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Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine LearningAtmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.
Document ID
20190025185
Acquisition Source
Goddard Space Flight Center
Document Type
Presentation
Authors
Keller, Christoph A.
(Universities Space Research Association (USRA) Greenbelt, MD, United States)
Evans, Mat J.
(New York Univ. New York, NY, United States)
Date Acquired
May 16, 2019
Publication Date
April 23, 2019
Subject Category
Geosciences (General)
Report/Patent Number
GSFC-E-DAA-TN68395
Meeting Information
Meeting: Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction
Location: College Park, MD
Country: United States
Start Date: April 23, 2019
End Date: April 25, 2019
Sponsors: National Oceanic and Atmospheric Administration
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
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