Difference Learning for Air Quality Forecasting Transport Emulation
Abstract: Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species.
- Office Air Quality Planning and U.S.Environmental Protection Agency Standards “Technical Assistance Document for the Reporting of Daily Air Quality – the Air Quality Index (AQI)”, 2018 URL: https://www.airnow.gov/sites/default/files/2020-05/aqi-technical-assistance-document-sept2018.pdf
- “Long-term changes in extreme air pollution meteorology and the implications for air quality” In Scientific reports 6.1 Nature Publishing Group, 2016, pp. 1–9
- “Wildfire and prescribed burning impacts on air quality in the United States” In Journal of the Air & Waste Management Association 70.6 Taylor & Francis, 2020, pp. 583–615
- Makoto M Kelp, Christopher W Tessum and Julian D Marshall “Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation” In arXiv preprint arXiv:1808.03874, 2018
- “Learning skillful medium-range global weather forecasting” In Science 382.6677, 2023, pp. 1416–1421 DOI: 10.1126/science.adi2336
- Philip J Landrigan “Air pollution and health” In The Lancet Public Health 2.1 Elsevier, 2017, pp. e4–e5
- “Deep learning for air quality forecasts: a review” In Current Pollution Reports 6 Springer, 2020, pp. 399–409
- Julia Ling, Reese Jones and Jeremy Templeton “Global impact of landscape fire emissions on surface level PM2. 5 concentrations, air quality exposure and population mortality” In Journal of Computational Physics 318 Elsevier, 2016, pp. 22–35
- Mojtaba Moghani and Cristina L Archer “The impact of emissions and climate change on future ozone concentrations in the USA” In Air Quality, Atmosphere & Health 13 Springer, 2020, pp. 1465–1476
- “Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators” In arXiv preprint arXiv:2202.11214, 2022
- “Data-driven medium-range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench” In Journal of Advances in Modeling Earth Systems 13.2 Wiley Online Library, 2021, pp. e2020MS002405
- “Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution” In arXiv preprint arXiv:2008.08626, 2020
- Olaf Ronneberger, Philipp Fischer and Thomas Brox “U-net: Convolutional networks for biomedical image segmentation” In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 2015, pp. 234–241 Springer
- Sebastian Scher “Toward data-driven weather and climate forecasting: Approximating a simple general circulation model with deep learning” In Geophysical Research Letters 45.22 Wiley Online Library, 2018, pp. 12–616
- “A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0. 0 and 12.9. 1” In Geoscientific Model Development 15.4 Copernicus GmbH, 2022, pp. 1677–1687
- “Feature Engineering of Weather Data for Short-Term Energy Consumption Forecast” In 2021 IEEE Madrid PowerTech, 2021, pp. 1–6 DOI: 10.1109/PowerTech46648.2021.9494920
- “Artificial Intelligence Air Quality Forecast Emulation of Atmospheric Tracers” In 103rd AMS Annual Meeting, 2023 AMS
- “The Integration of Artificial Intelligence for Improved Operational Air Quality Forecasting” In AGU Fall Meeting Abstracts 2021, 2021, pp. A15E–1680
- “Development of Next-Generation Air Quality Predictions for the United States in the Unified Forecast System” In 103rd AMS Annual Meeting, 2023 AMS
- Jonathan A Weyn, Dale R Durran and Rich Caruana “Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere” In Journal of Advances in Modeling Earth Systems 12.9 Wiley Online Library, 2020, pp. e2020MS002109
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
