This 3-part training will 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.
If you would like to join us or pass along to colleagues who will find it useful, please do so. Please see the training details and registration information below.
Accessing and Analyzing Air Quality Data from Geostationary Satellites
The next generation of sensors in geostationary orbit offer unprecedented temporal resolution for air quality observations. Low Earth orbit satellites (e.g., MODIS, VIIRS, OMI, TROPOMI) can provide global coverage, but typically observe a given location one to two times per day. Sensors in geostationary orbit 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 will be a three-part webinar series 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 webinar series will a) provide an overview of geostationary capabilities for monitoring air quality around the world; b) introduce geostationary aerosol datasets from GOES-East, GOES-West, Himawari 8, and the Geostationary Environment Monitoring Spectrometer (GEMS); and 3) present data access and python tools to read and analyze the datasets.
Relevant UN Sustainable Development Goals:
- Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contamination
Course Dates: October 11, 18, and 25, 2022
Time: 10:00-12:00 EDT (UTC-4)
Register Here: https://go.nasa.gov/3bpVOZd
Learning 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
Course Format: Three, 2 hour parts
Twitter announcement: https://twitter.com/NASAARSET/status/1557357066305544194