气候变化领域集成服务门户(CCPortal): Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees

CCPortal

DOI	10.5194/acp-20-8063-2020
	Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees
	Ivatt P.D.; Evans M.J.
发表日期	2020
ISSN	1680-7316
起始页码	8063
结束页码	8082
卷号	20 期号:13
英文摘要	Predictions from process-based models of environmental systems are biased, due to uncertainties in their inputs and parameterizations, reducing their utility. We develop a predictor for the bias in tropospheric ozone (O3, a key pollutant) calculated by an atmospheric chemistry transport model (GEOS-Chem), based on outputs from the model and observations of ozone from both the surface (EPA, EMEP, and GAW) and the ozone-sonde networks. We train a gradient-boosted decision tree algorithm (XGBoost) to predict model bias (model divided by observation), with model and observational data for 2010-2015, and then we test the approach using the years 2016-2017. We show that the bias-corrected model performs considerably better than the uncorrected model. The root-mean-square error is reduced from 16.2 to 7.5 ppb, the normalized mean bias is reduced from 0.28 to -0.04, and Pearson's R is increased from 0.48 to 0.84. Comparisons with observations from the NASA ATom flights (which were not included in the training) also show improvements but to a smaller extent, reducing the root-mean-square error (RMSE) from 12.1 to 10.5 ppb, reducing the normalized mean bias (NMB) from 0.08 to 0.06, and increasing Pearson's R from 0.76 to 0.79. We attribute the smaller improvements to the lack of routine observational constraints for much of the remote troposphere. We show that the method is robust to variations in the volume of training data, with approximately a year of data needed to produce useful performance. Data denial experiments (removing observational sites from the algorithm training) show that information from one location (for example Europe) can reduce the model bias over other locations (for example North America) which might provide insights into the processes controlling the model bias. We explore the choice of predictor (bias prediction versus direct prediction) and conclude both may have utility. We conclude that combining machine learning approaches with process-based models may provide a useful tool for improving these models. © 2020 Copernicus GmbH. All rights reserved.
语种	英语
scopus关键词	atmospheric chemistry; atmospheric transport; concentration (composition); gradient analysis; prediction; regression analysis; uncertainty analysis
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/247657
作者单位	Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, YO10 5DD, United Kingdom; National Centre for Atmospheric Science, Department of Chemistry, University of York, York, YO10 5DD, United Kingdom
推荐引用方式 GB/T 7714	Ivatt P.D.,Evans M.J.. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees[J],2020,20(13).
APA	Ivatt P.D.,&Evans M.J..(2020).Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(13).
MLA	Ivatt P.D.,et al."Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.13(2020).