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Description

This advanced webinar series will build the capacity to apply Python codes and other online tools to read, map, and analyze datasets from NASA Goddard Earth Observing System (GEOS) air quality forecasts as well as products from the second Modern-Era Retrospective analysis for Research and Application (MERRA-2) reanalysis. The training will run three sessions, with each session two hours long. The sessions will include lectures and hands-on activities, including exercises, interacting with web-based and offline python tools, and time to perform analyses.

Agenda

Objective

By the end of this training attendees will be able to:

  • Download, map, and subset GEOS outputs using tools developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC)
  • Perform intercomparison and evaluation of analysis and forecast output using Jupyter notebooks
  • Perform air quality assessment using reanalysis and forecasts from GEOS, ground and satellite measurements
Audience

This training is primarily intended for local, regional, state, federal, and international organizations involved in air quality management and forecasting, and those with an interest in applying remote sensing and global models for air quality applications.

Course Format
  • Three sessions, each two hours long
  • Lecture and Hands-on Lab time (Advanced)
Sessions
Part 1 - Review of NASA Air Quality Forecasts and Reanalysis
10:00 am ~ 12:00 pm
EST (UTC-5:00)

  • GEOS FP and GEOS-CF forecasts, and MERRA-2 Reanalysis
  • GES DISC tools for visualization and analysis

Materials:

Part 2 - Introduction to Python Tools for Visualization and Analysis
10:00 am ~ 12:00 pm
EST (UTC-5:00)

  • Hands-On Exercise: Intercomparison with Satellite Observations
  • Hands-On Exercise: Validation with Surface Observations

Materials:

Part 3 - Interpreting Model Output for Air Quality Assessment
10:00 am ~ 12:00 pm
EST (UTC-5:00)

  • Case Study Analysis: Instructor-led exercise to combine data and apply tools to assess forecast and analysis performance

Materials:

Prerequisites

 

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