气候变化领域集成服务门户(CCPortal): Simulation of chemical transport model estimates by means of a neural network using meteorological data

CCPortal

DOI	10.1016/j.atmosenv.2021.118236
	Simulation of chemical transport model estimates by means of a neural network using meteorological data
	Vlasenko A.; Matthias V.; Callies U.
发表日期	2021
ISSN	1352-2310
卷号	254
英文摘要	Chemical substances of either anthropogenic or natural origin affect air quality and, as a consequence, also the health of the population. Therefore, there is a high demand for reliable air quality scenarios that can support possible management decisions. However, generating long term assessments of air quality assuming different emission scenarios is still a great challenge when using detailed atmospheric chemistry models. In this study, we test machine learning technique based on neural networks (NN) to emulate process-oriented modeling outcomes. A successfully calibrated NN might estimate concentrations of chemical substances in the air several orders faster than the original model and with reasonably small errors. We designed a simple recurrent 3-layer NN to reproduce daily mean concentrations of NO2, SO2 and C2H6 over Europe as simulated by the Community Multiscale Air Quality model (CMAQ). The general structure of the NN can be shown to approximate a continuity equation. Inputs of the network are daily mean meteorological state variables, taken from the climate model COSMO-CLM. The proposed NN emulates CMAQ outputs with an error not exceeding the difference between CMAQ and other known chemical transport models. © 2021 The Authors
关键词	Artificial intelligence Atmospheric modeling Chemical scenario forecasting Chemical transport models Ethane Neural network Nitrogen dioxide Sulfur dioxide
语种	英语
scopus关键词	Air quality; Atmospheric chemistry; Atmospheric movements; Climate models; Learning systems; Meteorology; Neural networks; Sulfur dioxide; Anthropogenics; Atmospheric model; Chemical scenario forecasting; Chemical substance; Chemical transport models; Community multi-scale air quality models; Meteorological data; Model estimates; Neural-networks; Nitrogen dioxides; Nitrogen oxides; carbon; ethane; hydrogen; nitrogen dioxide; sulfur dioxide; air quality; artificial neural network; atmospheric modeling; climate modeling; concentration (composition); data set; public health; simulation; air quality; algorithm; artificial neural network; calculation; chemical reaction; chemical structure; community multiscale air quality model; Europe; human; meteorology; model; priority journal; recurrent neural network; seasonal variation; sensitivity analysis; simulation; spring; summer; surface property; validation study; wind speed; winter; Europe
来源期刊	ATMOSPHERIC ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/248466
作者单位	Helmholtz Zentrum Geesthacht, Max-Planck-Straße 1, Geesthacht, 21502, Germany
推荐引用方式 GB/T 7714	Vlasenko A.,Matthias V.,Callies U.. Simulation of chemical transport model estimates by means of a neural network using meteorological data[J],2021,254.
APA	Vlasenko A.,Matthias V.,&Callies U..(2021).Simulation of chemical transport model estimates by means of a neural network using meteorological data.ATMOSPHERIC ENVIRONMENT,254.
MLA	Vlasenko A.,et al."Simulation of chemical transport model estimates by means of a neural network using meteorological data".ATMOSPHERIC ENVIRONMENT 254(2021).