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DOI | 10.5194/acp-20-8063-2020 |
Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees | |
Ivatt, Peter D.1,2; Evans, Mathew J.1,2 | |
2020-07-13 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2020 |
卷号 | 20期号:13页码:8063-8082 |
文章类型 | Article |
语种 | 英语 |
国家 | England |
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 (O-3, 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 rootmean-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. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000550784200001 |
WOS关键词 | SURFACE OZONE ; TROPOSPHERIC CHEMISTRY ; EMISSIONS ; BIAS ; PERFORMANCE ; METEOROLOGY ; INVENTORY ; FRAMEWORK ; AIRCRAFT ; IMPACT |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/284215 |
专题 | 地球科学 |
作者单位 | 1.Univ York, Dept Chem, Wolfson Atmospher Chem Labs, York YO10 5DD, N Yorkshire, England; 2.Univ York, Dept Chem, Natl Ctr Atmospher Sci, York YO10 5DD, N Yorkshire, England |
推荐引用方式 GB/T 7714 | Ivatt, Peter D.,Evans, Mathew J.. Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2020,20(13):8063-8082. |
APA | Ivatt, Peter D.,&Evans, Mathew J..(2020).Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees.ATMOSPHERIC CHEMISTRY AND PHYSICS,20(13),8063-8082. |
MLA | Ivatt, Peter D.,et al."Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees".ATMOSPHERIC CHEMISTRY AND PHYSICS 20.13(2020):8063-8082. |
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