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.
10
datasets available to search
ShareScore release 0.7.1
Dataset results
10 results for “chemical transport model”
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>
Digitized Particulate Matter Size Distribution Profiles from Literature Sources for Improved Size Representation of PM Emissions in Atmospheric Chemical Transport Models
<p>Processing particulate matter (PM) emissions for use in a chemistry transport model (CTM) such as GEM-MACH (Global Environmental Multiscale Modelling Air-Quality and Chemistry) requires detailed information about particle size distribution and chemical speciation for different PM emissions source types. The current PM size distribution and speciation profile library used at Environment and Climate Change Canada (ECCC) for preparing model-ready emission files for GEM-MACH contains very detailed chemical speciation profiles for PM emissions from 91 source types but only has three generic PM size disaggregation profiles, one each for mobile, point, and area sources. These generic profiles are used to disaggregate bulk PM emissions to a 12-bin sectional size representation, where PM<sub>2.5</sub> emissions are split into size bins 1-8 and PM<sub>10‑2.5</sub> emissions are split into bins 9 and 10. Since there is wide variability in the particle size distribution depending on the source type, the inclusion of source-type-specific PM size disaggregation profiles should lead to better representation of PM particle size for emissions from different source types in the model.</p> <p>A presentation entitled “Expansion of a Size Distribution Profile Library for Particulate Matter (PM) Emissions Processing from Three to 32 Source Categories” was given recently at the Community Modeling and Analysis System (CMAS) conference in Chapel Hill, North Carolina in October 2019 (<a href="https://www.cmascenter.org/conference/2019/slides/1300_zhang_expansion_size_2019.pptx">https://www.cmascenter.org/conference//2019/slides/1300_zhang_expansion_size_2019.pptx</a>) . This presentation described work carried out at ECCC to improve the PM size disaggregation profile library used to generate model-ready emissions. In particular, the number of PM size disaggregation profiles in the library was increased from three generic profiles to 32 source-type-specific profiles. After the conference, four more profiles were added to the library for a total of 36 PM size disaggregation profiles. In order to carry out this study, over 100 PM size distribution profiles from various PM emissions sources were gathered from literature publications, analyzed, and transformed into size disaggregation profiles that correspond to the GEM-MACH 12-bin sectional configuration. The 36 PM size disaggregation profiles that were obtained were then combined with detailed PM chemical speciation data to compile a new PM size disaggregation and chemical speciation library for emissions processing using the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system.</p> <p>This Excel workbook provides the digitized particle size distribution data for PM emissions from 36 different source types that were used as input to calculate the PM size disaggregation profiles for the GEM-MACH 12-bin sectional configuration. The digitized particle size distribution profiles were obtained by digitizing images of size distribution plots obtained from the literature publications using graph digitizing software such as Engauge Digitizer (<a href="http://markummitchell.github.io/engauge-digitizer/">http://markummitchell.github.io/engauge-digitizer/</a>) and WebPlot Digitizer (<a href="https://directory.fsf.org/wiki/WebPlotDigitizer">https://directory.fsf.org/wiki/WebPlotDigitizer</a>). By manually defining the axes and selecting points along the curve by computer mouse, a comma-separated-values file was generated for each size distribution profile image. From there, a series of transformations were carried out as required, including particle diameter conversions from aerodynamic diameter to Stokes diameter, and conversion of number-weighted size distributions to volume-weighted size distributions, in order to obtain a harmonized set of profiles. This Excel workbook contains the raw digitized data for all literature size distributions included in the compilation of the new library, as well as the diameter and size distribution weighting conversions. There are 39 worksheets: the first is an introductory worksheet entitled “Spreadsheet_Info” while the next 36 worksheets are ordered alphabetically and correspond to each of the 36 emissions source types for which a PM size disaggregation profile was generated. The final two worksheets contain digitized particle penetration data for common PM control devices.</p> <p>These digitized profiles may be used and adapted for use with other emissions processing systems and other CTMs with a size-resolved representation for PM. More details are provided in the following publication:</p> <p>Elisa I. Boutzis, Junhua Zhang & Michael D. Moran (2020) Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles, <em>Journal of the Air & Waste Management Association</em>, 70:11, 1067-1100, DOI: <a href="https://doi.org/10.1080/10962247.2020.1743794">10.1080/10962247.2020.1743794</a></p>
Global High Resolution Dust Emission Inventory for Chemical Transport Models
<p><strong>Overview:</strong><br> ==================================================================================</p> <p>Offline dust emissions in 2016 are now available at 0.25° x 0.3125° resolution. This dataset is calculated using the native resolution <a href="http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-FP">GMAO meteorology (GEOS-FP) fields</a>. </p> <p>Codes and Instructions (README file in the GitHub repository) to generate these offline emissions can be found on <a href="https://github.com/Jun-Meng/geos-chem/tree/v11-01-Patches-UniCF-vegetation">GitHub</a>.</p> <p>The offline emissions in this database have no scale factor applied, so users should apply the required scale factor in their application. Suggested scale factor to make the global total annual dust emission to 2000 Tg is 5.7141e-4. </p> <p><br> <strong>Zip File Details:</strong><br> ===============================================================================</p> <p>2016.zip contains daily (366 in total) netCDF files (stored in monthly folders) of global gridded hourly mineral dust emission flux rate. </p> <p> </p> <p>Individual file: </p> <p>/YYYY/MM/dust_emissions_025x0.3125.YYYYMMDD.nc</p> <p> Resolution : 0.25 x 0.3125 grid (721 x 1152 boxes)<br> Units : kg m-2 s-1<br> Timestamps : Hourly, 2016<br> Compression : Level 1 (nccopy -d1)<br> Chunking : nccopy -c lon/1152,lat/721,time/24</p> <p> </p> <p>Variables in each file: </p> <p>EMIS_DST1, EMIS_DST2, EMIS_DST3 and EMIS_DST4 represent dust emission flux rate in four size bins (0.1-1.0, 1.0-1.8, 1.8-3.0, and 3.0-6.0 micro in radius). </p> <p> </p> <p>*<em>Version 2020_v1.0 of this dataset was produced to accompany the following manuscript:<br> Meng, Jun, R. V. Martin, P. Ginoux, M. Hammer, M. P. Sulprizio, D. A. Ridley, and A. van Donkelaar, Grid-independent high resolution dust emissions (v1.0) for chemical transport models: application to GEOS-Chem (version 12.5.0), Geoscientific Model Development, Submitted</em></p>
Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model
<p>This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air).</p> <p>The dimensions in each field are:</p> <p>lat --> latitude</p> <p>lon --> longitude</p> <p>lev --> hydrid pressure level</p> <p>time --> months of the simulation</p> <p>The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files:</p> <p>toz_A_ERAI.nc</p> <p>toz_B_ERA5.nc</p> <p>The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively):</p> <p>sco_SWOOSH_A_ERAI_B_ERA5.nc</p> <p>The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files:</p> <p>O3_mm_A_ERAI.nc</p> <p>O3_mm_B_ERA5.nc</p> <p>The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files:</p> <p>te_mm_A_ERAI.nc</p> <p>te_mm_B_ERA5.nc</p> <p>The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files:</p> <p>Age_mm_A_ERAI.nc</p> <p>Age_mm_B_ERA5.nc</p> <p>The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files:</p> <p>ERA5_1_O3_2000_18.nc</p> <p>ERA5_1_te_2000_18.nc</p> <p>ERA5_1_Age_2000_18.nc</p> <p> </p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
Identifying contributors to PM2.5 simulation biases of chemical transport model using fully connected neural networks
<p>The processed data and codes in the study are included. </p> <p><strong>Source data:</strong></p> <p>The training and testing dataset is composed of observed and simulated data of pollutants and meteorology in the BTH and YRD regions in the whole year of 2015. The processed datasets used for training are named as "dataset_BTH" and "dataset_YRD" in the folder.</p> <ul> <li><em>The hourly observed pollution data</em> are from China National Urban Air Quality Real-time Release Platform of the National Environmental Monitoring Station</li> <li><em>The hourly simulated pollutants data</em> comes from the output of WRF-CMAQv5.2 (spatial resolution of 27 km).</li> <li><em>Meteorological observation data</em> is provided by China Meteorological Data Service Centre</li> <li><em>The meteorological simulation data</em> comes from the simulation results of the WRF model</li> </ul> <p><strong>Codes:</strong></p> <ul> <li>preprocessing of raw CMAQ data, observed pollution data and meteorological data</li> <li>bulid and train process of fully connected neural networks</li> <li>calculation of correlation between variables</li> <li>feature selection method</li> <li>contribution analysis</li> </ul>
Dataset in support of the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Dataset supporting the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file in the CloudJ sub-folders.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Data repository in support of the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created modules providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models are called CloudJ_mod.f90.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts provided here, can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p> <p> </p>
ATom: Global Modeling Initiative (GMI) Chemical Transport Model (CTM) Output
This dataset contains Global Modeling Initiative (GMI) Chemical Transport Model (CTM) outputs from the four Atom campaigns. GMI simulations of the ATom flight periods have a horizontal resolution of 1.0 x 1.25 degrees, with output every 15 minutes. The ICARTT files are generated by spatially and temporally interpolating the output to the ATom flight track. Vertical interpolation is linear in log-pressure. The netCDF files provide three-dimensional (3D) GMI simulation output for the region surrounding the flight track every 15 minutes at the original model resolution. GMI is a 3-D CTM that includes full chemistry for both the troposphere and stratosphere. GMI simulates the concentrations of many of the species measured during ATom.
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.