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首页> 外文期刊>Aerospace science and technology >Towards analysis and predicting maps of ultraviolet index from experimental astronomical parameters (solar elevation, total ozone level, aerosol index, reflectivity). Artificial neural networks global scale approach
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Towards analysis and predicting maps of ultraviolet index from experimental astronomical parameters (solar elevation, total ozone level, aerosol index, reflectivity). Artificial neural networks global scale approach

机译：从实验天文参数（太阳高度，总臭氧水平，气溶胶指数，反射率）走向分析和预测紫外线指数图。人工神经网络全球规模方法

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UV radiation is an important problem in climatology, ecology but also has direct effect on human health. A novel method for analysis and prediction of global erythemal UV for clear-sky at noon at any localisation, expressed as the UV index, has been proposed. The supervised artificial neural networks (ANN) were trained using purely experimental astronomical parameters (input: solar elevation, total ozone level, aerosol index, reflectivity and required output: erythemal local noon UV irradiance expressed as the UV index) for all dates from a 3-year representative period (2001-2003) collected by Total Ozone Mapping Spectrometer (TOMS). The input data from the 3-year period provide three sets of 1095 grids, each consisting of 288 x 180 i.e. 56,764,800 training vectors for total ozone level, aerosol index, reflectivity, while the output data provide only one set of data of the same resolution. The trained network delivers a good long-term representation of the physical problem as it is able to predict clear-sky global UV index maps (UVI for any location and date) with an excellent accuracy close to the detection error (3%, 0.5 unit of UVI). The omission of data on aerosol index (slightly) and reflectivity (highly) deteriorates the quality of UVI prediction (MSPE error 6.4%, 1 unit of UVI) but also confirms the importance of total ozone level for the UVI prediction. The neglect of data on aerosol index and reflectivity evidently removes the inhomogeneities and results in smoother and less reliable UVI maps. Reflectivity plays a very important role in variation of UV radiation level. The results are presented in the form of 2D rectangular maps (WGS-84 projection) of UV index. The neural network approach to UVI forecasting and analysis yields reasonable results and can be considered as an alternative to traditional approaches mainly based on radiative-transfer or regression models. (C) 2015 Elsevier Masson SAS. All rights reserved.
机译：紫外线辐射是气候，生态学中的重要问题，但也直接影响人类健康。提出了一种新颖的方法，用于分析和预测中午在任何位置的晴空全局红斑紫外线，用紫外线指数表示。使用纯实验天文参数（输入：太阳高度，总臭氧水平，气溶胶指数，反射率和所需的输出：红斑局部中午的紫外线辐照度表示为紫外线指数）对有监督的人工神经网络（ANN）进行了训练，时间从3开始总臭氧图谱仪（TOMS）收集的2001年至2003年的代表期。三年期间的输入数据提供了三组1095网格，每组包括288 x 180，即56,764,800个总臭氧水平，气溶胶指数，反射率的训练矢量，而输出数据仅提供了一组相同分辨率的数据。训练有素的网络可以很好地长期解决物理问题，因为它能够以接近检测误差（3％，0.5单位）的极高准确度预测天空晴朗的全球紫外线指数图（任何位置和日期的紫外线指数） UVI）。缺少气溶胶指数和反射率数据（高度）会降低UVI预测的质量（MSPE误差6.4％，UVI 1单位），但也证实了总臭氧水平对于UVI预测的重要性。气溶胶指数和反射率数据的忽略显然消除了不均匀性，并导致更平滑，更不可靠的UVI贴图。反射率在紫外线辐射水平的变化中起着非常重要的作用。结果以2D矩形矩形图（WGS-84投影）的形式显示。用于UVI预测和分析的神经网络方法产生了合理的结果，可以被认为是主要基于辐射传递或回归模型的传统方法的替代方法。 （C）2015 Elsevier Masson SAS。版权所有。

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