OpenAQ Impact Story: Modeling Predictions: NASA GMAO’s Global Air Quality Forecasting System

NASA’s Global Modeling and Assimilation Office (GMAO) began using data available on OpenAQ back in 2017, when the team had just formed to develop an experimental GEOS Composition Forecast system, a global air quality forecasting system in near real-time. As they were exploring ways to evaluate the forecasts, the global coverage of ground-monitoring, reference-grade data on OpenAQ made it possible to properly evaluate.

“OpenAQ provides global air quality observations available in near-real time on one platform in the same file format, which makes it an ideal tool for validating a global, near-real time air quality model,” explains Dr. Emma Knowland, Universities Space Research Association (USRA) Research Scientist working at NASA GMAO.

The NASA GMAO team alongside other NASA entities including the NASA Atmospheric Chemistry and Dynamics Lab and universities including York University contributed to the model validation and dissemination. The team used data on OpenAQ to evaluate the GEOS-CF surface concentration of ozone, fine particulate matter (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2). As the number of stations on the OpenAQ database grew, they relied more heavily on the OpenAQ Platform to evaluate the global forecasts, specifically evaluating diurnal, weekday/weekend, seasonal, and annual cycles of the four major pollutant concentrations.

Image 1: GEOS-CF model skill scores for the hindcast (forecast day −1) and the 5-days forecasts (forecast day +1 to +5) relative to the ground level AQ data made available from data on OpenAQ. The Boxplots show the variation in the NMB, NRMSE, and R across all surface sites for daily mean O3, NO2, and PM2.5.

As a result of this project, NASA has been able to identify current strengths and weaknesses of the GEOS-CF system from comparisons of GEOS-CF modeled surface concentrations of ozone, PM2.5, SO2 and NO2 to concurrent observations available on the OpenAQ platform. For example, model versus observation comparisons show a persistent high bias of modeled summertime afternoon ozone over the Eastern US, and modeled PM2.5 concentrations in Chinese megacities are biased high while the GEOS-CF significantly underestimates observed PM2.5 concentrations in other locations, such as Mongolia. These findings help define our model development priorities. For instance, they highlight the need to update the team’s PM2.5 emission data in China and Mongolia.

Image 2: Composition Forecast Map of Surface PM2.5 in the East Asia region (NASA GMAO)

“Air pollutant concentrations available on the OpenAQ platform became a key tool for monitoring and evaluating GEOS-CF predicted surface air quality. A platform, such as OpenAQ, compiling publicly available air quality data in near-real time into a publicly available database is essential to the global air quality forecasting community,” explained Dr. Emma Knowland.

NASA GMAO forecasting data plays an integral role in paving the way forward toward more effective air pollution mitigation initiatives and advancing air quality research on a global scale.

References:

Keller, C. A., Knowland, K. E., Duncan, B. N., Liu, J., Anderson, D. C., Das, S., et al. (2021). Description of the NASA GEOS composition forecast modeling system GEOS-CF v1.0. Journal of Advances in Modeling Earth Systems, 13, e2020MS002413. https://doi/10.1029/2020MS002413

Duncan, B. N., Malings, C. A., Knowland, K. E., Anderson, D. C., Prados, A. I., Keller, C. A., etal. (2021). Augmenting the standard operating procedures of health and air quality stakeholders with NASA resources. GeoHealth, 5, e2021GH000451. https://doi.org/10.1029/2021GH000451

To visit the GEOS-CF, visit: https://gmao.gsfc.nasa.gov/weather_prediction/GEOS-CF/