Study on Icing Environment Judgment Based on Radar Data

As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machin...

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Autores principales:	Jinhu Wang, Binze Xie, Jiahan Cai, Yuhao Wang, Jiang Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	aviation safety icing index radar data linear regression clustering analysis neural network Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/2286be56e9784ee594cafb37d31b92d7
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id	oai:doaj.org-article:2286be56e9784ee594cafb37d31b92d7
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spelling	oai:doaj.org-article:2286be56e9784ee594cafb37d31b92d72021-11-25T16:45:57ZStudy on Icing Environment Judgment Based on Radar Data10.3390/atmos121115342073-4433https://doaj.org/article/2286be56e9784ee594cafb37d31b92d72021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1534https://doaj.org/toc/2073-4433As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machine learning methods are used to establish the relationship between radar data and icing index (Ic) to determine the ice accumulation environment. The research shows the following. (1) A linear model was established, based on the scattering rate factor (Zh), radial velocity (v), spectral width (w), velocity standard deviation (σ) detected by 94 GHz millimeter wave radar, and backward attenuation coefficient (β) detected by 905 nm lidar, so linear regression was carried out. After principal component analysis (PCA), the correction determination coefficient of the linear equation was increased from 0.7127 to 0.7240. (2) Ice accumulation was unlikely for samples that were significantly off-center. By clustering the data into three or four categories, the proportion of icing lattice points could be increased from 18.81% to 33.03%. If the clustering number was further increased, the ice accumulation ratio will not be further increased, and the increased classification is reflected in the classification of pairs of noises and the possibility of omission is also increased. (3) Considering the classification and nonlinear factors of ice accumulation risk, the neural network method was used to judge the ice accumulation environment. Two kinds of neural network structures were established for quantitative calculation: Structure 1 first distinguished whether there was ice accumulation, and further calculated the icing index for the points where there was ice accumulation; Structure 2 directly calculated the temperature and relative humidity, and calculated the icing index according to definition. The accuracy of the above two structures could reach nearly 60%, but the quantitative judgment of the ice accumulation index was not ideal. The reasons for this dissatisfaction may be the small number of variables and samples, the interval between time and space, the difference in instrument detection principle, and the representativeness of the ice accumulation index. Further research can be improved from the above four points. This study can provide a theoretical basis for the diagnosis and analysis of the aircraft ice accumulation environment.Jinhu WangBinze XieJiahan CaiYuhao WangJiang ChenMDPI AGarticleaviation safetyicing indexradar datalinear regressionclustering analysisneural networkMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1534, p 1534 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	aviation safety icing index radar data linear regression clustering analysis neural network Meteorology. Climatology QC851-999
spellingShingle	aviation safety icing index radar data linear regression clustering analysis neural network Meteorology. Climatology QC851-999 Jinhu Wang Binze Xie Jiahan Cai Yuhao Wang Jiang Chen Study on Icing Environment Judgment Based on Radar Data
description	As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machine learning methods are used to establish the relationship between radar data and icing index (Ic) to determine the ice accumulation environment. The research shows the following. (1) A linear model was established, based on the scattering rate factor (Zh), radial velocity (v), spectral width (w), velocity standard deviation (σ) detected by 94 GHz millimeter wave radar, and backward attenuation coefficient (β) detected by 905 nm lidar, so linear regression was carried out. After principal component analysis (PCA), the correction determination coefficient of the linear equation was increased from 0.7127 to 0.7240. (2) Ice accumulation was unlikely for samples that were significantly off-center. By clustering the data into three or four categories, the proportion of icing lattice points could be increased from 18.81% to 33.03%. If the clustering number was further increased, the ice accumulation ratio will not be further increased, and the increased classification is reflected in the classification of pairs of noises and the possibility of omission is also increased. (3) Considering the classification and nonlinear factors of ice accumulation risk, the neural network method was used to judge the ice accumulation environment. Two kinds of neural network structures were established for quantitative calculation: Structure 1 first distinguished whether there was ice accumulation, and further calculated the icing index for the points where there was ice accumulation; Structure 2 directly calculated the temperature and relative humidity, and calculated the icing index according to definition. The accuracy of the above two structures could reach nearly 60%, but the quantitative judgment of the ice accumulation index was not ideal. The reasons for this dissatisfaction may be the small number of variables and samples, the interval between time and space, the difference in instrument detection principle, and the representativeness of the ice accumulation index. Further research can be improved from the above four points. This study can provide a theoretical basis for the diagnosis and analysis of the aircraft ice accumulation environment.
format	article
author	Jinhu Wang Binze Xie Jiahan Cai Yuhao Wang Jiang Chen
author_facet	Jinhu Wang Binze Xie Jiahan Cai Yuhao Wang Jiang Chen
author_sort	Jinhu Wang
title	Study on Icing Environment Judgment Based on Radar Data
title_short	Study on Icing Environment Judgment Based on Radar Data
title_full	Study on Icing Environment Judgment Based on Radar Data
title_fullStr	Study on Icing Environment Judgment Based on Radar Data
title_full_unstemmed	Study on Icing Environment Judgment Based on Radar Data
title_sort	study on icing environment judgment based on radar data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2286be56e9784ee594cafb37d31b92d7
work_keys_str_mv	AT jinhuwang studyonicingenvironmentjudgmentbasedonradardata AT binzexie studyonicingenvironmentjudgmentbasedonradardata AT jiahancai studyonicingenvironmentjudgmentbasedonradardata AT yuhaowang studyonicingenvironmentjudgmentbasedonradardata AT jiangchen studyonicingenvironmentjudgmentbasedonradardata
_version_	1718413005390086144

Study on Icing Environment Judgment Based on Radar Data

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