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A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

التفاصيل البيبلوغرافية
العنوان: A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data
المؤلفون: A. Sorrentino, A. Sannino, N. Spinelli, M. Piana, A. Boselli, V. Tontodonato, P. Castellano, X. Wang
المصدر: Atmospheric Measurement Techniques, Vol 15, Pp 149-164 (2022)
بيانات النشر: Copernicus Publications, 2022.
سنة النشر: 2022
المجموعة: LCC:Environmental engineering
LCC:Earthwork. Foundations
مصطلحات موضوعية: Environmental engineering, TA170-171, Earthwork. Foundations, TA715-787
الوصف: We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1867-1381
1867-8548
Relation: https://amt.copernicus.org/articles/15/149/2022/amt-15-149-2022.pdf; https://doaj.org/toc/1867-1381; https://doaj.org/toc/1867-8548
DOI: 10.5194/amt-15-149-2022
URL الوصول: https://doaj.org/article/17b58a357acb4f86b032d6e617eacfae
رقم الأكسشن: edsdoj.17b58a357acb4f86b032d6e617eacfae
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:18671381
18678548
DOI:10.5194/amt-15-149-2022