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1,574 results for “atmospheres”
La Silla (ESO) Atmospheric Extinctions
Open the record for dataset details and reuse information.
Atmospheric Rivers Antacrtica IPSL-CM6
<p>Netcdf file of detected Atmospheric Rivers (AR) in Antarctica using an vIVT based algorithm.</p> <p>Two time periods:</p> <ul> <li>"hist": 1995 to 2015, calculated from historical simulations from IPSL-CM6. r*i1p1f1 represent the diferente ensemble members</li> <li>"ssp245": calculated from scenarioMIP simulations from IPSL-CM6 with the ssp245 scenariop. 2015 to 2055 for folowing ensemble members: r2i1p1f1, r3i1p1f1, r4i1p1f1, r6i1p1f1, r14i1p1f1 r22i1p1f1 and 2015 to 2100 for r1i1p1f1, r5i1p1f1, r10i1p1f1, r11i1p1f1.</li> </ul> <p>Two threshods used during the detection:</p> <ul> <li>"hist_thr": Threshold based on the 98th percentile of Historical vIVT values (it is a fixed threshold).</li> <li>"adp_thr": Adaptive threshold scaled on the increase of humidity in the southern hemisphere.</li> </ul>
HIDRA simulations and post-processing scripts for JGR: SP manuscript: characterization of N+ abundances in the terrestrial polar wind using the multiscale atmosphere-geospace environment
<div> <div> <div> <p>The High-latitude Ionosphere Dynamics for Research Applications (HIDRA) model is part of the Multiscale Atmosphere-Geospace Environment (MAGE) model under development by the Center for Geospace Storms (CGS) NASA DRIVE Science Center. This study employs HIDRA to simulate upflows of H+, He+, O+, and N+ ions, with a particular focus on the relative N+ concentrations, production and loss mechanisms, and thermal upflow drivers as functions of season, solar activity, and magnetospheric convection. The simulation results demonstrate that N+ densities typically exceed He+ densities, N+ densities are typically ∼ 10% O+ densities, and N+ concentrations at quiet-time are approximately 50-100% of N+ concentrations during storm-time. Furthermore, the N+ and O+ upflow fluxes show similar trends with variations in magnetospheric driving. The inclusion of ion-neutral chemical reactions involving metastable atoms is shown to have significant effects on N+ production rates. With this metastable chemistry included, the simulated ion density profiles compare favorably with satellite measurements from Atmosphere Explorer C (AE-C) and Orbiting Geophysical Observatory 6 (OGO-6).</p> </div> </div> </div>
Data for "The influence of non-thermal collisions in Europa's atmosphere
<p>See "README.txt" for an explanation of how to read the data sets listed above in order to reproduce Figures 1-4 and the figure in the supplemental material.</p>
Flowfield Properties for Calculating Electrical Conductivity in a CO2 Atmosphere
<p>This dataset provides flowfield quantities necessary to calculate the electrical conductivity in the flowfield around an entry vehicle in a CO2 atmosphere. The CFD data was generated using the Data Parallel Line Relaxation Code. The freestream conditions cover velocities from 5 - 12 km/s and altitudes from 20 - 80 km at Mars (85 - 115 km at Venus). The vehicle geometry is a 10 m diameter 70 deg spherecone.</p>
Complete Data and Results for "Four-of-a-kind? Comprehensive atmospheric characterisation of the HR~8799 planets with VLTI/GRAVITY"
<p><strong>Four-of-a-kind? Comprehensive atmospheric characterisation of the HR~8799 planets with VLTI/GRAVITY</strong></p> <p>We explored the atmospheres of the HR 8799 system of directly imaged planets using petitRADTRANS atmospheric retrievals. This required reprocessing existing data, preparing new observations from VLTI/GRAVITY and combining additional archival spectroscopic and photometric datasets. We ran 95 retrievals across the four planets, and include here the full set of outputs from the sampling process, as well as synthesised results for each retrieval. Finally, we also include templates for running these retrievals.</p> <p>This data, together with the results of the retrievals and templates for running similar emission spectra retrievals are included in this Zenodo archive. Additional README files are included throughout the archive in order to explain file structures, units, and to provide original references where necessary. An archived repository of the petitRADTRANS code can be found at <span><a href="https://doi.org/10.5281/zenodo.10892021">https://doi.org/10.5281/zenodo.10892021</a>,</span> while the up-to-date repository is available at <span><a href="https://gitlab.com/mauricemolli/petitRADTRANS">https://gitlab.com/mauricemolli/petitRADTRANS</a>.</span></p>
Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves -> Model outputs
<p>This repository contains MITgcm outputs associated with the paper "Chlorophyll production in the Amundsen Sea boosts heat flux to atmosphere and weakens heat flux to ice shelves", submitted to the Journal of Geophysical Research: Oceans. The outputs are presented in netCDF format and come from two simulations - <em><strong>GREEN</strong></em>, with chlorophyll, and <em><strong>BLUE</strong></em>, without chlorophyll affecting shortwave heating.</p> <ul> <li>Temperature and salinity <ul> <li>green_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>green_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>green_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> <li>blue_temp.nc <ul> <li>3-dimensional monthly fields of temperature in the <em><strong>BLUE</strong><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C</li> </ul> </li> <li>blue_salt.nc <ul> <li>3-dimensional monthly fields of salinity in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: g/kg</li> </ul> </li> <li>blue_ohc.nc <ul> <li>3-dimensional monthly fields of ocean heat content trend in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: deg.C/day</li> </ul> </li> </ul> </li> <li>Sea ice <ul> <li>ice_green.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>GREEN</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li>sit_green.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> <li>ice_blue.nc <ul> <li>2-dimensional monthly fields of sea ice concentration in the <em><strong>BLUE </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>range: 0 -> 1</li> </ul> </li> <li> sit_blue.nc <ul> <li>2-dimensional monthly fields of sea ice effective thickness in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> <li>Ice shelves <ul> <li>green_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> <li>blue_meltrate.nc <ul> <li>2-dimensional monthly fields of ice shelf basal melt in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: kg/m^2/s</li> </ul> </li> </ul> </li> <li>Surface heat fluxes <ul> <li>tflux_green.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_green.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_green.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>GREEN </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_green.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_green.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>tflux_blue.nc <ul> <li>2-dimensional monthly fields of total downward surface heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>latent_blue.nc <ul> <li>2-dimensional monthly fields of downward latent heat flux in the <em><strong>BLUE</strong></em><em><strong> </strong></em>experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>sensible_blue.nc <ul> <li>2-dimensional monthly fields of downward sensible heat flux in the <em><strong>BLUE </strong></em>experiment </li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>longwave_blue.nc <ul> <li>2-dimensional monthly fields of upward longwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> <li>shortwave_blue.nc <ul> <li>2-dimensional monthly fields of upward shortwave heat flux in the <em><strong>BLUE</strong></em> experiment</li> <li>01.01.2008 -> 31.12.2014</li> <li>units: W/m^2</li> </ul> </li> </ul> </li> <li>BLING <ul> <li>chlorophyll.nc <ul> <li>2-dimensional monthly fields of surface chlorophyll in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: mg/m^3</li> </ul> </li> <li>euphotic_depth.nc <ul> <li>2-dimensional monthly fields of euphotic depth in the <em><strong>GREEN</strong></em> experiment</li> <li>01.01.2003 -> 31.12.2014</li> <li>units: m</li> </ul> </li> </ul> </li> </ul>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers"
<p>Dataset of the paper "Response of Sea Surface Temperature to Atmospheric Rivers", whose manuscript will be submitted by 10/25/2023</p> <p><br>The dataset contains the necessary data to generate the figures in the paper with the code in the link <a href="https://doi.org/10.5281/zenodo.10958491">https://doi.org/10.5281/zenodo.10958491</a> whose Github reference is <a href="https://github.com/meteorologytoday/paperfigures-2023-AR-SST-response">https://github.com/meteorologytoday/paperfigures-2024-AR-SST-response</a></p> <p> </p>
Model Datafiles for Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice
<p>Datafiles produced by the 1-D photochemistry model used to create the publication "Observed seasonal changes in Martian hydrogen chloride consistent with heterogeneous chemistry on atmospheric dust and ice" - Taysum et al. 2024.</p> <p> </p> <p>matching_orbits_v2.txt : Lists the orbital parameters and water vapour / aerosol file names corresponding to each of the 77 ACS MIR HCl observations that we study.</p> <p> </p> <p><strong>MCD_GlobaMaps.zip</strong> : .nc files containing model output where the model is ran at Longitude 0 degrees, across Ls 0 --> 360 in intervals of 30 degrees, and latitudes 60 S to 60 N in intervals of 15 degrees.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li> </li> </ul> </li> </ul> <p><strong>ACS_Comparisons.zip </strong>: .nc files containing the model output for specific ACS MIR HCl observations in Mars Year 34.</p> <ul> <li>LowTau : model runs with the chlorine heterogeneous chemistry scheme active. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, and TGO measured water ice, dust, and H2O vapor profiles, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> <li>No_Chlorine : model runs with no chlorine tracers in the model chemistry. <ul> <li>MY34/ : Runs driven with MY34 climatology from the MCDv6.1</li> <li>StandardClim/ : Runs driven with Standard Climatology from the MCDv6.1</li> <li>TGO/ : Runs driven with MY34 climatology, driven at the position of the ACS-TIRVIM aerosol retrievals corresponding to the approximately colocated ACS MIR HCl observation</li> </ul> </li> </ul>
Dataset Mayen et al_Temporal variations of water carbon and atmospheric carbon dioxide fluxes in a temperate salt marsh and influence of aquatic metabolism
Open the record for dataset details and reuse information.
Data from: Global Fjords Are Minor Sources Of Nitrous Oxide To The Atmosphere
<ol> <li>The study sites included six different fjords located in an area spanning from 56.6˚N to 66.6˚N and from −39.0˚W to 13.7˚E.</li> <li>All data were collected throughout five different cruises from April to July 2023, with instruments installed on R/V Skagerak (University of Gothenburg). N2O dissolved in the surface water was measured continuously via Cavity Enhanced Absorption Spectroscopy using a LI-7820 N2O/H2O trace gas analyzer (LI-COR Biosciences). The LI-7820 produced high-precision N2O (ppb) measurement data every second, response time of 0 to 330 ppb ≤ 2 seconds and maximum drift of < 1 ppb per 24-hour period.</li> <li> Nitrous oxide measurements (>1800 in each fjord) were integrated over 30 min and adjusted to account for gas exchange equilibration over the closed loop and for the ~10 min time lag (delayed response time of ~5 min from the exchanger to the gas detector and additional lag of ~5 min from sea to exchanger).The ship was equipped with a -4H-FerryBox (JENA Engeneering GmbH, Germany), an automatic flow-through system with various sensors measuring hydrographic and biochemical parameters such as <u>s</u>alinity, temperature, dissolved oxygen (O2), pH, chlorophyll, and turbidity (-4H-JENA engineering GmbH, n.d.). All potentiometric FerryBox pH (EGA150, Meinsberg) measurements on the NBS scale were corrected by a constant offset (ΔpH = -0.451 ±0.016) based on the concurrent spectrophotometric pH measurements (Müller et al., 2018) during the Greenlandic and Icelandic cruises. Water was pumped through a subsurface-inlet and circulates with a speed of 1 m<u> </u>s−1 in the system. Bubbles and particles are removed by a debubbling unit (Ferry- Box Task Team, n.d.). Windspeed (m s−1) measurements were recorded with an onboard sonic anemometer (Airmar PB200) mounted approximately 19 meters above the water line and then logarithmically corrected to 10 meters above water line.</li> <li>Additionally, water samples for dissolved nitrates and nitrites (NOx-) and ammonium (NH4+) concentrations were collected along the survey transects using Niskin-rosette bottles from a depth of ~3 m. Dissolved nutrient samples were collected by filtering sample water through cellulose acetate filters (0.45 μm) into pre-rinsed 12 mL polypropylene vials before immediate freezing until laboratory analysis. Nutrient samples for dissolved NH4+, NO3− and NO2−<u> </u>were filtered through pre-rinsed cellulose acetate filters (0.45 μm, Sartorius) and frozen at −20 °C until segmented flow analysis (QuAAtro, XY-3 Sampler, Seal Analytical 2015; detection limits and precisions 0.2 μM and 7% for NH4+, 0.05 μM and 7% for NO3− and 0.02 μM and 7% for NO2−).</li> <li>N2O saturation values were calculated as the ratio between concentrations of dissolved N2O in seawater and the corresponding computed concentrations in the atmosphere (Walter et al., 2004). Diffusive sea-air fluxes (f) were measured according to the formula: " f=(<em>p_wate</em>r- <em>p_air</em> ) α k " where <em>p_water</em> is the partial pressure of N2O in the surface water layers calculated after correcting for the water vapor partial pressure in the equilibrated headspace and local atmospheric pressure. <em>p_air</em> is the partial pressure of N2O in air. For pair the global values from NOAA were used (Lan, 2024). Both partial pressures are expressed in natm. α is the solubility coefficient that was calculated from temperature and salinity using equations of Weiss and Price (1980). <em>k</em> is the gas transfer velocity calculated from the wind-based empirical model by (Wanninkhof, 2014). The k values were calculated according to the formula: "k = 0.251 <em>U</em>^2 〖(<em>Sc</em>/660)〗^(-0.5)" where <em>U </em>is the wind speed. We used in situ daily averaged wind speed measurements. <em>Sc</em> is the Schmidt number, which is water kinematic viscosity divided by the molecular diffusion coefficient of N2O (Wanninkhof, 2014). We used 4H–FerryBox temperature observations from the same water line as for the continuous N2O measurements. N2O fluxes at the sea-air interface are expressed in µg N2O m−2 day−1.</li> </ol> <p> </p> <p>Refeferences:</p> <p>Lan, X., Thoning, K.W., Dlugokencky, E.J.:. (2024). Trends in globally-averaged CH4, N2O, and SF6 determined from NOAA Global Monitoring Laboratory measurements. Version 2024-10 <u><a>https://doi.org/</a></u> <u><a>https://doi.org/10.15138/P8XG-AA10</a></u></p> <p>Müller, J. D., Schneider, B., Aßmann, S., & Rehder, G. (2018). Spectrophotometric pH measurements in the presence of dissolved organic matter and hydrogen sulfide. Limnology and Oceanography: Methods, 16(2), 68-82.</p> <p>Wanninkhof, R. (2014). Relationship between wind speed and gas exchange over the ocean revisited. Limnology and Oceanography: Methods, 12(6), 351-362.</p> <p>Walter, S., Bange, H. W., & Wallace, D. W. (2004). Nitrous oxide in the surface layer of the tropical North Atlantic Ocean along a west to east transect. Geophysical Research Letters, 31(23).</p> <p>Weiss, R., & Price, B. (1980). Nitrous oxide solubility in water and seawater. Marine chemistry, 8(4), 347-359.</p> <p> </p>
Data archive for the peer-reviewed journal article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions"
<p>This data archive accompanies the article "Online measurements during simulated atmospheric aging track the strongly increasing oxidative potential of complex combustion aerosols relative to their primary emissions", which was accepted in November 2024 in the peer-reviewed journal Environmental Science and Technology Letters. The data archive contains the processed OP_DTT, PM loading, oxidant level, and elemental ratio measurements presented in this journal article. </p>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
Dataset for "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al.
<p>The dataset used in the paper "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al. The doi of the preprint of the paper is https://doi.org/10.5194/egusphere-2024-654. The dataset contains the diurnal cycles of delta_atm(18O), delta(O2/N2), y(CO2), and delta(Ar/N2) at Tsukuba, Japan averaged for the period 2013-2022 ("data_for_Fig4.csv"), the rolling average values of delta_atm(18O) and delta(O2/N2) anomalies for 300 data at TKB during August and February, 2017 ("data_for_Fig6.csv"), and the monthly mean values of delta_atm(18O) and the delta(O2/N2) at TKB for the period 2013-2022("data_for_Fig7.csv"). The raw data before averaging are also presented ("raw_data_2013.csv" to "raw_data_2022.csv"). See text of Ishidoya et al. in more details. </p> <p> </p>
Dataset for "A low-cost and low-power continuous measurement system for atmospheric carbonyl sulfide concentration"
<p>The dataset used in the manuscript "A low-power continuous measurement system for atmospheric carbonyl sulfide concentration" by Kamezaki et al. The dataset contains COS concentrations at Tsukuba in Japan. Additionally, the data used in the paper are also included. Raw data are available upon request from the author.</p>
A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images
<p>We have constructed a SAR (Synthetic Aperture Radar) image semantic segmentation dataset that includes 12 oceanic and atmospheric phenomena: Atmospheric Front (AF), Oceanic Front (OF), Rainfall (RF), Iceberg (IC), Sea Ice (SI), Pure Ocean Wave (POW), Wind Streak (WS), Low Wind Area (LWA), Biological Slick (BS), Micro Convective Cells (MCC), Internal Wave (IW), and Eddy.</p> <p>This dataset is built using Sentinel-1 IW and WV mode images. For WV mode data, we referenced TenGeoP-SARwv and SAR_WV_SemanticSegmentation and selected 2,383 images for semantic segmentation and annotation. For IW mode images, we incorporated some images from Tao et al.'s internal wave detection dataset. We selected 484 Sentinel-1 IW mode images obtained from 2015 to 2022 and divided them into 2,628 sub-images.</p> <p>The dataset contains a total of 5,011 image slices, with approximately 400 images for each phenomenon. All images are 16-bit .tiff files with a resolution of 100m and a size of 256x256 pixels. The images were manually annotated using the Labelme software, generating corresponding JSON files, which were then used to create the related annotation .png files.</p> <p>The updated version(V2) provides geographic information for each image.</p> <p>Thank you for your interest in our dataset. Here are the meanings of each label:</p> <p>1. BG: The unlabelled parts in JSON files are "BG" (Background)<br>2. AF: Atmospheric Front<br>3. BS: Biological Slick<br>4. I: “I” is equivalent to “IB”, representing icebergs<br>5. LWA: Low Wind Area<br>6. MCC: Micro Convective Cells<br>7. OF: Oceanic Front<br>8. POW: Pure Ocean Wave<br>9. RC: “RC” (Rain Cells) is equivalent to “RF” (Rainfall), both representing the rainfall phenomenon in the SAR image. <br>10. SI: Sea Ice<br>11. WS: Wind Streak<br>12. Eddy<br>13. IW: Internal Wave<br><em>14. HM: Represents the artificial objects appearing in the image, such as ships, aquaculture floating rafts, wind power facilities, etc.</em><br><em>15. OS: Unlike “BS”,“OS” represents mineral oil spills appearing in the SAR image (currently, there is insufficient data available for training, which will be supplemented in the future).</em></p>
Code and Data for Li et al. Characterizing the Speed of Chemical Cycling in the Atmosphere
<p>This Zenodo archive contains the code and data used in the manuscript "Characterizing the Speed of Chemical Cycling in the Atmosphere"</p>
Archived Model Output and Code for "Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations "
<div> <p>This is an archive of CAM6 simulation output used in the paper Marine Boundary Layer Cloud Condensation Nuclei Bias over the Southern Ocean: Comparisons between the Community Atmosphere Model 6 and Field Observations, submitted to the AGU Journal. Codes used to read the nc file is also attached.</p> </div>
Code and data for Porting the Meso-NH Atmospheric Model on Different GPU Architectures for the Next Generation of Supercomputers (version MESONH-v55-OpenACC)
<p>GeometricMG.pdf (source: https://bitbucket.org/em459/tensorproductmultigrid/src/master/Documentation/)<br>MESONH_Bench_HECTOR_ADASTRA_LEONARDO.tar.gz: code and data for Meso-NH bench<br>Performance.zip: code and data for figures related to performance<br>WeatherApplications.zip: namelists for running weather applications<br>OASIS3_WW3.tar.gz: OASIS and WW3 codes for running the Meso-NH WWW3 coupled simulation</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.