气候变化领域集成服务门户(CCPortal): Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results

CCPortal

DOI	10.5194/acp-22-15313-2022
	Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results
	Bernier, Claudia; Wang, Yuxuan; Gronoff, Guillaume; Berkoff, Timothy; Knowland, K. Emma; Sullivan, John T.; Delgado, Ruben; Caicedo, Vanessa; Carroll, Brian
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	15313
结束页码	15331
卷号	22 期号:23 页码:19
英文摘要	Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a mesoscale and have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multi-dimensional measurements from ozone lidar in conjunction with both an offline GEOS-Chem chemical-transport model (CTM) simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS)-1, 2018 OWLETS-2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in five ozone profile curtain clusters. The established five clusters all varied significantly in ozone magnitude vertically and temporally, which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. An overall evaluation of the models reveals good agreement (R approximate to 0.70) in the low-level altitude range (0 to 2000 m), with a low and unsystematic bias for GEOS-Chem and a high systemic positive bias for GEOS-CF. The mid-level (2000-4000 m) performances show a high systematic negative bias for GEOS-Chem and an overall low unsystematic bias for GEOS-CF and a generally weak agreement to the lidar observations (R=0.12 and 0.22, respectively). Evaluating cluster-by-cluster model performance reveals additional model insight that is overlooked in the overall model performance. Utilizing the full vertical and diurnal ozone distribution information specific to lidar measurements, this work provides new insights on model proficiency in complex coastal regions.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000912575100001
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/273318
作者单位	University of Houston System; University of Houston; National Aeronautics & Space Administration (NASA); NASA Langley Research Center; Science Systems and Applications Inc; National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; Morgan State University; University System of Maryland; University of Maryland Baltimore County; University System of Maryland; University of Maryland Baltimore County
推荐引用方式 GB/T 7714	Bernier, Claudia,Wang, Yuxuan,Gronoff, Guillaume,et al. Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results[J],2022,22(23):19.
APA	Bernier, Claudia.,Wang, Yuxuan.,Gronoff, Guillaume.,Berkoff, Timothy.,Knowland, K. Emma.,...&Carroll, Brian.(2022).Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(23),19.
MLA	Bernier, Claudia,et al."Cluster-based characterization of multi-dimensional tropospheric ozone variability in coastal regions: an analysis of lidar measurements and model results".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.23(2022):19.