A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data
<p>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 dis...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/15/149/2022/amt-15-149-2022.pdf |
| _version_ | 1828147425827618816 |
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| author | A. Sorrentino A. Sannino N. Spinelli M. Piana A. Boselli V. Tontodonato P. Castellano X. Wang |
| author_facet | A. Sorrentino A. Sannino N. Spinelli M. Piana A. Boselli V. Tontodonato P. Castellano X. Wang |
| author_sort | A. Sorrentino |
| collection | DOAJ |
| description | <p>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 PM<span class="inline-formula"><sub>1</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> concentrations are reconstructed with tolerable deviations.</p> |
| first_indexed | 2024-04-11T21:00:46Z |
| format | Article |
| id | doaj.art-17b58a357acb4f86b032d6e617eacfae |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-11T21:00:46Z |
| publishDate | 2022-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-17b58a357acb4f86b032d6e617eacfae2022-12-22T04:03:30ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482022-01-011514916410.5194/amt-15-149-2022A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar dataA. Sorrentino0A. Sannino1N. Spinelli2M. Piana3A. Boselli4V. Tontodonato5P. Castellano6X. Wang7Dipartimento di Matematica, Università di Genova, Genova, ItalyDipartimento di Fisica, Università di Napoli Federico II, Napoli, ItalyDipartimento di Fisica, Università di Napoli Federico II, Napoli, ItalyDipartimento di Matematica, Università di Genova, Genova, ItalyCNR-IMAA, Potenza, ItalyALA Srl Advanced Lidar Applications, Napoli, ItalyALA Srl Advanced Lidar Applications, Napoli, ItalyCNR-SPIN, Napoli, Italy<p>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 PM<span class="inline-formula"><sub>1</sub></span>, PM<span class="inline-formula"><sub>2.5</sub></span> and PM<span class="inline-formula"><sub>10</sub></span> concentrations are reconstructed with tolerable deviations.</p>https://amt.copernicus.org/articles/15/149/2022/amt-15-149-2022.pdf |
| spellingShingle | A. Sorrentino A. Sannino N. Spinelli M. Piana A. Boselli V. Tontodonato P. Castellano X. Wang A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data Atmospheric Measurement Techniques |
| title | A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| title_full | A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| title_fullStr | A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| title_full_unstemmed | A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| title_short | A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| title_sort | bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data |
| url | https://amt.copernicus.org/articles/15/149/2022/amt-15-149-2022.pdf |
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