J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
IF 4
3区 地球科学
Q1 GEOSCIENCES, MULTIDISCIPLINARY
引用次数: 0
Abstract
Abstract. New-particle formation from condensable gases is a common atmospheric process that has significant but uncertain effects on aerosol particle number concentrations and aerosol–cloud–climate interactions. Assessing the formation rates of nanometer-sized particles from different vapors is an active field of research within atmospheric sciences, with new data being constantly produced by molecular modeling and experimental studies. Such data can be used in large-scale climate and air quality models through parameterizations or lookup tables. Molecular cluster dynamics modeling, ideally benchmarked against measurements when available for the given precursor vapors, provides a straightforward means to calculate high-resolution formation rate data over wide ranges of atmospheric conditions. Ideally, the incorporation of such data should be easy, efficient and flexible in the sense that same tools can be conveniently applied for different data sets in which the formation rate depends on different parameters. In this work, we present a tool to generate and interpolate lookup tables of formation rates for user-defined input parameters. The table generator primarily applies cluster dynamics modeling to calculate formation rates from an input quantum chemistry data set defined by the user, but the interpolator may also be used for tables generated by other models or data sources. The interpolation routine uses a multivariate interpolation algorithm, which is applicable to different numbers of independent parameters, and gives fast and accurate results with typical interpolation errors of up to a few percent. These routines facilitate the implementation and testing of different aerosol formation rate predictions in large-scale models, allowing the straightforward inclusion of new or updated data without the need to apply separate parameterizations or routines for different data sets that involve different chemical compounds or other parameters.J-GAIN v1.1:一个灵活的工具，将分子模型获得的气溶胶形成率纳入大尺度模型
摘要可冷凝气体形成新粒子是一个常见的大气过程，对气溶胶粒子数量浓度和气溶胶-云-气候相互作用有显著但不确定的影响。评估来自不同蒸汽的纳米级粒子的形成速率是大气科学中一个活跃的研究领域，通过分子建模和实验研究不断产生新的数据。这些数据可通过参数化或查找表用于大尺度气候和空气质量模型。分子簇动力学建模，理想情况下是针对给定前体蒸汽的测量，提供了一种直接的方法来计算大范围大气条件下的高分辨率形成率数据。理想情况下，这些数据的合并应该是简单、有效和灵活的，因为相同的工具可以方便地应用于不同的数据集，其中形成率取决于不同的参数。在这项工作中，我们提出了一个工具来生成和插入用户定义输入参数的形成率查找表。表生成器主要应用聚类动力学建模，从用户定义的输入量子化学数据集计算生成速率，但内插器也可用于由其他模型或数据源生成的表。该插补程序采用多元插补算法，适用于不同数量的独立参数，结果快速准确，典型的插补误差可达几个百分点。这些程序有助于在大尺度模型中实现和测试不同的气溶胶形成率预测，允许直接包含新的或更新的数据，而无需对涉及不同化合物或其他参数的不同数据集应用单独的参数化或程序。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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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.
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