气候变化领域集成服务门户(CCPortal): Toward Stable, General Machine-Learned Models of the Atmospheric Chemical System

CCPortal

DOI	10.1029/2020JD032759
	Toward Stable, General Machine-Learned Models of the Atmospheric Chemical System
	Kelp M.M.; Jacob D.J.; Kutz J.N.; Marshall J.D.; Tessum C.W.
发表日期	2020
ISSN	2169897X
卷号	125 期号:23
英文摘要	Atmospheric chemistry models—components in models that simulate air pollution and climate change—are computationally expensive. Previous studies have shown that machine-learned atmospheric chemical solvers can be orders of magnitude faster than traditional integration methods but tend to suffer from numerical instability. Here, we present a modeling framework that reduces error accumulation compared to previous work while maintaining computational efficiency. Our approach is novel in that it (1) uses a recurrent training regime that results in extended (>1 week) simulations without exponential error accumulation and (2) can reversibly compress the number of modeled chemical species by >80% without further decreasing accuracy. We observe an ~260× speedup (~1,900× with specialized hardware) compared to the traditional solver. We use random initial conditions in training to promote general applicability across a wide range of atmospheric conditions. For ozone (concentrations ranging from 0–70 ppb), our model predictions over a 24-hr simulation period match those of the reference solver with median error of 2.7 and <19 ppb error across 99% of simulations initialized with random noise. Error can be significantly higher in the remaining 1% of simulations, which include extreme concentration fluctuations simulated by the reference model. Results are similar for total particulate matter (median error of 16 and <32 μg/m3 across 99% of simulations with concentrations ranging from 0–150 μg/m3). Finally, we discuss practical implications of our modeling framework and next steps for improvements. The machine learning models described here are not yet replacements for traditional chemistry solvers but represent a step toward that goal. © 2020. American Geophysical Union. All Rights Reserved.
英文关键词	atmospheric chemical mechanism; chemical mechanism; machine learning; model emulation; surrogate model
语种	英语
来源期刊	Journal of Geophysical Research: Atmospheres
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/185626
作者单位	Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States; Department of Applied Mathematics, University of Washington, Seattle, WA, United States; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States; Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
推荐引用方式 GB/T 7714	Kelp M.M.,Jacob D.J.,Kutz J.N.,et al. Toward Stable, General Machine-Learned Models of the Atmospheric Chemical System[J],2020,125(23).
APA	Kelp M.M.,Jacob D.J.,Kutz J.N.,Marshall J.D.,&Tessum C.W..(2020).Toward Stable, General Machine-Learned Models of the Atmospheric Chemical System.Journal of Geophysical Research: Atmospheres,125(23).
MLA	Kelp M.M.,et al."Toward Stable, General Machine-Learned Models of the Atmospheric Chemical System".Journal of Geophysical Research: Atmospheres 125.23(2020).