table contains the retrieved methane mixing ratio through the atmospheric column. First column is the<br>time since beginning of GCMS measurements. Column 2, 3 and 4 are the altitude, atmospheric pressure and temperature, respec-<br>tively, from HASI measurements for the corresponding time stamp. Column 5 and 6 are the retrieved methane mole fraction and its<br>standard deviation. For altitudes comprised between 146 and 30 km, data has been binned to a kilometric resolution to enhance S/N.<br>The associated values for time, altitude, pressure and temperature correspond to the average value of the bin in the GCMS and HASI<br>data. Associated methane mole fraction corresponds to the retrieved value for the binned data. From 30 km and below, we used the<br>native GCMS vertical resolution. In this range, timestamps correspond to the exact time of each measurement according to GCMS<br>clock. Corresponding altitude, pressure and temperature were calculated using a linear interpolation between the two nearest HASI<br>points. Reported methane mole fractions and uncertainties correspond to the retrieved values for each altitude smoothed using a 10<br>points moving median.</p>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
Data set of detected atmospheric rivers, cyclones, and fronts within the region of 75°N – 82.5°N, 0°E – 30°E and at Ny-Ålesund (Svalbard) for 2017 – 2021
<p>This data set contains times when atmospheric rivers, cyclones, or fronts have been detected within the broader region of 75°N – 82.5°N, 0°E – 30°E and specifically at Ny-Ålesund, Svalbard (78.92308 °N, 11.92108 °E) for the years 2017 to 2021. To this end, the detection methods, as described in Lauer et al. (2023), have been applied to the hourly-resolved ERA5 reanalysis (Hersbach et al., 2020) data. </p> <p>Data set overview</p> <p>Each file contains the times (year, month, day, hour in UTC) when the corresponding weather system, i.e. atmospheric river, cyclone and front, has been detected within the region of 75°N – 82.5°N, 0°E – 30°E. The last column indicates if the weather system was located also over Ny-Ålesund Svalbard (78.92308 °N, 11.92108 °E). </p>
Data archive for the peer-reviewed journal article "Links between atmospheric aerosols and sea state in the Arctic Ocean"
<p>This dataset accompanies the peer-reviewed journal article titled "Links between atmospheric aerosols and sea state in the Arctic Ocean" which was accepted for publication in the Journal of Atmospheric Environment in September 2024, https://doi.org/10.1016/j.atmosenv.2024.120844. </p> <p>This dataset contains information on sea surface properties, meteorology, and aerosol data from measurements conducted during the Arctic Century Expedition which was carried out in August and September of 2021 in the Russian Arctic region. The dataset contains the following information:</p> <p><br>1) aerosol_size_distributions.csv: The hourly averaged time-series of aerosol size distribution measurements from an aerodynamic particle sizer. Further information for this data file is provided in Meta_data_for_aerosol_size_distributions.txt.</p> <p><br>2) aerosol_composition_and_volume.csv: Time series of mass concentrations of Na+Mg (SSA proxy) and Al+Si+Ca (dust proxy) in aerosol particles collected on filters. The time-series also contains aerosol volume concentration information for the coarse and fine aerosol categories, i.e., samples with count median diameters larger than 0.99 µm and smaller than 0.99 µm, respectively. Further information for this data file is provided in Meta_data_for_aerosol_composition_and_volume.txt. </p> <p><br>3) sea_surface_elevation_time_series.pkl: a pickle file containing the sea surface elevation time-series. The sea surface elevation data was extracted from 3D-reconstructed sea surface data. The 3D reconstruction of the sea surface was achieved by processing stereoscopic images of the sea surface using the Waves Acquisition Stereo System (WASS) software (Bergamasco et al., 2017). Further information for this data file is provided in Metadata_for_sea_surface_elevation_time_series.txt.</p> <p><br>4) aerosol_meteo_wave_merged_data.csv: This file contains the time-series of merged hourly averages of aerosol number concentrations, meteorological data, environmental data, and sea surface properties. The dataset also contains the average coordinate of the research vessel and its distance to land masses throughout the expedition. The meteorological data were measured during the expedition and the original unmerged data are available in Thurnherr et al. (2024). Other environmental data, such as sea surface temperature, are obtained from the fifth generation ECMWF reanalysis for the global climate and weather (ERA5, Hersbach et al., 2023), and sea ice concentration was obtained from AMSR-2 daily satellite measurements (Copernicus Climate Change Service (C3S), 2020). Sea surface properties are extracted from time series of sea surface elevation. Further information for this data file is provided in Metadata_for_aerosol_meteo_wave_merged_data.txt.</p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs
<p>Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs"</p> <p>Correspondence: Alexander R. Groos (alexander.groos@fau.de)</p> <p><br>The repository contains:<br>(1) The raw data (log files) for each UAV-based atmospheric sounding<br>(2) The postprocessed and reformatted data for each sounding and vertical profile<br>(3) The commented R-Scripts for data processing, analysis and visualisation<br>(4) A subset of the meteorological data from the nearby weather stations</p> <p><br>Description of sub-folders:</p> <p>-aws_data<br>-- aws_fisistock.txt # meteorological data from AWS Fisistock for the period of the campaign<br>-- aws_gandegg.txt # meteorological data from AWS Gandegg for the period of the campaign<br>-- aws_sackhorn.txt # meteorological data from AWS Sackhorn for the period of the campaign</p> <p>- processed_data<br>-- kanderfirn_2021-06-16_10:45_p1_pprz.tab # meteorological data for first profile/descent at about <br>-- kanderfirn_2021-06-16_10:45_p2_fr.tab # flight recorder data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_p2_pprz.tab # meteorological data for second profile/descent at about 10:45 CEST<br>-- kanderfirn_2021-06-16_10:45_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST<br>-- .<br>-- .<br>-- .<br>-- kanderfirn_2021-06-16_16:50_p1_pprz.tab # meteorological data for first profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_fr.tab # flight recorder data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_p2_pprz.tab # meteorological data for second profile/descent at about 16:50 CEST<br>-- kanderfirn_2021-06-16_16:50_pprz.tab # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST<br>-- kanderfirn_soundings_2021-06-16.csv # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable<br>-- kanderfirn_turbulence_2021-06-16.csv # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent</p> <p>- raw_data<br>-- fr_kanderfirn_2021-06-16_10:45.LOG # flight recorder data from the sounding at about 10:45 CEST (binary file)<br>-- .<br>-- .<br>-- .<br>-- fr_kanderfirn_2021-06-16_16:50.LOG # flight recorder data from the sounding at about 16:50 CEST (binary file)<br>-- pprz_kanderfirn_2021-06-16_10:45.LOG # meteorological data from the sounding at about 10:45 CEST (human readable text file)<br>-- .<br>-- .<br>-- .<br>-- pprz_kanderfirn_2021-06-16_16:50.LOG # meteorological data data from the sounding at about 16:50 CEST (human readable text file)</p> <p>- R_scripts<br>-- figures.R # Script to create Figures 5, 6, 8, 9, 10, 11, 12<br>-- lapse_rate.R # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1)<br>-- postprocessing.R # Script to reformat preprocessed and preselected pprz-files<br>-- turbulence.R # Script for the calculation of the turbulence proxy from the recorded roll rate</p>
The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3
<p>Supporting data for figures in GMD draft paper: The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3</p>
Global 32-4 km variable-resolution mesh for the MPAS-Atmosphere model
<div> <div> <div>This mesh was created by a collaboration between the Department of Energy’s Water Cycle and Climate Extremes Modeling (WACCEM) project and the</div> <div>Mesoscale and Microscale Meteorology (MMM) Laboratory at the National Science Foundation National Center for Atmospheric Research (NSF/NCAR).</div> <div> </div> <div>The mesh contains 1,830,914 horizontal grid cells. The circular refinement region has a radius of approximately 20 degrees. The 4-32km grid-spacing range aims to achieve convection-permitting resolution in the high-resolution domain and resolution sufficient for the jet stream and mid-latitude wave activities (Lu et al., 2015) in the low-resolution domain.</div> <div> </div> <div>The netcdf file "x8.1830914.grid.nc" includes the variables defining the global unstructured grid for the MPAS model as described in the <a href="https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf">MPAS Mesh Specification</a>. Following the other MPAS mesh data, we provide graph.info.part.* files necessary for the Message Passing Interface (MPI) parallelism. For example, a simulation using 1024 MPI ranks will use graph.info.part.1024. For the number of MPI tasks not provided in this dataset, a user needs to create a new partitioning file using the METIS tool and the graph.info file as described in the MPAS user guide (available <a href="https://mpas-dev.github.io/atmosphere/atmosphere_download.html" target="_blank" rel="noopener">here</a>).</div> <div> </div> </div> </div> <div>This data will also be available from the <a href="https://mpas-dev.github.io/atmosphere/atmosphere_meshes.html">MPAS mesh website</a>.</div> <div> </div> <div>The mesh generation is supported by the U.S. Department of Energy Office of Science Biological and Environmental Research (BER) as part of the Regional and Global Model Analysis Program Area. We acknowledge the use of computational resources of the National Energy Research Scientific Computing Center (NERSC). The Pacific Northwest National Laboratory is operated for the Department of Energy by Battelle Memorial Institute under contract DE-AC05-76RL01830.</div>
Intermediate data products for: Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar (Zippel et al. 2021, Journal of Atmospheric and Oceanic Technology)
<p>This repository contains some of the intermediate data products needed to reproduce the results in the <em>Journal of Atmospheric and Oceanic Technology</em> article "Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar" by S.F. Zippel, J. T. Farrar, C. J. Zappa, U. Miller, L. St. Laurent, T. Ijichi, R. A. Weller, L. McRaven, S. Nylund, and D. Le Bel. Specifically, this material should allow reproduction of Figures 3, 5-7, 12 and 13. Reproduction of Figures 8-11 also requires data from associated glider deployments nearr the SPURS-1 mooring, which may be requested from co-author L. St. Laurent.</p> <p>Code to do the analysis and make the plots is here: https://github.com/zippelsf/MooredTurbulenceMeasurements</p> <p>Matlab data files:</p> <p>(1) 677404_burst1865.mat</p> <p>Single-burst data used for the example spectral fit in Figure 7. The burst was collected during the SPURS-1 project at 21.5m depth. The data collection and processing methods are described in detail in Section 2. </p> <p>(2) 811604_burst0510.mat (Single-burst data used in the unwrapping example, Figure 5)</p> <p>(3) 8116_dissipation_timeseries.mat (Used for associated ancillary data in Figure 6)</p> <p>(4) 913411_burst2879.mat (Single-burst data, used for ancillary data to make Figure 3).</p> <p>(5) BuoyancyFlux_b.mat</p> <p>Ocean buoyancy flux estimates for SPURS-2 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(6) BuoyancyFlux_c.mat</p> <p>Ocean buoyancy flux estimates for SPURS-1 dataset, created from the 1-hr "met" and "flux" files available on the UOP website, and using the Gibbs SeaWater (GSW) toolbox to estimate "alpha" and "beta". The estimated buoyancy fluxes were used for Figure 12.</p> <p>(7) SPURS1_dissipation_grid_v1d.mat</p> <p>Gridded TKE dissipation rates for SPURS-1 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 8-13. Dissipation rates also available on NASA's PODAAC.</p> <p>(8) spurs1_met_1hr.mat (Processed met data from SPURS-1 mooring. Also available on WHOI's UOP website.)</p> <p>(9) SPURS2_dissipation_grid_v1c.mat</p> <p>Gridded TKE dissipation rates for SPURS-2 dataset. Processing of these data is described extensively in Section 2. Data used in Figures 12. Dissipation rates also available on NASA's PODAAC.</p>
Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean
<p>These are the Wave glider data used in the analysis and creation of figures in Edholm et al. 2022: <em>Atmospheric Rivers Contribute to Summer Surface Buoyancy Forcing in the Atlantic Sector of the Southern Ocean</em> in support of open-code, transparency, and repeatability.</p> <p>Abstract:</p> <p>Atmospheric rivers (ARs) dominate moisture transport globally; however, it is unknown what impact ARs have on surface ocean buoyancy. This study explores the surface buoyancy gained by ARs using high-resolution surface observations from a Wave Glider deployed in the subpolar Southern Ocean (54°S, 0°E) between 19 December 2018 and 12 February 2019 (55 days). When ARs combine with storms, the associated precipitation is significantly enhanced (189%). In addition, the daily accumulation of AR-induced precipitation provides a buoyancy gain to the surface ocean equivalent to warming by surface heat fluxes. Over the 55 days, ARs accounted for 47% of the total precipitation equating to 10% of the summer surface ocean buoyancy gain. This study indicates that ARs play an important role in the summer precipitation over the subpolar Southern Ocean and that they can alter the upper-ocean buoyancy budget from synoptic to seasonal timescales.</p>
The metal content of the hot atmospheres of galaxy groups - supporting data
<p>Supporting data used to generate the figures included in the review chapter "The metal content of the hot atmospheres of galaxy groups", to appear in the MDPI journal "Universe". These values were collected and compiled from existing literature; each text file lists the relevant references to the original articles where various sets of results were initially published. </p>
Tropical Atmospheric Anomalies on the Surface (TROPAAS)
<p>This dataset contains the monthly, daily and 6-hour anomalies of the wind speed at 10 m, air temperature at 10 m, incident long-wave radiation, incident solar radiation, atmospheric pressure at sea level, the specific humidity at 10 m and precipitation in the entire tropical region (180°W – 178.125°E / 31.4281°S - 31.4281°N), calculated from the CORE v2 dataset (Coordinated Ocean-ice Reference Experiments) experiment with the correction of CIAF v2 (Corrected Inter-Annual Forcing). Covers the period from 1948 to 2009.</p> <p> </p> <p>There are 3 compressed files, each of which contains 6 3D grids in NetCDF files format with the following name description:</p> <p> </p> <p>anom_<frequency>_<variablename>_TROPAAS.nc</p> <p> </p> <p><frequency>:</p> <p>mm – Monthly means.</p> <p>dm – Daily means.</p> <p>6hrs – Each 6-hours.</p> <p> </p> <p><variablename>:</p> <p>precip – Precipitation.</p> <p>q - Specific humidity at 10 m.</p> <p>rad - Incident long-wave radiation and incident solar radiation.</p> <p>Slp - Pressure at sea level.</p> <p>T - Temperature at 10 m.</p> <p>winds - Zonal and meridional components and the module of the wind speed at 10 m.</p> <p> </p> <p>All the grids are made up of 192 x 34 nodes, with a spatial resolution of 1.875 x 1.904733 degrees.</p> <p> </p>
Sources and sinks of carbonyl sulfide inferred from tower and mobile atmospheric observations
<p>These datasets include the results of the combination of STILT simulations with COS and CO2 fluxes datasets as well as the observations at the Lutjewad measurement station (LUT, 53.4235°N, 6.3094°E). Please refer to the ReadMe file for further details.</p>
Atmospheric clumped O2 isotope composition simulation data and analysis scripts from EMAC/aMC models
<p>This publication contains source code, data and analysis scripts/results of the simulations presented in the following manuscript:</p> <blockquote> <p>Laskar, A.H., G.A. Adnew, S.S. Gromov, R. Peethambaran, B. Steil, J. Lelieveld, T. Blunier and T. Röckmann (2022). "Large variations in atmospheric oxidants and temperature during the Holocene" (in review)</p> </blockquote> <p> </p> <p><strong>EMAC simulations analysis</strong></p> <p>The analysis contains integrals of species burdens and other atmospheric physicochemical parameters obtained with the clumped isotopes of oxygen (CIO)-enabled ECHAM/MESSy Atmospheric Chemistry model (EMAC, see <a href="https://www.messy-interface.org">MESSy consortium website</a> for more information) model in various climate states. Simulations were performed in 2021–2022 at the <a href="https://www.dkrz.de">German Climate Computing Centre</a> (DKRZ) with the support of the <a href="https://www.palmod.de">PalMod project</a>.</p> <p>Analysis data is stored in human/machine-readable file <code>D36-EMAC-analysis.dat</code>, please refer to its header for variables description, etc.</p> <p>Additional (to those presented in the manuscript) analysis plots from EMAC data analysis are available in <code>D36-EMAC-analysis.vsz</code> (see the hardcopy in <code>D36-EMAC-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software.</p> <p> </p> <p><strong>2BM/MC (two-box Monte-Carlo) model code, simulation data and analysis</strong></p> <p>2BM/MC code/simulation setup is implemented within the advanced Monte-Carlo framework (aMC) and is available in the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO">respective repository</a>. A copy of the source code used to perform simulations is provided here (see <code>aMC-vpCIO.tar.gz</code> archive).</p> <p>2BM/MC output is stored in the <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF format</a> (ver. 4) and can be read in by any compatible software. The output contains probe statistics (reference <em>probed</em> distributions of the variables) in <code>vpCIO-probe_stat-*.nc</code> and resulting statistics (distributions <em>matching</em> given criteria, i.e. changes to the Δ36 signature vs. PD conditions) in <code>vpCIO-delta-*.nc</code> files, respectively.</p> <p>We use <a href="https://ferret.pmel.noaa.gov">NOAA Ferret</a> software to derive additional statistics of the third parameter (viz. average STE (<em>S</em>) changes) over the obtained 2D frequency histograms of other parameters (viz. changes to equilibration rate (<em>Req)</em> and temperature (<em>Teq</em>)). The scripts exemplifying this calculation are presented in <code>D36-vpCIO-analysis__proc*</code> files, which output results/overview plots in <code>vpCIO-delta-*__proc.nc</code> and <code>vpCIO-delta-*.gif</code> files.</p> <p>The analysis of the 2BM/MC simulation is available in <code>D36-vpCIO-analysis.vsz</code> script (see the hardcopy in <code>D36-vpCIO-analysis.pdf</code>) prepared using the <a href="https://veusz.github.io">Veusz</a> software. Note that some plots require the abovementioned third-parameter statistics as input.</p> <p><strong>Performing simulations with 2BM/MC</strong></p> <p>In order to perform simulations (e.g. with altered parameters), please follow the <a href="https://gitlab.com/sergey.gromov/amc/-/tree/vpCIO#integrating-your-code-building-executing">respective guide</a> for and build the <code>aMC-vpCIO</code> model. A typical sequence of shell commands to build and run 2BM/MC (which is referred to as <code>vpCIO</code> generic model within the <code>aMC</code>) is:</p> <pre><code># clone the distribution and check-out `vpCIO` branch or particular commit referenced in the repository history [user@pc]/~> git clone https://gitlab.com/sergey.gromov/amc.git [user@pc]/~> cd amc [user@pc]/~/amc> git checkout vpCIO # or unpack the source code available in this publication: [user@pc]/~> tar -xvf `aMC-vpCIO.tar.gz` [user@pc]/~> cd amc # build the aMC/vpCIO model executable # (note that you need at least a GCC or Intel compiler suite and respective netCDF v.4 library Fortran interface available in your environment): [user@pc]/~/amc> make vpCIO # adjust model setup (see the `vpCIO/amc.nml` namelist) ... # perform simulation [user@pc]/~/amc> cd vpCIO [user@pc]/~/amc/vpCIO> ./xamc # calculate additional statistics/produce overview with NOAA Ferret: [user@pc]/~/amc/vpCIO> ferret -gif -script D36-vpCIO-analysis__proc.jnl MH [user@pc]/~/amc/vpCIO> ./D36-vpCIO-analysis__proc</code></pre> <p>Note that output files contain the build timestamp and repository commit hash for the code used in the simulation, e.g.:</p> <pre><code>[user@pc]/~/amc/vpCIO> ncdump -h ./vpCIO-delta-dMH.nc | grep 'build' :build = "vpCIO@https://gitlab.com/sergey.gromov/amc__aMC_v1.9-110-g2566229@2022-12-09T16:43:12+01:00__built@2022-12-09T16:48:03+01:00__<user>@<email.com>" ;</code></pre> <p> </p> <p>Please contact Sergey Gromov ( sergey.gromov (at) mpic.de ) for additional information and access to the original experiment data.</p> <p> </p>
Atmospheric deposition in Mediterranean mountain catchments
<p>Bulk wet deposition data at Mediterranean mountain site from 1978 to 2019. Throughfall data in non continuous periods within the same date range.</p>
ScienceDex guides
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.