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679 results for “Ozone”
Atmospheric ozone data from Soddie, 2005 - 2017.
This is a summary of hourly ozone concentrations measured at a height of ~2.5 on a tower in the Soddie meadow. The focus was on the winter, but both the sampling frequency and the length of sampling during a year varied.
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
<p>The data provided in this repository relates to a paper on ozone radiative forcing submitted for publication in Atmos. Chem. Phys., as part of the TOAR-II special issue (<a href="https://acp.copernicus.org/articles/special_issue1256.html">ACP – Special issue – Tropospheric Ozone Assessment Report Phase II (TOAR-II) Community Special Issue (ACP/AMT/BG/GMD inter-journal SI)</a>). The paper is entitled "<span>Climate Forcing due to Future Ozone Changes</span><span>: An intercomparison of metrics and methods" by authors <span><span>William J. Collins</span></span><span><span>,</span> <span>Fiona M. O’Connor</span></span><span><span>, </span><span>Connor R. Barker</span></span><span><span>, </span><span>Rachael E. Byrom</span></span><span><span>, </span><span>Sebastian D. Eastham</span></span><span><span>,</span> <span>Øivind Hodnebrog</span></span><span><span>, Patrick Jöckel</span></span><span><span>, </span><span>Eloise A. Marais</span></span><span><span>, </span><span>Mariano Mertens</span></span><span><span>, Gunnar Myhre</span></span><span><span>, Matthias Nützel</span></span><span><span>, Dirk Olivié</span></span><span><span>, Ragnhild </span><span>Bieltvedt</span><span> Skeie</span></span><span><span>5</span></span><span><span>, Laura Stecher</span></span><span><span>, Larry W. Horowitz</span></span><span><span>, Vaishali Naik</span></span><span><span>, Gregory Faluvegi</span></span><span><span>, Ulas Im</span></span><span><span>, Lee T. Murray</span></span><span><span>, Drew Shindell</span></span><span><span>, Kostas Tsigaridis</span></span><span><span>, Nathan Luke Abraham</span></span><span><span>, James Keeble.</span></span></span></p>
ML-TOMCAT V2.0: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset
<p>MLTOMCAT V2 is 46 years (1979-2024) of gap free ozone profile data sets that is created by correcting biases in a TOMCAT Chemical Transport Model (CTM) simulated ozone profiles. We use Random Forest regression model to correct model biases. </p> <p>Each file contain monthly mean zonal mean ozone profiles. There are 6 data files.</p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_vmr_V2.nc</a> contains ozone profiles on geometric height levels (1 to 60 km) in mixing ratio units, whereas <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_ht_nd_V2.nc</a> contains ozone profile in number density units.</p> <p>Similarly, </p> <p><a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_vmr_V2.nc</a> contains ozone profiles on 43 MLS pressure levels (1000 to 0.1 hPa) in mixing ratio units, whereas <a href="https://zenodo.org/api/files/0416f9bd-c908-4d3f-9368-47ca2e04d7bd/MLTOMCAT_1979_2020_72_ht_vmr.nc">MLTOMCAT_1979_2024_72_plev_nd_V2.nc</a> contains ozone profile in number density units.</p> <p>Please note that data below 300 hPa (~8km) and 1 hPa (~50 km) should be used with caution.</p> <p>There are two straospheric column files</p> <p>ML-TOMCAT-SCO_120ppb_boundary_V2_197901-202412.nc and</p> <p>ML-TOMCAT-SCO_150ppb_boundary_V2_197901-202412.nc</p> <p>Stratospheric column files calculated using 120 ppb and 150 ppb as a chemical ozone boundaries.</p> <p>A manuscript describing MLTOMCAT would be published in EESD (Dhomse et al., 2021).</p>
BNNOz - Infilled vertically resolved ozone dataset
<p>This vertical ozone dataset is a fusion of an existing ozone dataset (<a href="http://www.bodekerscientific.com/data/monthly-mean-global-vertically-resolved-ozone">Bodeker Scientific</a>) with chemistry-climate model output from the Chemistry-Climate modelling initiative.</p> <p>The vertically and latitudinally resolved ozone dataset (zmo3_BNNOz.nc) has been produced by fusing the above data within a <a href="https://proceedings.neurips.cc/paper/2020/file/0d5501edb21a59a43435efa67f200828-Paper.pdf">Bayesian neural network</a>.</p> <p>More information about this processing and the data can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>In addition to the output product we include the training dataset of observed and modelled ozone as a python pickled dataframe. The code to use this training dataset can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>This data submission supports a manuscript submission to ESSD.</p>
TOMCAT CTM simulated ozone profiles using NRL2, SATIRE and SORCE solar fluxes
<p>Individual file contain TOMCAT CTM simulated ozone profiles from five model simulations analysed in the following publication. Briefly, </p> <p>vmro3_T2Mz_TOMCAT_A_NRL2_2005-2020.nc contain ozone profiles from the control simulation that uses ERA5 dynamical forcing fields and NRL V2 solar fluxes</p> <p>vmro3_T2Mz_TOMCAT_B_SATIRE_2005-2020.nc and vmro3_T2Mz_TOMCAT_C_SORCE_2005-2020.nc contain ozone profiles from a simulations that are similar to the control simulation but with SATIRE and SORCE solar fluxes</p> <p>vmro3_T2Mz_TOMCAT_D_SFix_2005-2020.nc has ozone profiles from simulation that is similar to the control simulation but with fixed solar fluxes, whereas vmro3_T2Mz_TOMCAT_E_DFix_2005-2020.nc also contain ozone profiles from a simulation where model uses annually repeating dynamical fields.</p> <p> </p> <p>Dhomse, S. S., Chipperfield, M. P., Feng, W., Hossaini, R., Mann, G. W., Santee, M. L., and Weber, M.: A Single-Peak-Structured Solar Cycle Signal in Stratospheric Ozone based on Microwave Limb Sounder Observations and Model Simulations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-663, in review, 2021.</p>
OMPS-NPP L2 LP USask Ozone (O3) Vertical Profile swath daily V1.1
<p>The USask OMPS-LP L2 2D Ozone v1.1 product provides ozone profile retrievals performed at the University of Saskatchewan for the central slit of the Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) instrument on the Suomi-NPP satellite. The two-dimensional retrieval algorithm accounts for variation in the along orbital track dimension, retrieving an entire orbit simultaneously instead of treating each image independently. Ozone is retrieved from the thermal tropopause to 59 km on a 1 km grid with a vertical resolution of approximately 2 km.</p> <p>Each granule contains data from the daylight portion of each orbit measured for a full month. Spatial coverage is global (-82 to +82 degrees latitude), and there are about 14.5 orbits per day, each has typically 160 profiles with an along orbital track sampling of 125 km. The files are written using NetCDF4.</p>
Merged SCIAMACHY-OMPS limb ozone time series
<p>This data set contains the time series of merged monthly mean ozone profiles retrieved at the University of Bremen from SCIAMACHY and OMPS-LP limb observations. The merging is performed on deseasonalized anomalies, but the data set contains also the reconstructed number density time series. The data set is longitudinally resolved, with a 5° latitude and 20° longitude resolution, and a vertical grid with 3.3 km spacing. </p>
Life Cycle Impact Assessment method for ozone depletion based on WMO 2022
<p>This dataset provides the most recent <a>characterization factors</a> for ozone depletion based on the latest ozone depletion potentials from the 2022 World Meteorological Organization (WMO) scientific assessment. The dataset is formatted for easy import into life cycle assessment (LCA) software such as Brightway, the Activity Browser, and SimaPro. The characterization factors are available for both 100-year and infinite time horizons.</p> <p>When using the dataset, please cite the folllowing publication:</p> <p>van den Oever, A. E.M., Puricelli, S., Costa, D., Thonemann, N., Lavigne Philippot, M., Messagie, M., Dataset with updated ozone depletion characterization factors for life cycle impact assessment, Data in Brief (in press), 2024, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.111103" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.111103</a></p>
GUV total ozone column and effective cloud transmittance from three Norwegian sites 1995-2019
<p>Total ozone column (TOC) and effective cloud transmittance (eCLT) from GUV-511 in Oslo (Norway), GUV-541 from Andøya/Tromsø (Norway), and GUV-541 from Ny-Ålesund (Svalbard, Norway).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research<br> Collaborative institute: Norwegian Radiation and Nuclear Safety Authority, DSA</p> <p>Method described in: Dahlback, A. (1996), Measurements of biologically effective UV doses, total ozone abundances, and cloud effects with multichannel, moderate bandwidth filter instruments, Appl. Opt. 35, 6514–6521</p> <p>1h average noon-time values, based on data with 1-minute time resolution.</p> <p>TOC retrievals from 305/320 nm channel ratio<br> eCLT retrievals from 340 nm channel </p> <p>Calibrations based on the FARIN2005-campaign and annual site visits with a travelling reference GUV instrument from DSA (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2007JD009731)</p> <p>Funding: NILU - Norwegian Institute for Air Research, Norwegian Environment Agency, Norwegian Ministry of health and Care Services</p>
TCOM-O3: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric ozone profile dataset [1991-2021] constructed using machine-learning
<p>Methodology: TOMCAT simulation is performed at T64L32 resolution for the 2000-2024 time period. Collocated Ozone (O3) profiles are divided in five latitude bins: SH polar (90S-50S), SH mid-lat (70S-20S), tropics (40S-40N), NH mid-lat (20N-70N) and NH polar (50N-90N). Initially, model-measurement differences are calculated for each zonal bins (51 height levels, 10km to 60km). Note that if enough ACE measurements are not avaliable for a particular level then data is purely based on TOMCAT simulated output field. Separate XGBoost regression models are trained for the differences between TOMCAT and measurements at each level for a given latitude bin. XGBoost model is then used to estimate error corrections for all the TOMCAT grids. TOMCAT output sampled at 1.30 pm local time at the equator. Estimated corrections for a given model grid that are added to the original TOMCAT simulated day and night time ozone profiles. Height resolved data are then interpolated on 28-pressure levels (300 - 0.1hPa). For overlapping latitude bins, we use averages and then calculate daily zonal mean values. For more details see attached presentation. Previous version use both HALOE and ACE data. Here only ACE data is used (hence starting date is 01 January 2000). PDF file shows comparison between v1.0 and v1.1 as well as TOMCAT data.</p> <p>Dataset also includes two files containing daily mean zonal mean hydrogen fluoride profiles on height (10-60 km) and pressure (300-0.1 hPa) levels:</p> <p>zmo3_TCOM_hlev_T2Dz_2000_2024.nc – height level data (10 to 60 km)</p> <p>zmo3_TCOM_plev_T2Dz_2000_2024.nc – pressure level data (300 to 0.1 hPa)</p> <p>Daily 3D profiles on height and pressure levels would be made available on request.</p>
Investigation of spatial and temporal variability in lower tropospheric ozone from RAL Space UV-Vis satellite products - Dataset
<p>This data set represents a long-term (1996-2017) harmonised record of lower tropospheric ozone (surface - 450 hPa or surface - approximately 6 km) from satellite instruments. These instruments include the Global Ozone Monitoring Experiment (GOME-1, 1996–2002), the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY, 2003–2004) and the Ozone Monitoring Instrument (OMI, 2005–2017). These original products were produced by the Rutherford Appleton Laboratory (RAL) Space using the retrieval scheme described by Miles et al., (2015 - doi:10.5194/amt-8-385-2015). Pre-print of accepted manuscript can be found at https://doi.org/10.5194/egusphere-2023-1172.</p>
Global Surface Ozone Concentration Dataset 1990-2017 Mapped at Fine Resolution through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output
<p>This global surface ozone concentration dataset corresponds to the data developed in this paper:</p> <p>DeLang, M. N., J. S. Becker, K.-L. Chang, M. L. Serre, O. R. Cooper, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, S. Cleland, E. Collins, M. Brauer, and J. J. West (2021) Mapping yearly fine resolution global surface ozone through the Bayesian Maximum Entropy data fusion of observations and model output for 1990-2017, <em>Environmental Science & Technology</em>, 55, 4389-4398, doi: 10.1021/acs.est.0c07742.</p> <p>Ozone concentrations are estimated as described in the paper, with output shown for the Ozone Season Daily Maximum 8-hr metric (OSDMA8) for each year between 1990 and 2017, at 0.1 degree spatial resolution. Ozone is estimated through data fusion of output from several global models, with observations of ozone collected by TOAR. The data fusion involves application of the M3Fusion method to create a multi-model composite of several global models, followed by BME data fusion, as described in the paper. </p> <p>The *.nc file contains the latitude, longitude, ozone concentration estimate, and estimated variance for each 0.1 x 0.1 degree grid cell.</p> <p>Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We'd like to hear from you to know how you're using the data!</p> <p> </p> <p> </p>
Data complementing the publication: "Does total column ozone change during a solar eclipse?"
<p>Data published in this zip file complement the publication "Does total column ozone change during a solar eclipse?" by Germar H. Bernhard, George T. Janson, Scott Simpson, Raúl R. Cordero, Edgardo I. Sepúlveda Araya, Jose Jorquera, Juan A. Rayas, and Randall N. Lind, which will be published in the journal "Atmospheric Chemistry and Physics". A DOI of the publication will be added to this meta data description when available. The DOI of the publication's pre-print (paper under review) is: https://doi.org/10.5194/egusphere-2024-2659</p> <p>The contents of the zip file are organized in the following four subdirectories:</p> <p>- Figures: This directory contains the figures of the paper in PDF and PNG format plus the data used for plotting the figures.</p> <p>- GUVis-3511 Data Processor: This directory contains the software for processing the raw data collected during the solar eclipses described in the publication as well as ancillary data used for processing and manuals describing the software.</p> <p>- Limb darkening functions: This directory contains the functions expressing the change in the spectral irradiance during the eclipses discussed in the publication as a function of time and wavelength.</p> <p>- Raw data: This directory contains the raw data measured during the eclipses discussed in the publication.</p> <p>Each subdirectory and subdirectories nested therein contains "readme.txt" (in English) and "léeme_Espanol.txt" (in Spanish) files with further information of the contents of each subdirectory.</p>
Source Data and ambient ozone dataset generated in "Substantially underestimated global health risks of current ozone pollution"
<p>Existing assessments might have underappreciated ozone-related health impacts worldwide. Here our study assesses current global ozone pollution using the high-resolution (0.05°) estimation from a geo-ensemble learning model, with key focuses on population exposure and all-cause mortality burden. Our model demonstrates strong performance, achieving a mean bias of less than -1.5 parts per billion against in-situ measurements. We estimate that 66.2% of the global population is exposed to excess ozone for short term (> 30 days per year), and 94.2% suffers from long-term exposure. Furthermore, severe ozone exposure levels are observed in Cropland areas, particularly over Asia. Importantly, the all-cause ozone-attributable deaths significantly surpass previous recognition from specific diseases worldwide. Notably, mid-latitude Asia (30°N) and the western United States show high mortality burden, contributing substantially to global ozone-attributable deaths. Our study highlights current significant global ozone-related health risks and may benefit the ozone-exposed population in the future.</p>
Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction
<p>This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution. Also reported is the variance. Estimates correspond to this paper:</p> <p><span>Becker, J. S.</span><span>, DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, <u>H. Wang</u>, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and <span>J. J. West</span> (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, <em>Elementa Science of the Anthropocene</em>, 11: 1, doi: 10.1525/elementa.2022.00025.</span></p> <p>The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models. Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time. The estimates here are the final results using a weighted RAMP bias correction. </p>
Serdyuchenko-Gorshelev UV/VIS/NIR ozone absorption cross-section
<p>This dataset provides ozone absorption cross-sections in the range of 213-1100 nm at a spectral resolution of about 1 cm^-1 (0.01-0.03nm) recorded with a combination of a Bruker HR 120 Fourier transform and ESA 400 Echelle spectrometer. Cross-section data are available for 11 temperatures from 193K to 293K sampled at 0.01nm.</p> <p>Further details on this dataset can be found in the two follwing publications:</p> <p>Gorshelev, V., Serdyuchenko, A., Weber, M., Chehade, W., and Burrows, J. P., <strong>High spectral resolution ozone absorption cross-sections – Part 1: Measurements, data analysis and comparison with previous measurements around 293 K</strong>, Atmos. Meas. Tech., 7, 609-624, doi:10.5194/amt-7-609-2014, 2014.</p> <p>Serdyuchenko, A., Gorshelev, V., Weber, M., Chehade, W., and Burrows, J. P., <strong>High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence</strong>, Atmos. Meas. Tech., 7, 625-636, doi:10.5194/amt-7-625-2014, 2014.</p>
Second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'
<p>This is the second release of the data associated with the paper entitled 'Cluster-enhanced ensemble learning for mapping global monthly surface ozone from 2003 to 2019'.</p> <p>The paper was published in Geophysical Research Letters. We provide the data that has been smoothed by moving filter and not. The data can be loaded by the <em>raster </em>package in <em>R.</em> Note that the unit is ppmv.</p> <p>Please note that both of these files must be in the same directory to open in <em>R</em> properly<em>.</em></p> <p>Please get in touch with the authors if you have any issues, email: xliu21@smail.nju.edu.cn or wanghk@nju.edu.cn</p>
Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi
<p>This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision).</p> <p><strong>Experiment description</strong></p> <p>We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model.</p> <p>The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources.</p> <p>In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi.</p> <p><strong>Files</strong></p> <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> <p><strong>Contact</strong></p> <p>Shin-Ya Ogino<br> Japan Agency for Marine-Earth Science and Technology (JAMSTEC)<br> E-mail: ogino-sy@jamstec.go.jp</p>
Ozone dry deposition and ozone fields modeled by WRF-Chem
<p>This dataset includes hourly ozone dry deposition velocity v<sub>d</sub> and surface ozone concentration fields over the southeastern US in August 2016, which are simulated by the NASA Land Information System/Weather Research and Forecasting model with online Chemistry, without and with the assimilation of soil moisture retrievals from NASA’s Soil Moisture Active Passive mission. Different dry deposition parameterizations are used in this modeling/data assimilation work, as described in "Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 2: Sensitivity to dry-deposition parameterizations", by Huang et al. (2022). The model grid definition, along with the grid-dominant land use/cover type information, is also supplied. All data are stored in NetCDF files.</p>
Brewer Global Iradiance (GI) total ozone data at two Norwegian sites (2000 to 2020)
<p>Total column ozone (TCO) derived from Global Irradiance (GI) measurements from the Brewer instruments B042 in Oslo (Norway) and B104 in Andøya (Norway) from 01-01-2000 to 31-12-2020.</p> <p>The data consist of daily values averaged +/-2 hours around local noon.</p> <p>GI calibrations where performed with a clear sky direct sun (DS) measurements in 06-2001, 08-2014, 08-2016, 08-2018, and 08-2019 at Andøya, and in 08-2005, 06-2019, and 08-2019 at Oslo. The data has been calibrated with standard lamp measurements and have been homogenized with DS measurements as a function of clouds and solar zenith angle.</p> <p>The method, calibration, and homogenization is described by Bernet et al. (2022) (Appendix A).</p> <p>Responsible institute: NILU - Norwegian Institute for Air Research</p> <p>Funded by the Swiss National Science Foundation and the Norwegian Environment Agency</p> <p>Bernet, L., Svendby, T., Hansen, G., Orsolini, Y., Dahlback, A., Goutail, F., Pazmiño, A., Petkov, B., and Kylling, A., Total ozone trends at three northern high-latitude stations, 2022.</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.