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opencc-by-4.0Feb 2023View details →

TCOM-HCl : Daily global gap-free stratospheric hydrogen chloride profile data set based on TOMCAT CTM and Occultation Measurements

Methodology:   The TOMCAT simulation was conducted at a T64L32 resolution, consistent with previous work by Dhomse et al. (2021, 2022), covering the period from 2000 to 2024. These simulations utilized ERA-5 reanalysis data. <h3>HCl Profile Processing and Bias Correction</h3> Collocated HCl profiles are organized into five distinct latitude bins: <ul> <li> NH polar: 90∘N - 50∘N </li> <li> NH mid-lat: 20∘N - 70∘N </li> <li> Tropics: 40∘S - 40∘N </li> <li> SH mid-lat: 70∘S - 20∘S </li> <li> SH polar: 90∘S - 50∘S </li> </ul> Initially, differences between TOMCAT and satellite measurements (primarily ACE-FTS data) are calculated for each zonal bin across 51 height levels (ranging from 10,km to 60,km). Separate XGBoost regression models are then trained for these HCl differences at each height level within a given latitude bin. These trained models are subsequently used to estimate HCl bias corrections for all daytime TOMCAT grids (9132 days), specifically sampled at 1:30 PM local time at the equator. This yields grid-specific bias corrections that are applied to the original TOMCAT profiles. Height-resolved HCl profile data are then interpolated onto 28 standard pressure levels (from 300,hPa to 0.1,hPa), using pressure levels directly from the TOMCAT grids. For overlapping latitude bins, values are averaged to ensure smoother fields near boundary regions. <h3>Data Files</h3> The dataset includes two files containing daily mean zonal mean HCl profiles: <ul> <li> <code>zmhcl_TCOM_hlev_T2Dz_2000-2024_V1.1.nc</code>: Contains height level data (10,km to 60,km). </li> <li> <code>zmhcl_TCOM_plev_T2Dz_2000-2024_V1.1.nc</code>: Contains pressure level data (300,hPa to 0.1,hPa). </li> </ul> <h3>Reference Publication</h3> This methodology, incorporating only ACE-FTS data and various minor algorithmic developments, is based on the following publication: Dhomse, S. S. and Chipperfield, M. P.: Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets, Earth Syst. Sci. Data, 15, 5105–5120, <a title="null" href="https://doi.org/10.5194/essd-15-5105-2023">https://doi.org/10.5194/essd-15-5105-2023</a>, 2023

Daily 1-km gap-free PM2.5 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM2.5 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

Daily 1-km gap-free PM10 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free PM10 concentration product with daily 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

Daily 1-km gap-free AOD grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide a 21-year-long (2000–2020) gap free AOD product with daily 1-km resolution covering the land area of China. The dataset was generated via a seamless integration of the tensor flow based multimodal data fusion with ensemble learning based knowledge transfer in statistical data mining. The proposed method transformed a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility that were acquired from diversified sensors or platforms via integrative efforts of spatial pattern recognition for high dimensional gridded data analysis toward data fusion and multiresolution image analysis. The daily gap free AOD was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

Annual mean 1-km gap-free AOD, PM2.5, and PM10 grids in China, v1 (2000–2020)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP aerosol dataset (LGHAP.v1), we provide 21-year-long (2000–2020) gap free annual mean AOD, PM2.5 and PM10 concentration data with a 1-km resolution covering the land area of China. The dataset was generated from the daily gap free AOD (https://doi.org/10.5281/zenodo.5652257) that was derived through an integration of a set of data tensors of AOD and other related datasets such as air pollutants concentration and atmospheric visibility acquired from diversified sensors or platforms via a machine learned regression model. The dataset was provided in the NetCDF format, while data in each individual year were archived in a zip file. Python, Matlab, R, and IDL codes were also provided to help users read and visualize the LGHAP data.

opencc-by-4.0Mar 2023View details →

Global monthly and 0.1° gap-free XCO2 dataset

This dataset consists of the global continental continuous column-averaged dry-air mole fraction of  carbon dioxide (XCO2, unit: parts per million i.e., ppm) covering the period from September 2014 to December 2020.  The dataset is derived from the official OCO-2 XCO2 product and incorporates data from multiple sources, including CO2 concentration data, vegetation index data, and meteorological data. We utilized a machine learning approach to generate a monthly-scale gapless CO2 product with a spatial resolution of 0.1°, stored in GeoTiff format.   Please cite the following article when using the dataset: L. Zhang, T. Li, J. Wu, H. Yang, Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data, Environment International (2023), doi: https://doi.org/10.1016/j.envint.2023.108057

LGHAP v2: Global daily 1-km gap-free AOD grids (2002)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2000)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2007)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2003)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2009)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2014)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2016)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2012)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2015)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2008)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2011)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2018)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.

LGHAP v2: Global daily 1-km gap-free AOD grids (2004)

A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.