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Data Quality Enhancement for Atmospheric Chemistry Field Experiments via Sequential Monte Carlo Filters
Atmospheric Measurement Techniques ( IF 3.2 ) Pub Date : 2022-11-14 , DOI: 10.5194/egusphere-2022-1080
Lenard L. Röder , Patrick Dewald , Clara M. Nussbaumer , Jan Schuladen , John N. Crowley , Jos Lelieveld , Horst Fischer

Abstract. In this study we explore the applications and limitations of Sequential Monte Carlo filters (SMC) to atmospheric chemistry field experiments. The proposed algorithm is simple, fast, versatile and returns a complete probability distribution. It combines information from measurements with known system dynamics to decrease the uncertainty of measured variables. The method shows high potential to increase data coverage, precision and even possibilities to infer unmeasured variables. We extend the original SMC algorithm with an activity variable that gates the proposed reactions. This extension makes the algorithm more robust when dynamical processes not considered in the calculation dominate and the information provided via measurements is limited. The activity variable also provides a quantitative measure of the dominant processes. Free parameters of the algorithm and their effect on the SMC result are analyzed. The algorithm reacts very sensitively to the estimated speed of stochastic variation. We provide a scheme to choose this value appropriately. In a simulation study O3, NO, NO2 and jNO2 are tested for interpolation and de-noising using measurement data of a field campaign. Generally, the SMC method performs well under most conditions, with some dependence on the particular variable being analyzed.

中文翻译:

通过顺序蒙特卡罗过滤器提高大气化学现场实验的数据质量,通过顺序蒙特卡罗过滤器提高大气化学现场实验的数据质量

摘要。在这项研究中,我们探讨了顺序蒙特卡洛滤波器 (SMC) 在大气化学现场实验中的应用和局限性。所提出的算法简单、快速、通用并返回完整的概率分布。它将来自测量的信息与已知的系统动力学相结合,以降低测量变量的不确定性。该方法显示出提高数据覆盖率、精度甚至推断未测量变量的可能性的巨大潜力。我们用一个活动变量来扩展原始的 SMC 算法,该变量可以控制所提出的反应。当计算中未考虑的动态过程占主导地位并且通过测量提供的信息有限时,此扩展使算法更加稳健。活动变量还提供了主要过程的定量测量。分析了算法的自由参数及其对SMC结果的影响。该算法对随机变化的估计速度非常敏感。我们提供了一个方案来适当地选择这个值。在模拟研究中 O如图3所示,利用野外活动的测量数据对NO、NO 2j NO 2进行插值和去噪测试。通常,SMC 方法在大多数条件下表现良好,但对所分析的特定变量有一定的依赖性。,抽象的。在这项研究中,我们探讨了顺序蒙特卡洛滤波器 (SMC) 在大气化学现场实验中的应用和局限性。所提出的算法简单、快速、通用并返回完整的概率分布。它将来自测量的信息与已知的系统动力学相结合,以降低测量变量的不确定性。该方法显示出提高数据覆盖率、精度甚至推断未测量变量的可能性的巨大潜力。我们用一个活动变量来扩展原始的 SMC 算法,该变量可以控制所提出的反应。当计算中未考虑的动态过程占主导地位并且通过测量提供的信息有限时,此扩展使算法更加稳健。活动变量还提供了主要过程的定量测量。分析了算法的自由参数及其对SMC结果的影响。该算法对随机变化的估计速度非常敏感。我们提供了一个方案来适当地选择这个值。在模拟研究中 O如图3所示,利用野外活动的测量数据对NO、NO 2j NO 2进行插值和去噪测试。通常,SMC 方法在大多数条件下表现良好,但对所分析的特定变量有一定的依赖性。
更新日期:2022-11-14
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