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122 results for “Emission modeling”
Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki
<p>This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are:</p> <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>
Exploring ultraweak photon emissions as optical markers of brain activity - Dataset and statistical models
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Model data for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions and burned area as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br><sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br><sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br><sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br><sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p><strong>UPDATE OF DATASET TO 2023:</strong></p> <p>This dataset has now been extended to 2023. Since the first release of this dataset, multiple updates to the model input data have been made:</p> <p>- Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.<br>- Update of Hansen forest loss data from v1.9 to v1.11.<br>- Update of GLEAM evaporative stress data from v3.6b to v3.7b.<br>- Extension of ERA5-land data to 2023.<br>- Addition of land cover type layers to the 500-m resolution data files.</p> <p> </p> <p>Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)" published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 500-m resolution files include land cover type grids. 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.</p> <p>Zip archives with filenames "500m_YYYY.zip" contain annual files named "Model500m_2002-2023yr_h##v##_YYYY.nc", which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:</p> <p>- Total biomass burning carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_TOT)</p> <p>- Total biomass burning carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_TOT)</p> <p>- Fire-related forest loss carbon emissions from aboveground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_AG_FL)</p> <p>- Fire-related forest loss carbon emissions from belowground; g C m<sup>-2</sup> month<sup>-1</sup> (/MOD_Grid/emissions/C_BG_FL)</p> <p>Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.</p> <p>Burned area data layers are:</p> <p>- Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)</p> <p>- Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)</p> <p>The Zip archive with filename "025d_2002_2023.zip" contains annual files named "Model500m_2002-2023yr_025d_YYYY.nc", which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.</p>
Model code for "Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"
<p>500 m fire carbon emissions model code as part of the publication:</p> <p>"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)"</p> <p>Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup></p> <p><sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</p> <p>DOI: https://doi.org/10.5194/gmd-15-8411-2022</p> <p> </p> <p>Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.</p>
Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions
<p>The files provide additional information to the paper “Grey model analysis of vehicle population, road transport energy consumption, and vehicular emissions". The supporting data file contains excel sheets of data used in the analysis, and the supporting information file provides some assumptions, background information and other results not included in the paper</p>
Model data and code supporting "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "
<p>Model data and analysis code supporting the Geoscientific Model Development manuscript "Updated Isoprene and Terpene Emission Factors for the Interactive BVOC Emission Scheme (iBVOC) in the United Kingdom Earth System Model (UKESM1.0) "</p> <p> </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>
A structured evaluation of regression models for predicting CO2 concentration from plasma emission spectra, dataset
<p>Dataset for publication: <a href="https://doi.org/10.1016/j.sab.2022.106467">https://doi.org/10.1016/j.sab.2022.106467</a>.</p> <p>The recorded spectra are stored as comma separated values, the set includes a meta data-file (.mat-file), and a column descriptions (columns.pdf).</p>
Products and Models for "A broadband thermal emission spectrum of the ultra-hot Jupiter WASP-18b"
<p>Close-in giant exoplanets with temperatures greater than 2,000 K (“ultra-hot Jupiters”) have been the subject of extensive efforts to determine their atmospheric properties using thermal emission measurements from the Hubble and Spitzer Space Telescopes1–3. However, previous studies have yielded inconsistent results because the small sizes of the spectral features and the limited information content of the data resulted in high sensitivity to the varying assumptions made in the treatment of instrument systematics and the atmospheric retrieval analysis3–12. Here we present a dayside thermal emission spectrum of the ultra-hot Jupiter WASP-18b obtained with the NIRISS13 instrument on JWST. The data span 0.85 to 2.85 μm in wavelength at an average resolving power of 400 and exhibit minimal systematics. The spectrum shows three water emission features (at <6σ confidence) and evidence for optical opacity, possibly due to H-, TiO, and VO (combined significance of 3.8σ). Models that fit the data require a thermal inversion, molecular dissociation as predicted by chemical equilibrium, a solar heavy-element abundance (“metallicity”, M/H = 1.03-0.51+1.11 x solar), and a carbon-to-oxygen (C/O) ratio less than unity. The data also yield a dayside brightness temperature map, which shows a peak in temperature near the sub-stellar point that decreases steeply and symmetrically with longitude toward the terminators.</p>
Synergy between deep learning and numerical modeling in estimating NOx emissions at a fine spatiotemporal resolution
<p>This study focused on the remarkable applicability of deep learning (DL) together with numerical modeling in estimating NO<sub>x</sub> emissions at a fine spatiotemporal resolution in the summer of 2017 over the contiguous United States (CONUS). We leveraged the partial convolutional neural network (PCNN) and the deep neural network (DNN) to impute gaps in the OMI tropospheric NO<sub>2</sub> column and estimate the daily complete surface NO<sub>2</sub> map at a spatial resolution of 10 km × 10 km, showing high capability with a strong correspondence (R: 0.92, IOA: 0.96, MAE: 1.43). We then used the Community Multi-scale Air Quality (CMAQ) model at 12 km grid spacing to conduct an inversion of NO<sub>x</sub> emissions that allowed us to promote a comprehensive understanding of the chemical evolution. Compared to the prior emissions, the inversion suggested 3.21 ± 3.34 times higher NO<sub>x</sub> emissions over CONUS, significantly mitigating the underestimation of surface NO<sub>2</sub> concentrations with the prior emissions. The results displayed the primary benefits of incorporating DL-estimated daily complete surface NO<sub>2</sub> map, which in turn greatly reduced bias (-1.53 ppb to 0.26 ppb) and enhanced daily variability with higher correspondence (0.84 to 0.92) and lower error (0.48 ppb to 0.10 ppb) over the CONUS. </p>
Emissions impact of connected and automated vehicle deployment in California - model results
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Modeled isoprene emissions from NYC-MEGAN
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Dynamic inferential NOx emission prediction model with delay estimation for SCR de-NOx process in coal-fired power plants
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Model code for reconstructing biomass burning emissions from ice core records of acetylene, ethane, and methane
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The dataset of the manuscript "Numerical study of the initial condition and emission on simulating PM2.5 concentrations in Comprehensive Air Quality Model with extensions version 6.1 (CAMx v6.1): Taking Xi'an as example"
<ul> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/bcfile.rar?versionId=4909d094-5877-408e-bd4f-0c969c54e585">bcfile.rar</a>: the clean initial and boundary condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forNov.rar?versionId=0a1e8b66-5157-4819-8c03-20fb7797d8ef">Emis_forNov.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/Emis_forDec.rar?versionId=d4db00f1-ec1b-4096-973e-6a87133e4eac">Emis_forDec.rar</a>: the emission files in November and December 2016.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/tuvfile.rar?versionId=5dcf0977-e416-466e-9089-bbf0726c788d">tuvfile.rar</a> and <a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/o3mapfile.rar?versionId=9c25cae9-f00e-4ad3-b4dc-7a721f7f44d7">o3mapfile.rar</a>: the photolysis files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.cp1.rar?versionId=09ded31b-4c42-4e40-a21c-0f18877e9e41">camx.cp[1-5].rar</a>: the results of sensitivity experiments for using clean initial condition files.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1120p1.rar?versionId=45d6e209-8e66-43ed-bc0b-dc8af5521352">camx.r1120p[1-3].rar</a>: the results of sensitivity experiments for R1120.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/camx.r1124.rar?versionId=c138e436-0416-4701-948d-ce761cf6c5cf">camx.r1124.rar</a>: the results of sensitivity experiments for R1124.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B12.rar?versionId=34485c43-77ac-4001-8a6d-a57b7ff821e3">contnuous_B12.rar</a>: the results of sensitivity experiments for CT12.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/contnuous_B24.rar?versionId=0f325a61-f19c-4bac-b8b4-7e229c332bf9">contnuous_B24.rar</a>: the results of sensitivity experiments for CT24.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/scripts.zip?versionId=b030444c-51a5-4673-b9d1-7e80ec42a3b9">scripts.zip</a>: all scripts covering every data processing action for all the results reported in the paper.</li> <li><a href="https://zenodo.org/api/files/cabf59e1-a955-4190-a83c-48d82efea4e6/data.zip?versionId=52917a53-fca7-4a72-ba2f-a2ce7593adc4">data.zip</a>: final data tables used to plot figures and tables.</li> </ul>
Dataset for AMT paper "Ammonia emissions from a grazed field estimated by miniDOAS measurements and inverse dispersion modelling"
<p>Single excel file containing 30 minute averaged measurements and model outputs. Includes miniDOAS concentration measurements, sonic anemometer (Gill WindMaster) wind & turbulence, environmental measurements (relative humidity, rainfall, temperature, net radiation flux), deposition velocity resistance components (measured Ra and Rb and modelled Rc), and bLS-R model outputs (dispersion coefficients, also known as C/E ratios). These data provide the model inputs required to run the bLS-R dispersion model, (alternatively the WindTrax dispersion model can be used). The emissions from the field during both measuring periods can be determined from the measured horizontal concentration gradient and the simulated dispersion coefficients (Emissions = (Cdownwind-Cupwind)/Dispersion coefficient).</p>
Observed monthly N2O emission dataset used for model carlibaration and validation
<p>The N<sub>2</sub>O emission dataset for calibration sites was extracted from the published figures and tables using GetData Graph Digitizer version 2.24; the other information such as biome, geographic location, experimental period, soil organic carbon content, soil pH and soil texture was selected from corresponding literature. If the data related to soil was not avaliable, we extracted them from the soil database(IGBP-DIS)</p>
Source code and model outputs for 'Increasing Aerosol Direct Effect Despite Declining Global Emissions'
<p>Source code, model outputs and python scripts used for the publication of Hermant, A., Huusko, L., & Mauritsen, T: Increasing Aerosol Direct Effect Despite Declining Global Emissions</p>
AGN and stellar spectra used to produce model molecular emission lines
<p><span>Aiming to investigate the effects of stellar and AGN radiation fields to the excitation of molecular gas, we present an analysis of model CO, H</span><span><sub>2</sub>O, and OH<sup>+</sup> emission lines produced with the application of these radiation fields using MOLPOP-CEP. We find that the ratios of CO(J<sub>upp</sub>) = 11, 12, 13, 14, 15) to CO(J<sub>upp</sub>) = 1, 2, 3, 4, 5, 6, 7, 8) obtained with the application of AGN radiation are larger than the ones produced by applying stellar radiation. Similarly, some H<sub>2</sub>O and </span><span>OH<sup>+</sup> ratios obtained with AGN radiation are found to be higher than the corresponding ratios produced by applying stellar radiation field, making the line ratios a diagnostic tool to disentangle AGN from star formation. In all cases, we find that the ratios vary as a function of the molecular gas density and the scale of the radiation field, the larger the scale of the radiation field is the stronger the excitation of the molecular transitions. Also, the effect of the radiation fields is found to be dependent on the molecular gas density, indicating that the effect of the radiation fields is regulated by the physical properties that are intrinsic to the molecular gas. Thus, due to the dependency of the line ratios on the scale of the radiation fields and the effect of the radiation fields on the molecular gas density, we conclude that it is not sufficient to use a single line ratio to claim the presence of AGN contribution to the molecular gas excitation.</span></p>
Tool and python programs for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model"
<p>Here is the content:</p> <p> * file dir_list which contains information about each file's content</p> <p> * the tool is used to alter a data file by keeping a specific number of significant digits for the paper "The Impact of Altering Emission Data Precision on Compression Efficiency and Accuracy of Simulations of the Community Multiscale Air Quality Model'</p> <p> * pythons program and its associated data to create each figure and table in the paper (data for Table 07 is not included due to size is larger than 50GB)</p>
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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)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.