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Description

The next generation of sensors in geostationary orbit offer unprecedented temporal resolution for air quality observations. Low Earth orbit (LEO) satellites such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Ozone Monitoring Instrument (OMI), and Tropospheric Monitoring Instrument (TROPOMI) can provide global coverage, but typically observe a given location one to two times per day. Sensors in geostationary orbit (GEO) observe the same geographic region at all times of the day. These datasets are essential for understanding diurnal changes in air quality, monitoring real-time movement of smoke and dust events, and improving model forecasting capabilities via data assimilation.

This Applied Remote Sensing Program (ARSET) training is in partnership with the National Oceanic and Atmospheric Administration (NOAA) and the National Institute Of Environmental Research (NIER, South Korea) on air quality (AQ) data analysis from geostationary satellites. The training sessions provide an overview of geostationary capabilities for monitoring air quality around the world; introduce geostationary aerosol datasets from GOES-East, GOES-West, Himawari 8, and the Geostationary Environment Monitoring Spectrometer (GEMS); and present data access and Python tools to read and analyze the datasets.

Access the Agenda (PDF, 0.8MB)

Prerequisites

Objectives

By the end of this training, attendees will have:

  • An understanding of aerosol and trace gas datasets from geostationary satellites.
  • The capability to access, visualize, and download datasets.
  • Python scripts to read and analyze air quality datasets.

Target Audience

This training is intended for AQ forecasters, the AQ modeling and monitoring community, and local, state, and federal agencies.

Course Format

  • Three, 2-hour parts

Sessions

Part 1: Introduction to Geostationary AQ Observations and AQ Products from Himawari

Tuesday, Oct. 11, 2022

  • Introduction to air quality observations from geostationary satellites.
  • Differences and similarities between LEO and GEO observations.
  • GOES and Himawari true color images and loops - Worldview Exercise.
  • Tour of P-Tree visualization tool for Himawari-8 data.
  • Future AQ GEO missions.
  • Introduction to the Tropospheric Emissions: Monitoring of Pollution (TEMPO) Mission.

ARSET Instructors: Pawan Gupta, Melanie Follette-Cook, and Sarah Strode

Guest Instructor: Aaron Naeger

Materials

Part 2: AQ Products from GOES

Tuesday, Oct. 18, 2022

  • Introduction to NOAA’s GEO aerosol products - algorithms and validation.
  • Dataset details (files, frequency, parameters), access from NOAA's GOES-R archive on AWS S3.
  • Python Jupyter notebooks to read, map, and extract aerosol datasets.
  • Tour of NOAA Aerosol Watch website.

ARSET Instructors: Pawan Gupta, Melanie Follette-Cook, and Sarah Strode

Materials

Part 3: AQ Products from GEMS

Tuesday, Oct. 25, 2022

  • Introduction to the GEMS mission.
  • GEMS AQ datasets - algorithms and validation.
  • GEMS AQ data access.
  • Python Jupyter notebooks exercise to read, map, and analyze GEMS data.

ARSET Instructors: Pawan Gupta, Melanie Follette-Cook, and Sarah Strode

Guest Instructors: Sujung Go (UMBC/NASA GSFC), and Jhoon Kim

Materials

Citation

(2022). ARSET - Accessing and Analyzing Air Quality Data from Geostationary Satellites. NASA Applied Remote Sensing Training Program (ARSET). https://www.earthdata.nasa.gov/learn/trainings/accessing-analyzing-air-quality-data-from-geostationary-satellites. 

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Details

Last Updated

Sept. 8, 2025

Published

Sept. 8, 2025

Data Center/Project

Applied Remote Sensing Training Program (ARSET)