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DOI | 10.5194/acp-18-15879-2018 |
An automatic observation-based aerosol typing method for EARLINET | |
Papagiannopoulos, Nikolaos1; 39;Amico, Giuseppe2 | |
2018-11-06 | |
发表期刊 | ATMOSPHERIC CHEMISTRY AND PHYSICS |
ISSN | 1680-7316 |
EISSN | 1680-7324 |
出版年 | 2018 |
卷号 | 18期号:21页码:15879-15901 |
文章类型 | Article |
语种 | 英语 |
国家 | Italy; Spain; Greece; Netherlands; Germany; Romania; Portugal |
英文摘要 | We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with lit-erature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80 %. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000449479300002 |
WOS关键词 | SPECTRAL-RESOLUTION LIDAR ; MULTIWAVELENGTH RAMAN LIDAR ; EYJAFJALLAJOKULL VOLCANIC CLOUD ; BIOMASS BURNING EPISODE ; LONG-RANGE TRANSPORT ; SAHARAN DUST EVENT ; MICROPHYSICAL PROPERTIES ; OPTICAL-PROPERTIES ; IBERIAN PENINSULA ; DEPOLARIZATION RATIO |
WOS类目 | Environmental Sciences ; Meteorology & Atmospheric Sciences |
WOS研究方向 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/28371 |
专题 | 地球科学 |
作者单位 | 1.CNR, IMAA, I-85050 Tito, PZ, Italy; 2.Univ Politecn Cataluna, CommSensLab, Dept Signal Theory & Commun, Barcelona, Spain; 3.Andalusian Inst Earth Syst Res IISTA CEAMA, Granada 18006, Spain; 4.Univ Granada, Dept Appl Phys, E-18071 Granada, Spain; 5.Natl Observ Athens, IAASARS, Athens, Greece; 6.Natl Tech Univ Athens, Laser Remote Sensing Unit, Dept Phys, Athens, Greece; 7.Royal Netherlands Meteorol Inst KNMI, De Bilt, Netherlands; 8.Leibniz Inst Tropospher Res TROPOS, Leipzig, Germany; 9.Natl Inst R&D Optoelect INOE, Magurele, Romania; 10.Earth Sci Inst ICT, Evora, Portugal; 11.Univ Politecn Cataluna, CTE, CRAE, IEEC, Barcelona, Spain; 12.LMU, Meteorol Inst, Theresienstr 37, D-80333 Munich, Germany |
推荐引用方式 GB/T 7714 | Papagiannopoulos, Nikolaos,39;Amico, Giuseppe. An automatic observation-based aerosol typing method for EARLINET[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2018,18(21):15879-15901. |
APA | Papagiannopoulos, Nikolaos,&39;Amico, Giuseppe.(2018).An automatic observation-based aerosol typing method for EARLINET.ATMOSPHERIC CHEMISTRY AND PHYSICS,18(21),15879-15901. |
MLA | Papagiannopoulos, Nikolaos,et al."An automatic observation-based aerosol typing method for EARLINET".ATMOSPHERIC CHEMISTRY AND PHYSICS 18.21(2018):15879-15901. |
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