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Climate models, weather data, oceanography, hydrology, atmospheric science, environmental monitoring
28,225 datasets
22-year-long gap-free daily aerosol optical depth and PM2.5 concentration grids at 1-km resolution cover the global land area from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An ensemble learning graph attention network was developed to predict PM2.5 concentrations, especially for regions with limited ground measurements.
22-year-long daily 1-km resolution gap-free aerosol optical depth and PM2.5 concentration grids covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An improved big earth data analytic framework with attention-reinforced tensor construction and a scene-aware ensemble learning graph attention network were developed to generate these gap-free predictions.
LGHAP v2 provides 22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced the dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework. Data are archived in NetCDF format with individual submissions per year, and Python, MATLAB, R, and IDL codes are provided for reading and visualization.
22-year-long gap-free daily aerosol optical depth and PM2.5 concentration grids at 1-km resolution cover global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset using an improved big earth data analytic framework integrating multimodal satellite, ground monitor, and model data. A scene-aware ensemble learning graph attention network was developed to predict PM2.5 concentrations globally, including in regions with limited in-situ measurements.
LGHAP v2 is a 22-year-long gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering global land areas from 2000 to 2021. It was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. The dataset is archived in NetCDF format with individual yearly submissions, and Python, MATLAB, R, and IDL codes are provided for reading and visualization.
22-year-long daily gap-free grids of aerosol optical depth and near-surface PM2.5 concentrations at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. Data is provided in NetCDF format with Python, MATLAB, R, and IDL codes for reading and visualization.
22-year-long gap-free daily aerosol optical depth and PM2.5 concentration grids at 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University created this dataset using an improved big earth data analytic framework that integrates satellite, ground monitor, and numerical model data. A scene-aware ensemble learning graph attention network was developed to reduce modeling bias in regions with sparse ground measurements.
Global land area daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021 at 1-km resolution. The dataset was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating multimodal satellite, ground monitor, and numerical model data. It is archived in yearly NetCDF files with provided Python, MATLAB, R, and IDL codes for reading and visualization.
22-year-long daily gap-free grids of aerosol optical depth (AOD) and near-surface PM2.5 concentrations at 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
LGHAP v2 is a long-term, gap-free dataset providing daily 1-km resolution grids of aerosol optical depth and near-surface PM2.5 concentrations across global land areas from 2000 to 2021. It was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates satellite, ground monitor, and numerical model data. The dataset is archived in NetCDF format with provided code for reading and visualization.
LGHAP v2 provides 22 years of gap-free daily aerosol optical depth (AOD) and near-surface PM2.5 concentration data at a 1-kilometer resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai at East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
A Long-term Gap-free High-resolution Air Pollutants concentration dataset provides daily, 1-kilometer resolution grids of aerosol optical depth and near-surface PM2.5 concentrations covering global land areas from 2000 to 2021. It was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. The dataset is archived in NetCDF format with helper codes for Python, MATLAB, R, and IDL.
22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids with daily 1-km resolution covering global land area from 2000 to 2021. The dataset was produced by Kaixu Bai at East China Normal University using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and models. Data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
22-year-long gap-free aerosol optical depth and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas. Kaixu Bai from East China Normal University produced this dataset by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. An ensemble learning graph attention network was developed to predict PM2.5 concentrations, especially for regions with limited ground measurements.
22-year-long gap-free daily PM2.5 concentration grids at 1-km resolution cover global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced this dataset using an attention-reinforced tensor construction framework that integrates satellite, ground monitor, and model data. A scene-aware ensemble learning graph attention network (SCAGAT) was developed to reduce modeling bias in regions with sparse ground measurements.
A 22-year-long global land dataset provides daily, 1-km resolution gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids from 2000 to 2021. It was created by Kaixu Bai of East China Normal University using an improved big earth data analytic framework that integrates multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models. The dataset is archived in NetCDF format, with data for each year stored as an individual submission, and includes Python, MATLAB, R, and IDL codes for reading and visualization.
LGHAP v2 provides a 22-year-long, gap-free dataset of aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering global land areas from 2000 to 2021. Kaixu Bai from East China Normal University produced it using an improved big earth data analytic framework integrating multimodal AODs and air quality measurements from satellites, ground monitors, and models. The data is archived in NetCDF format with Python, MATLAB, R, and IDL codes provided for reading and visualization.
LGHAP v2 is a long-term, high-resolution air pollutants concentration dataset from East China Normal University. It provides 22 years of daily, gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration grids at a 1-km resolution covering global land areas from 2000 to 2021. The data was generated by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an improved big earth data analytic framework.
Global land area daily gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data from 2000 to 2021 at 1-km resolution. The dataset was produced by Kaixu Bai of East China Normal University using an improved big earth data analytic framework integrating satellite, ground monitor, and model data. It is provided in NetCDF format with Python, MATLAB, R, and IDL codes for reading and visualization.
22-year-long gap-free aerosol optical depth (AOD) and near-surface PM2.5 concentration data with daily 1-km resolution covering global land areas from 2000 to 2021. The dataset was created by Kaixu Bai of East China Normal University by integrating multimodal AODs and air quality measurements from satellites, ground monitors, and numerical models using an attention-reinforced tensor construction framework. Data for each year is archived in NetCDF format, with Python, MATLAB, R, and IDL codes provided for reading and visualization.