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114 results for “TROPOMI”
Dataset: all TROPOMI detected plumes for 2021. [Schuit et al. 2023: Automated detection and monitoring of methane super-emitters using satellite data]
<p>Dataset of all TROPOMI detected methane plumes in 2021, including estimates for the source location, emission quantification and source type. Corresponds to Figure 6 of Schuit et al. 2023 [Automated detection and monitoring of methane super-emitters using satellite data, https://doi.org/10.5194/acp-23-9071-2023]. Additional details and context are provided in Section 3 of the paper.</p> <p> </p> <p><em>Contents and data formats</em></p> <p><strong>date</strong>, date of the TROPOMI observation. format: YYYYMMDD</p> <p><strong>time_UTC</strong>, time of the TROPOMI observation in UTC. format: HH:MM:SS</p> <p><strong>lat</strong>, latitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>lon</strong>, longitude of the center of the TROPOMI pixel at the estimated source location. format: float</p> <p><strong>source_rate_t/h</strong>, estimated emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>uncertainty_t/h</strong>, the uncertainty of the emission source rate in tonnes per hour, the methodology is described in Section 2.5.1 of the paper. format: int</p> <p><strong>estimated_source_type</strong>, the locally dominant anthropogenic source sector based on bottom-up inventories, the methodology is described in Section 2.5.3 of the paper. format: str</p> <p> </p> <p>Full citation of the paper:</p> <p>Schuit, B. J., Maasakkers, J. D., Bijl, P., Mahapatra, G., van den Berg, A.-W., Pandey, S., Lorente, A., Borsdorff, T., Houweling, S., Varon, D. J., McKeever, J., Jervis, D., Girard, M., Irakulis-Loitxate, I., Gorroño, J., Guanter, L., Cusworth, D. H., and Aben, I.: Automated detection and monitoring of methane super-emitters using satellite data, Atmos. Chem. Phys., 23, 9071–9098, https://doi.org/10.5194/acp-23-9071-2023, 2023.</p>
Non-methane volatile organic compound emissions over China estimated using TROPOMI HCHO retrievals
<p>We used the Regional multi-Air Pollutant Assimilation System (RAPAS) with the EnKF algorithm to optimize daily NMVOC emissions in China by assimilating TROPOMI HCHO retrievals. </p><p>airqual.qc.csv includes assimilated and verified surface NO2 observations.</p><p>HCHO.tar.gz includes assimilated TROPOMI HCHO retrievals.</p><p>posterior_emission_27km.nc and posterior_emission_mg_27km.nc includes inferred daily posterior anthropogenic and biogenic NMVOC emissions respectively for August 2022.</p>
University of Leicester TROPOMI Stable Water Vapour Isotopologue (H2O-ISO) Prototype Product (Vesrion 1.0.0)
<p>This repository holds the prototype level 2 TROPOMI stable isotopologue product for June 2019 used in the study:</p> <p>Thurnherr, I., Sodemann, H., Trent, T., Werner, M., and Boesch, H., 2024. Evaluating TROPOMI δD column retrievals with in situ airborne measurements using expanded collocation criteria, Earth Space Science, in review</p> <p>For further details, please refer to the project website (https://s5pinnovationh2o-iso.le.ac.uk/), which contains the Algorithm Theoretical Baseline Document (ATBD) and Product User Guide (PUG). The final prototype product (version 1.0.2) is freely available from T. Trent (University of Leicester) upon request.</p>
Estimating global transpiration from TROPOMI SIF with angular normalization and separation for sunlit and shaded leaves
<p>All three types of SIF-driven T models integrate canopy conductance (gc) with the Penman-Monteith model, differing in how gc is derived: from a SIFobs driven semi-mechanistic equation, a SIFsunlit and SIFshaded driven semi-mechanistic equation, and a SIFsunlit and SIFshaded driven machine learning model. </p> <p>The difference between a simplified SIF-gc equation and a SIF-gc equation is the treatment of some parameters and is shown in <a href="https://doi.org/10.1016/j.rse.2024.114586" rel="noreferrer">https://doi.org/10.1016/j.rse.2024.114586</a>.</p> <p>In this dataset, the temporal resolution is 1 day, and the spatial resolution is 0.2 degree.</p> <p>BL: SIFobs driven semi-mechanistic model</p> <p>TL: SIFsunlit and SIFshaded driven semi-mechanistic model</p> <p>hybrid models: SIFsunlit and SIFshaded driven machine learning model.</p>
TROPOMI-derived ground level NO2 concentrations (2019 & 2020 Monthly Means)
<p>Monthly mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations for January-June 2019 and 2020. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.<br> </p>
TROPOMI-derived ground level NO2 concentrations (2019 Annual mean)
<p>Annual mean ground level NO2 concentrations derived from TROPOMI satellite NO<sub>2</sub> observations. Ground level concentrations are derived from observed column densities using the GEOS-Chem chemical transport model constrained with ground monitor observations following the method outlined in Cooper et al 2021 (DOI: 10.1038/s41586-021-04229-0)</p> <p><br> Annual mean data are provided at ~1x1 km<sup>2</sup> resolution at satellite overpass time (~1:30 PM local). Datasets are in netcdf (.nc) format.</p>
Annual mean TROPOMI-derived ground-level NO2 mixing ratio (2019 - North America v1)
<p>Annual mean ground-level NO2 mixing ratio for 2019 inferred from the TROPOMI satellite instrument over North America at 0.025x0.03125 degree resolution. Included is 2019 annual mean and 1.5 year mean spanning July 2018 – December 2019.</p> <p><strong>Reference:</strong></p> <p>Cooper, M.J., R.V. Martin, C.A. McLinden, and J.R. Brook (2020), Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument, Env. Res. Lett., DOI:10.1088/1748-9326/aba3a5</p>
Global spatiotemporal continuous daily high-resolution total column carbon monoxide for TROPOMI
<p>A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.</p>
TROPOMI research ozone profile dataset in August 2022
<p>This dataset contains ozone profile retrievals from TROPOMI (TROPOspheric Monitoring Instrument) backscattered ultraviolet measurements during the Asian Summer Monsoon Chemical and Climate Impacts Project (ACCLIP) conducted over the Western Pacific in summer 2022. The data is stored in individual files for each date, with all variables described in the attribute field. This dataset has been used in Bak et al. (submitted) for an integrated analysis of ozone and carbon monoxide alongside ACCLIP field datasets and other satellite products.</p> <p><strong>Details on Retrieval Methodology:</strong></p> <ol> <li><strong>Inverse Method:</strong> Optimal estimation</li> <li><strong>Radiative Transfer Model:</strong> PCA-VLIDORT v2.8</li> <li><strong>A Priori Ozone:</strong> Tropopause-based ozone profile climatology</li> <li><strong>Fitting Window:</strong> 310-335 nm in the TROPOMI UV3 channel</li> <li><strong>Spatial Resolution:</strong> Cross-track pixels are binned to match the spatial resolution of the TROPOMI UV1 channel, with every 5 scan pixels coadded.</li> <li><strong>Spatial coveragne</strong> : 50E-180E, 10S-70S</li> <li><strong>Heritage:</strong> OMI PROFOZ ozone profile algorithm (Liu et al., 2010; Bak et al., 2024)</li> </ol>
MAX-DOAS tropospheric NO2 column measurements in Islamabad, Pakistan (33°N, 73°E) from 2015 to 2019 and comparisons with OMI and TROPOMI satellite data
<p>This data presents an intercomparison of NO<sub>2</sub> retreival settings using Differential Optical Absorption Spectroscopy (DOAS) and those based on literature published over last 20 years. Moreover, it presents comparison of NO<sub>2</sub> Vertical Column Densities(VCD) obtained from ground based MAX-DOAS in Islamabad, Pakistan with satellite data from 2015-2019. MAX-DOAS has retrieved data at seven elevation angles i.e., 2, 4, 5, 10, 15, 30, 45. On the other hand, VCDs are in molecules per cm<sup>2</sup>. However, in order to collect NO2 dataset, DOASIS was used was used to obtain data from MAX-DOAS and further analyzed using QDOAS. Then geometric approximation was applied to obtain VCDs that are presented in this data set.</p>
Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network
<p>Public Repository of the model outputs of TROPOMI NO2 datasets for 2019 and 2020.</p> <p>Comprises:</p> <p>Saved Partial Convolution Neural Network models (PCNN, PCNN-ST, and DW-PCNN) and code to load the models.</p> <p>Datasets (in Netcdf4 format) from PCNN model outputs, Inverse Distance Weighting, Inverse Distance Weighting with Kriging, spatial coordinates, time, target NO2 for imputation, and masks.</p> <p> </p>
SRON S5P - RemoTeC scientific TROPOMI XCH4 dataset
<p>The datasets uploaded contain SRON S5P - RemoTeC scientific TROPOMI XCH4 data that has been post processed from the 'raw' orbit-by-orbit dataset available for download at https://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/ch4/14_14_Lorente_et_al_2020_AMTD/ (last access: 20 January 2021).</p> <p>File [1] contains global TROPOMI and GOSAT proxy XCH4 daily collocated means averaged into a 2 x 2 degree grid for the period 1 January 2018–31 December 2019.</p> <p>File [2] contains global TROPOMI XCH4 data for 2019 averaged in a cylindrical equal-area grid with 0.3 x 0.5 degree resolution at the Equator.</p> <p>[1] tropomi_gosat_pr_xch4_2x2latlon_dailymeans_14_14_6575_7304_20182019_correction_albedo_only.nc</p> <p>[2] tropomi_xch4_14_14_corr_only_alb_2yr.nc</p> <p> </p>
Absorbing Aerosol Optical Central Height (AOCH) retrieved from TROPOMI with UIowa's AOCH-O2AB algorithm
<p>Absorbing Aerosol Optical Centroid Height (AOCH) retrieved from TROPOMI with UIowa’s AOCH-O<sub>2</sub>AB algorithm. Dataset for analyzing dust and smoke cases over Asia during 2021-2023.</p> <p>More information about this dataset can be found in: </p> <p>Chen, X., Wang, J., Xu, X. G., Zhou, M., Zhang, H. X., Garcia, L. C., Colarco, P. R., Janz, S. J., Yorks, J., McGill, M., Reid, J. S., de Graaf, M., and Kondragunta, S.: First retrieval of absorbing aerosol height over dark target using TROPOMI oxygen B band: Algorithm development and application for surface particulate matter estimates, Remote Sensing of Environment, 265, 18, <a href="https://doi.org/10.1016/j.rse.2021.112674">https://doi.org/10.1016/j.rse.2021.112674</a>, 2021.</p>
Global gridded NOx emissions using TROPOMI observations
<p>This dataset provides top-down global gridded emissions of NOx for the year 2022. It is constructed from retrievals of tropospheric vertical column densities of NO2 by the TROPOMI spaceborne instrument associated with winds and atmospheric composition data from ECMWF reanalyses, using an improved version of the flux-divergence method. The emissions are provided with a spatial resolution of 0.0625°×0.0625° and deliver a detailed overview of the distribution of emissions. <br>This dataset also provides two lists of point and diffuse sources, defined as clusters of at least 3 contiguous pixels above the value of 2 Pmolecules/cm²/h for annual averaged NOx emissions, calculated with the given monthly gridded maps. Point sources are defined as clusters comprising 3 to 9 pixels while diffuse sources are clusters with more than 10 pixels. Corrections have been applied from the previous version of the dataset; changes are generally minor, except for emissions at very high latitudes.</p>
A Reconstructed Global Daily Seamless TROPOMI SIF dataset at a 0.05-degree Resolution (SDSIF) using the ML approach
<p>To enhance the spatial and temporal resolutions and continuities of the TROPOMI SIF, a global daily seamless SIF product at 0.05-degree resolution (namely, SDSIF) from May 2018 to December 2020 was generated based on the ML approach using TROPOMI SIF, MODIS reflectance, and ERA5 reanalysis datasets. This dataset has been validated with the original TROPOMI SIF and the long-term tower-based SIF from five flux sites, which verified the reliability of SDSIF and the advantages over original TROPOMI SIF.</p>
SRON S5P - RemoTeC scientific TROPOMI XCH4 dataset v18_17
<p>The datasets uploaded contain SRON S5P - RemoTeC scientific TROPOMI XCH4 data that has been post processed from the 'raw' orbit-by-orbit dataset available for download at https://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/ch4/18\_17 (last access: 8 November 2022).</p> <p>Files contain global TROPOMI XCH4 data for 2018, 2019, 2020, and 2021 averaged in a cylindrical equal-area grid with 0.3 x 0.5 degree resolution at the Equator.</p> <p> </p>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
SRON S5P - RemoTeC scientific TROPOMI XCH4 dataset v19_446
<p>The dataset uploaded contain SRON S5P - RemoTeC scientific TROPOMI XCH4 data that has been post processed from the 'raw' orbit-by-orbit dataset available for download at https://ftp.sron.nl/open-access-data-2/TROPOMI/tropomi/ch4/19\_446 (last access: 24 March 2023).</p> <p>File contains global TROPOMI XCH4 data for 2018-2020, daily means averaged into a 0.2 x 0.2 degree grid.</p>
Experimental downscaled TROPOMI SIF dataset for continental Europe
<p>The present dataset represent the attempt done within the Sen4GPP project to produce a prototype downscaled SIF for continental Europe. The objective was to adapt an existing downscaling methodology (Duveiller et al. 2020) and apply it to selected sentinel data in order to downscale TROPOMI SIF data (Guanter et al. 2022) from a 10 km grid to a 1 km grid. The method relies on a locally calibrated model linking fine spatial resolution explanatory variables to the coarse spatial resolution target using a moving window. In this case, the explanatory variables are the Sentinel-3 OLCI green vegetation index (OGVI), and Sentinel-3 SLSTR daytime land surface temperature (LST), which are preprocessed into 8-daily composites by project partner U. of Southampton. For details on the downscaling algorithm, the reader is directed to the ATBD document of the Sen4GPP project.</p> <p>The dataset covers the TROPOMI period from 2018-05-11 until 2020-12-29 for continental Europe. The data is in provided in sinusoidal projection widely used with the MODIS land products, and it covers the area of the MODIS tiles v2 to v5 and h17 to h20. The dataset is divided in separate NetCDF files, with each file covering the entire spatial domain for a single time slice, and each slice representing a period of 8-days. The main variable of interest in each individual file is the predicted downscaled SIF at 1km (variable name: sif) that is mapped on the main dimensions (easting, northing) that cover 4800 by 4800 pixels of circa 1km in the Sinusoidal projection.</p>
TroDSIF: an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset
<p>TroDSIF is an improved spatially downscaled solar-induced chlorophyll fluorescence product of TROPOMI dataset at far-red band (wavelength at 740 nm), with a spatial resolution of 500 m and a temporal resolution of 16 days under clear-sky condition.<br>The original TROPOMI SIF was retrieved by Guanter et al., which was available at https://doi.org/10.5270/esa-s5p_innovation-sif-20180501_20210320-v2.1-202104.</p>
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