Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,574
datasets available to search
ShareScore release 0.9.0
Dataset results
1,574 results for “atmospheres”
Latitudinal Asymmetry in the Dayside Atmosphere of WASP-43b
<p>This repository contains data inputs and analysis products for the manuscript "Latitudinal Asymmetry in the Dayside Atmosphere of WASP-43b" (Challener et al., 2024) accepted for publication in The Astrophysical Journal Letters. The archive contains a README with further description of the included files and an example of how to make use of them. If you make use of these data in your work, please cite our paper: <a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240610207C/abstract">https://ui.adsabs.harvard.edu/abs/2024arXiv240610207C/abstract</a></p>
Output Dataset from "Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks"
<p>Output dataset of stellar parameters and associated uncertainties derived by the machine learning approach, as decribed in <a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener"><em>"Stellar Atmospheric Parameters From Gaia BP/RP Spectra using Uncertain Neural Networks", Fallows & Sanders</em> (</a><em><a href="https://doi.org/10.1093/mnras/stae1303" target="_blank" rel="noopener">2024)</a>. </em></p> <p>Included parameters: [Fe/H] (fe_h), effective temperature (teff), surface gravity (logg), [C/Fe] (c_fe), [N/Fe] (n_fe), and [a/M] (a_m). Gaia DR3 source ids are included for matching purposes, alongside Gaia bp_rp_excess_factor and ruwe metrics for quality filtering.</p> <p><em>We include predictions for only Gaia objects with radial velocity measurements ('output_0.0_360.0_v2.txt'), and for all* Gaia objects with XP spectra ('gaiaFull_output_0.0_360.0.zip'). Note our full catalogue has a total size of 30.9GB once uncompressed.</em></p> <p> </p> <p><em>* Not truly all objects with XP spectra; stars with spurious or unreliable measurements in our requred inputs (Gaia, 2MASS, WISE) have been removed.</em></p>
Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019.
<p>This dataset provides input data (fluxes, background concentrations, and observations) for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019 using chemical transport models (CTMs). While some components of the dataset are available in other repositories, this compilation serves to 1) streamline the data collection process for other users and 2) bypass the need to perform data aggregation.</p> <p>Here is a description of each dataset:</p> <p><strong>cams73_latest_co2_conc_surface_inst_2019*.nc</strong></p> <p>CO2 mole fractions from the CAMS global inversion-optimised product v20r2 (Chevallier et al., 2010).</p> <p>The data are provided at a resolution of 3.75° in longitude and 1.9° in latitude, with a 3-hourly temporal resolution. </p> <p><strong>monitor_CO2_CIF_2019.nc</strong></p> <div> <div> <div> <div> <div> <div> <p>Observed CO2 atmospheric mixing ratios in Europe, compiled in version V8 of the ICOS GlobalView Obspack (ICOS RI et al., 2023), include continuous measurements from 58 stations across Europe, incorporating both ICOS and non-ICOS facilities.</p> <p>The original dataset has been aggregated and adapted to match the format of the monitor files used in the Community Inversion Framework (CIF; Berchet et al., 2021).</p> </div> </div> </div> </div> </div> </div> <p><strong>EDGARv4.3_BP2021_CO2_EU2_2019.nc</strong></p> <p>Anthropogenic CO2 fluxes (European, hourly) obtained from EDGAR-v4.2 and BP.</p> <p>The anthropogenic CO2 emissions are based on the spatial distribution from the EDGAR-v4.2 inventory, national and annual budgets from British Petroleum (BP) statistics, and hourly temporal profiles derived using the COFFEE approach (Steinbach et al., 2011, available on the ICOS Carbon Portal). This data is provided at a 0.1° × 0.1° horizontal resolution and hourly temporal resolution.</p> <p><strong>FG2.TRENDY11.ORC3.S3.3H_NBP_resp_2019.nc</strong></p> <p>NBP CO2 fluxes (global, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-TRENDY simulation is conducted as part of the TRENDY model intercomparison project (e.g., Sitch et al., 2015; Friedlingstein et al., 2022). This simulation uses inputs provided by the project, including the CRUERA atmospheric climate forcing (global, 6-hourly, 0.5-degree resolution), LUH2 land-use change dataset, global atmospheric CO2 concentration data, and nitrogen fertilizer input datasets. All TRENDY simulations adhere to a standardized protocol: a model spin-up phase using recycled forcing data from 1901-1920, with other inputs from 1700, continues until the model's carbon pools reach equilibrium (340 years of spin-up for ORCHIDEE). This is followed by a transient simulation from 1700-1900, varying CO2 and land-use data while recycling climate forcing, and a historical simulation from 1901-2020 with all data inputs varied.</p> <p><strong>FR2.ORC3v7267.CRUERA3.NBP_3H.2019.nc</strong></p> <p>NBP CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-VERIFY simulation is performed as part of the VERIFY project over the European region. This simulation is driven by the CRUERA dataset, which is derived from the ERA5-Land dataset (originally global, 1-hourly, at 0.1-degree resolution), transformed to the VERIFY region of interest (35°N to 73°N, 25°W to 45°E, 3-hourly, at 0.125-degree resolution), and re-aligned with the CRU observation dataset (for air temperature, shortwave radiation, humidity, and precipitation). The Hilda+ dataset is used for land use, and the EMEP model outputs are used for nitrogen inputs. The VERIFY simulation follows the general protocol used in the TRENDY project.</p> <p><strong>FR2.ORC3v7267.CRUERA3.hetero_resp_3H.2019.nc</strong></p> <p>Heterotrophic respiration CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations as described in the previous section.</p> <p><strong>Becker_coastal_fluxes_RF_v2021_2_2019.nc</strong></p> <p>Ocean CO2 fluxes (Europe, daily). </p> <p>The ocean fluxes come from a hybrid product combining the University of Bergen coastal ocean flux estimate and the Rödenbeck global ocean estimate (Rödenbeck et al., 2014). This data is provided at a 0.125° × 0.125° horizontal resolution and at a daily temporal resolution.</p> <p> </p> <p><em><strong>References</strong></em> </p> <p> </p> <p>Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. v., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, https://doi.org/10.5281/zenodo.6304912, 2022</p> <p>Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2010JD013887, 2010</p> <p>Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P.,Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora,V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, O., Harris, I., Hefner, M., Houghton, R. A.,Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken,J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M.,Munro, D. R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L.,Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf,G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth System Science Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022,https://essd.copernicus.org/articles/14/4811/2022/, publisher: Copernicus GmbH, 2022</p> <p>ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O’Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023;<br>obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, https://doi.org/10.18160/CEC4-CAGK, 2023</p> <p>Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014</p> <p>Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford,C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G.,Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://bg.copernicus.org/articles/12/653/2015/, publisher: Copernicus GmbH, 2015.</p> <p>Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmospheric Chemistry and Physics, 11, 6855–6870,1160 https://doi.org/10.5194/acp-11-6855-2011, 2011</p> <p> </p> <p> </p> <p> </p>
Figures: 'Preliminary Sizing of High-Altitude Airships Featuring Atmospheric Ionic Thrusters: An Initial Feasibility Assessment'
<p><strong>Figures from the publication <em>Preliminary Sizing of High-Altitude Airships Featuring </em><em>Atmospheric Ionic Thrusters: An Initial Feasibility Assessment</em></strong></p> <p>*.fig files can be opened in <code>Matlab</code></p>
Dataset for "Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS"
<p>Dataset for the publication: Notø and Holzinger (2024), “Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS”, <br><a title="Atmospheric CFC-11 and CCl4: a Free Calibration Standard for PTR-MS" href="https://doi.org/10.1016/j.ijms.2024.117311">https://doi.org/10.1016/j.ijms.2024.117311</a></p> <p>The data consists of raw data files of measurements and the processing code to calculate pseudo reaction rate constants of CFC-11 and CCl4 with H3O+.</p>
Radiative-transfer dataset for "Distilling machine learning's added value: Pareto fronts in atmospheric applications"
<p>This dataset goes with the journal paper "Distilling machine learning's added value: Pareto fronts in atmospheric applications" by T. Beucler, A. Grundner, S. Shamekh, P. Ukkonen, M. Chantry, and R. Lagerquist.</p> <p>Subdirectory "training" contains unnormalized (in physical units) training data. Subdirectories "validation" and "testing" contain unnormalized validation and testing data. Subdirectory "training/for_pareto_paper_2024/simple" contains training data from the simple (clear-sky) dataset discussed in the paper; subdirectory "training/for_pareto_paper_2024/complex" contains training data from the complex (multi-cloud) dataset discussed in the paper. Subdirectories "validation/for_pareto_paper_2024/simple" and "validation/for_pareto_paper_2024/complex" are analogous but for the validation data; subdirectories "testing/for_pareto_paper_2024/simple" and "testing/for_pareto_paper_2024/complex" are analogous but for the testing data.</p> <p>Subdirectories beginning with "normalized_predictors" -- "normalized_predictors/training", "normalized_predictors/validation", "normalized_predictors/testing", "normalized_predictors/training/for_pareto_paper_2024/simple", "normalized_predictors/training/for_pareto_paper_2024/complex", etc. -- are analogous to the above but containing normalized predictors (in z-scores rather than physical units).</p> <p>Every file -- after unzipping, so that the extension is ".nc" rather than ".nc.gz" -- can be read by `example_io.read_file` in the ml4rt library (https://github.com/thunderhoser/ml4rt).</p>
A Site Atmospheric State Best Estimate of Temperature for Lauder, New Zealand (1997-2012)
<p>A Site Atmospheric State Best Estimate (SASBE) of the temperature profile above the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) site at Lauder, New Zealand, has been developed. Data from multiple sources are combined within the SASBE to generate a high temporal resolution data set that includes an estimate of the uncertainty on every value. The SASBE has been developed to enhance the value of measurements made at the distributed GRUAN site at Lauder and Invercargill (about 180 km apart), and to demonstrate a methodology which can be adapted to other distributed sites.</p> <p>Within GRUAN, a distributed site consists of a cluster of instruments at different locations.<br> The temperature SASBE combines measurements from radiosondes and automatic weather stations at Lauder and Invercargill, and ERA5 reanalysis, which is used to calculate a diurnal temperature cycle to which the SASBE converges in the absence of any measurements.<br> The SASBE provides hourly temperature profiles at 16 pressure levels between the surface and 10 hPa for the years 1997 to 2012. Every temperature value has an associated uncertainty which is calculated by propagating the measurement uncertainties, the ERA5 ensemble SDs, and the ERA5 representativeness uncertainty through the retrieval chain.</p> <p>This best-estimate temperature data product for Lauder is expected to be valuable for satellite and model validation as measurements of atmospheric essential climate variables are sparse in the Southern Hemisphere.</p> <p>A publication describing the data product is submitted to Earth System Science Data Discussions. The title of the publication is: Combining Data from the Distributed GRUAN Site<br> Lauder-Invercargill, New Zealand, to Provide a Site Atmospheric State Best Estimate of Temperature.</p> <p> </p> <p> </p>
Atmospheric inversion results: sources of atmospheric halocarbons in the Eastern Mediterranean
<p>Settings, run script, observations and results from all inverse modelling runs used in Schönenberger et al., APC, 2018, for European sources of halocarbons for the year 2013.</p> <p>Settings and run script can be found in <a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/inversion_run_scripts.tar.gz">inversion_run_scripts.tar.gz</a>. In order to reprocess the results the R package 'Rinversion' for atmospheric inversion (<a href="https://doi.org/10.5281/zenodo.1194641">https://doi.org/10.5281/zenodo.1194641</a>) has to be installed and the simulated source sensitivities (<a href="https://doi.org/10.5281/zenodo.1194037">https://doi.org/10.5281/zenodo.1194037</a>) have to be downloaded. Observations (<a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/halocarbon_observations.tar.gz">halocarbon_observations.tar.gz</a>) are post-processed observations of the AGAGE network (<a href="http://agage.mit.edu/">http://agage.mit.edu/</a>) plus those gathered at the Finokalia site (<a href="https://doi.org/10.5281/zenodo.1186221">https://doi.org/10.5281/zenodo.1186221</a>).</p> <p>Inversion results (<a href="https://zenodo.org/api/files/3adbd30c-fef8-4bf1-9c0d-01bdb1c9a40e/inversion_results.tar">inversion_results.tar</a>) are contained as separate packages for each individual sensitivity experiment described in the publication. The sensitivity experiments are listed in inversion_results/InversionRuns.xlsx and agree with those listed in Table 1 of the publication. Subfolders are organised by halocarbon species: HCFC_142b, HCFC_22, HFC_125, HFC_152a, HFC_134, HFC_143a. For each halocarbon results are stored in form of plots, ASCII spread sheets (csv, dat) or R data objects (.rda). Time series and distribution plots are available in separate subfolders. Intermediate data used by the inversion code are stored in 'intermediates', whereas the final results of each inversion run are stored as a single R object (inversion_results_XXX.rda).</p> <p> </p> <p> </p>
Data for: "Simulation of uranium plasma plume dynamics in atmospheric oxygen produced via femtosecond laser ablation"
<p>Data generated by 2D reactive, compressible, multi-species fluid model of uranium femtosecond laser ablation in an atmospheric oxygen environment. The dataset consists of a series of plain text tabular data files recorded at several time points during the simulation run. The data files are numbered according to the simulation time in nanoseconds (FFF-0100.txt is the data at 100 ns) and are given at intervals of 50 ns for the first 500 ns of simulation time, and every 100 ns thereafter, up to the total simulation time of 10000 ns. The initial conditions are provided in the first data file (FFF-0000.txt). Each data file contains spatially-resolved values of the fluid moments along with the molar concentrations of each species considered in the model (total of 30 species), given in a column format delimited by spaces.</p> <p>For details on the model implementation and simulation conditions, please refer to the associated manuscript.</p>
Data supporting the conclusions of Atmospheric boundary layer classification with Doppler lidar
<p>This is data set includes Doppler wind lidar quantities which were calculated from Halo Photonics Streamline measurements between 2 September 2015 and 16 November 2016 at Hyytiälä, Finland and between 1 January 2015 and 31 December 2016 at Jũlich, Germany. The data set also includes the respective boundary layer classification results generated from the calculated lidar quantities from both of the sites.</p>
Look Up Tables for removing background atmospherical signal in visible satellite imagery
<p>Look Up Tables for removing the background atmospherical signal due to Rayleigh scattering of molecules, absorption by atmospheric gases and aerosols, and Mie scattering of aerosols in satellite imagery utilising channels in the visible spectral range</p> <p>Derived from LibRadTran simulations for various standard atmospheres and various aerosol profiles.</p>
Atmospheric_river_precipitation_predictability_data
<p>Data for the manuscript entitled "Predictability of Extreme Precipitation Associated With Atmospheric Rivers in Western U.S. Watersheds".</p> <p> </p> <p>It includes daily precipitation data from WRF and PRISM. Also includes atmospheric river information derived from ARTMIP Tier 1 archive.</p> <p> </p> <p>The tools used to generate the figures in the paper is at: <a href="https://github.com/lucas-uw/Chen-2018-GRL">https://github.com/lucas-uw/Chen-2018-GRL</a></p> <p> </p> <p>If you use this dataset, please cite the following paper:</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. Geophysical Research Letters, 45, 11,693–11,701. <a href="http://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. Journal of Geophysical Research: Atmospheres, <a href="http://doi.org/10.1029/2019JD03468">https://doi.org/10.1029/2019JD03468</a></p>
Data from: Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events
<p>This data is related to the seismic hammer experiment discussed in "Numerical Simulation of the Atmospheric Signature of Artificial and Natural Seismic Events" by Martire et al. (2018, DOI will be added upon acceptance of the manuscript).</p> <p>The .zip file contains 3 .mseed files, and 1 .txt file. The .mseed are the raw seismometer signals. The .txt details the position of the sensor.</p> <p>Remaining data used in our paper can be found in the repository related to "Detection of Artificially Generated Seismic Signals using Balloon-borne Infrasound Sensor" by Krishnamoorthy et al. (2018, DOI 10.1002/2018GL077481). That repository has DOI 10.6084/m9.figshare.6137507.</p>
Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications
<p>This repository contains all data for the manuscript by Rastogi et al., titled "<strong>Large uptake of atmospheric OCS observed at a moist old growth forest: Controls and implications for carbon cycle applications</strong>". This manuscript has been accepted for publication in the Journal of Geophysical Research: Biogeosciences</p> <p> </p>
Data set used in the Article "Evaluation of Monte Carlo tools for high-energy atmospheric physics II: relativistic runaway electron avalanches"
<p>Data used for the Relativistic Runaway Electron Avalanches (RREA) simulations of the Article : "Evaluation of Monte Carlo tools for high energy atmospheric physics II: relativistic runaway electron avalanches" by D. Sarria et al.</p> <p>Includes two set of results : The Probability of Generating RREAs, and the Characterizations of RREAs.</p> <p>Link to the article : <a href="https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html">https://www.geosci-model-dev.net/11/4515/2018/gmd-11-4515-2018.html</a></p> <p>DOI of the article: 10.5194/gmd-2018-119</p>
A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration
<p>Selected model output data for supporting this paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the Jablonowski-Williamson Baroclinic Wave</p> <p> </p> <p>see https://doi.org/10.5281/zenodo.3544795 for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</p>
More atmospheric model fields shown in paper titled "E3SMv0-HiLAT: A Modified Climate System Model Targeted for the Study of High Latitude Processes
<p>These files contain atmospheric climatology, averaged over years 234-253 of the E3SMv0-HiLAT model preindustrial simulation, as generated by the CESM diagnostic package. Files are in netcdf format (subsequently compressed), with fields described within those files.</p>
MAJA look-up tables for Sentinel-2 A&B sensors, for Copernicus Atmosphere Monitoring Service aerosol types
<p>The archive contains the Look-up tables used by MAJA atmospheric correction software, used to process Sentinel-2 A&B sensors. These look-up tables correspond to the aerosol types used by Copernicus Atmosphere Monitoring Service (CAMS). However, the default continental model is also provided.</p> <p>Version 1.1 has new LUT for water vapour estimates, which corrects for a bias observed for large water vapour contents (above 2.5 g/cm2)</p> <p>Version 1.2 just changed the Folder name for a better integration with Start_maja.</p> <p>Version 1.3 added the Header files</p>
Global nitrous oxide fluxes estimated using atmospheric inversions
<p>Nitrous oxide emissions are presented from three independent atmospheric inversion frameworks. The frameworks are: 1) INVICAT: an inversion using the atmospheric transport model, TOMCAT and a 4D-Var optimisation method; 2) JAMSTEC: an inversion using the MIROC4-ACTM atmospheric transport model and a Bayesian analytical optimisation method; and 3) PYVAR: an inversion using the LMDZ5 atmospheric transport model and a 4D-var optimisation method. The emissions were optimised monthly and have been re-gridded from the model native resolution to 1.0 by 1.0 degrees. The files for TOMCAT and LMDZ5 (i.e. the inversion frameworks INVICAT and PYVAR, respectively) contain two flux variables: 1) the prior fluxes as estimated a priori, and 2) the posterior fluxes as estimated by the inversion. The file for the JAMSTEC inversion, contains five flux variables: 1) flux_apri_land: the prior fluxes over land, 2) flux_apri_ocean: the prior fluxes over ocean, 3) flux_apri_fossil: the prior estimate of emissions from combustion, 4) flux_apos_land: posterior fluxes over land estimated by the inversion, and 5) flux_apos_ocean: the posterior fluxes over ocean estimated by the inversion. Note that flux_apri_fossil was not optimised in the inversion but for the total posterior N<sub>2</sub>O emission, needs to be added to the flux_apos_ocean and flux_apos_land variables.</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </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.