气候变化领域集成服务门户(CCPortal): Assimilating spaceborne lidar dust extinction can improve dust forecasts

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DOI	10.5194/acp-22-535-2022
	Assimilating spaceborne lidar dust extinction can improve dust forecasts
	Escribano, Jeronimo; Di Tomaso, Enza; Jorba, Oriol; Klose, Martina; Goncalves Ageitos, Maria; Macchia, Francesca; Amiridis, Vassilis; Baars, Holger; Marinou, Eleni; Proestakis, Emmanouil; Urbanneck, Claudia; Althausen, Dietrich; Buehl, Johannes; Mamouri, Rodanthi-Elisavet; Perez Garcia-Pando, Carlos
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	535
结束页码	560
卷号	22 期号:1 页码:26
英文摘要	Atmospheric mineral dust has a rich tri-dimensional spatial and temporal structure that is poorly constrained in forecasts and analyses when only column-integrated aerosol optical depth (AOD) is assimilated. At present, this is the case of most operational global aerosol assimilation products. Aerosol vertical distributions obtained from spaceborne lidars can be assimilated in aerosol models, but questions about the extent of their benefit upon analyses and forecasts along with their consistency with AOD assimilation remain unresolved. Our study thoroughly explores the added value of assimilating spaceborne vertical dust profiles, with and without the joint assimilation of dust optical depth (DOD). We also discuss the consistency in the assimilation of both sources of information and analyse the role of the smaller footprint of the spaceborne lidar profiles in the results. To that end, we have performed data assimilation experiments using dedicated dust observations for a period of 2 months over northern Africa, the Middle East, and Europe. We assimilate DOD derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-Orbiting Partnership (SUOMI-NPP) Deep Blue and for the first time Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP)-based LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies (LIVAS) pure-dust extinction coefficient profiles on an aerosol model. The evaluation is performed against independent ground-based DOD derived from AErosol RObotic NETwork (AERONET) Sun photometers and ground-based lidar dust extinction profiles from the Cyprus Clouds Aerosol and Rain Experiment (CyCARE) and PREparatory: does dust TriboElectrification affect our ClimaTe (Pre-TECT) field campaigns. Jointly assimilating LIVAS and Deep Blue data reduces the root mean square error (RMSE) in the DOD by 39 % and in the dust extinction coefficient by 65 % compared to a control simulation that excludes assimilation. We show that the assimilation of dust extinction coefficient profiles provides a strong added value to the analyses and forecasts. When only Deep Blue data are assimilated, the RMSE in the DOD is reduced further, by 42 %. However, when only LIVAS data are assimilated, the RMSE in the dust extinction coefficient decreases by 72 %, the largest improvement across experiments. We also show that the assimilation of dust extinction profiles yields better skill scores than the assimilation of DOD under an equivalent sensor footprint. Our results demonstrate the strong potential of future lidar space missions to improve desert dust forecasts, particularly if they foresee a depolarization lidar channel to allow discrimination of desert dust from other aerosol types.
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:000746442100001
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/273869
作者单位	Universitat Politecnica de Catalunya; Barcelona Supercomputer Center (BSC-CNS); Helmholtz Association; Karlsruhe Institute of Technology; Universitat Politecnica de Catalunya; National Observatory of Athens; Leibniz Institut fur Tropospharenforschung (TROPOS); Helmholtz Association; German Aerospace Centre (DLR); ICREA
推荐引用方式 GB/T 7714	Escribano, Jeronimo,Di Tomaso, Enza,Jorba, Oriol,et al. Assimilating spaceborne lidar dust extinction can improve dust forecasts[J],2022,22(1):26.
APA	Escribano, Jeronimo.,Di Tomaso, Enza.,Jorba, Oriol.,Klose, Martina.,Goncalves Ageitos, Maria.,...&Perez Garcia-Pando, Carlos.(2022).Assimilating spaceborne lidar dust extinction can improve dust forecasts.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(1),26.
MLA	Escribano, Jeronimo,et al."Assimilating spaceborne lidar dust extinction can improve dust forecasts".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.1(2022):26.