A diagnostic package to facilitate and enhance chemical mechanism implementations within regional and global atmospheric chemistry models

EPA Grant Number: R840011
Title: A diagnostic package to facilitate and enhance chemical mechanism implementations within regional and global atmospheric chemistry models
Investigators: Nicely, Julie M , Keller, Christoph , Follette-Cook, Melanie , Tong, Daniel
Institution: University of Maryland - College Park , Universities Space Research Association , Morgan State University
EPA Project Officer: Chung, Serena
Project Period: August 1, 2020 through July 31, 2023
Project Amount: $796,885
RFA: Chemical Mechanisms to Address New Challenges in Air Quality Modeling (2019) RFA Text |  Recipients Lists
Research Category: Air , Air Quality and Air Toxics

Objective:

The objective of this study is to develop 1) a diagnostic software package, based on machine learning, to identify the key elements of a chemical mechanism to aid the future development of air quality models (AQMs) and 2) a chemical mechanism emulator that can be used as an alternative, computationally inexpensive method to simulate atmospheric chemistry.

Approach:

The proposed work builds on recent findings of the project team, which demonstrated the effectiveness of machine learning algorithms for analyzing, comparing, and emulating chemical mechanisms. Further development of these techniques, alongside creation of a diagnostic suite designed to probe the inner workings of any chemical mechanism of choice, constitute the primary tasks of the project.

Expected Results:

The expected results include a user-friendly software package, consisting of a framework to generate surrogate mechanisms, a set of diagnostic tools to facilitate study of the chemical mechanism, and an emulator interface that can be incorporated into any AQM. This project will greatly simplify application-specific model development, as will be demonstrated using the regional CMAQ and global GEOS-Chem model.

Supplemental Keywords:

atmosphere, air quality, ozone, modeling, chemical sensitivity, model performance, model intercomparison, contiguous US, machine learning