An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4

IF 4 3区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Model Development Pub Date : 2023-09-19 DOI:10.5194/gmd-16-5323-2023

Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, Andy Delcloo

引用次数: 0

Abstract

Abstract. Wet deposition plays a crucial role in the removal of aerosols from the atmosphere. Yet, large uncertainties remain in its implementation in atmospheric transport models, specifically in the parameterisation schemes that are often used. Recently, a new wet deposition scheme was introduced in FLEXPART. The input parameters for its wet deposition scheme can be altered by the user and may be case-specific. In this paper, a new method is presented to optimise the wet scavenging rates in atmospheric transport models such as FLEXPART. The optimisation scheme is tested in a case study of aerosol-attached 137Cs following the Fukushima Daiichi nuclear power plant accident. From this, improved values for the wet scavenging input parameters in FLEXPART are suggested.

查看原文本刊更多论文

改进大气输送模型中湿沉积建模的优化方法:应用于FLEXPART v10.4

摘要湿沉降在去除大气中的气溶胶方面起着至关重要的作用。然而，在其在大气输送模式中的实施，特别是在经常使用的参数化方案中，仍然存在很大的不确定性。最近，FLEXPART引入了一种新的湿沉积方案。其湿沉积方案的输入参数可以由用户更改，并且可能是具体情况。本文提出了一种优化大气输运模型(FLEXPART)湿清除速率的新方法。该优化方案在福岛第一核电站事故后的气溶胶附着137Cs案例研究中进行了测试。在此基础上，提出了FLEXPART中湿扫输入参数的改进值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-

CiteScore

8.60

自引率

9.80%

发文量

352

审稿时长

6-12 weeks

期刊介绍： Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.