气候变化领域集成服务门户(CCPortal): Data assimilation of volcanic aerosol observations using FALL3D+PDAF

CCPortal

DOI	10.5194/acp-22-1773-2022
	Data assimilation of volcanic aerosol observations using FALL3D+PDAF
	Mingari, Leonardo; Folch, Arnau; Prata, Andrew T.; Pardini, Federica; Macedonio, Giovanni; Costa, Antonio
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	1773
结束页码	1792
卷号	22 期号:3 页码:20
英文摘要	Modelling atmospheric dispersal of volcanic ash and aerosols is becoming increasingly valuable for assessing the potential impacts of explosive volcanic eruptions on buildings, air quality, and aviation. Management of volcanic risk and reduction of aviation impacts can strongly benefit from quantitative forecasting of volcanic ash. However, an accurate prediction of volcanic aerosol concentrations using numerical modelling relies on proper estimations of multiple model parameters which are prone to errors. Uncertainties in key parameters such as eruption column height and physical properties of particles or meteorological fields represent a major source of error affecting the forecast quality. The availability of near-real-time geostationary satellite observations with high spatial and temporal resolutions provides the opportunity to improve forecasts in an operational context by incorporating observations into numerical models. Specifically, ensemble-based filters aim at converting a prior ensemble of system states into an analysis ensemble by assimilating a set of noisy observations. Previous studies dealing with volcanic ash transport have demonstrated that a significant improvement of forecast skill can be achieved by this approach. In this work, we present a new implementation of an ensemble-based data assimilation (DA) method coupling the FALL3D dispersal model and the Parallel Data Assimilation Framework (PDAF). The FALL3D+PDAF system runs in parallel, supports online-coupled DA, and can be efficiently integrated into operational workflows by exploiting high-performance computing (HPC) resources. Two numerical experiments are considered: (i) a twin experiment using an incomplete dataset of synthetic observations of volcanic ash and (ii) an experiment based on the 2019 Raikoke eruption using real observations of SO2 mass loading. An ensemble-based Kalman filtering technique based on the local ensemble transform Kalman filter (LETKF) is used to assimilate satellite-retrieved data of column mass loading. We show that this procedure may lead to nonphysical solutions and, consequently, conclude that LETKF is not the best approach for the assimilation of volcanic aerosols. However, we find that a truncated state constructed from the LETKF solution approaches the real solution after a few assimilation cycles, yielding a dramatic improvement of forecast quality when compared to simulations without assimilation.
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:000758101900001
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/273865
作者单位	Universitat Politecnica de Catalunya; Barcelona Supercomputer Center (BSC-CNS); University of Oxford; Istituto Nazionale Geofisica e Vulcanologia (INGV); Istituto Nazionale Geofisica e Vulcanologia (INGV); Istituto Nazionale Geofisica e Vulcanologia (INGV)
推荐引用方式 GB/T 7714	Mingari, Leonardo,Folch, Arnau,Prata, Andrew T.,et al. Data assimilation of volcanic aerosol observations using FALL3D+PDAF[J],2022,22(3):20.
APA	Mingari, Leonardo,Folch, Arnau,Prata, Andrew T.,Pardini, Federica,Macedonio, Giovanni,&Costa, Antonio.(2022).Data assimilation of volcanic aerosol observations using FALL3D+PDAF.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(3),20.
MLA	Mingari, Leonardo,et al."Data assimilation of volcanic aerosol observations using FALL3D+PDAF".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.3(2022):20.