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arXiv 1111.5308astro-ph.SRphysics.ao-phphysics.space-ph
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太阳总辐照度和光谱辐照度的短期预报

Short-term forecast of the total and spectral solar irradiance

Luis Eduardo A. Vieira, Thierry Dudok de Wit, Matthieu Kretzschmar

摘要:

在众多的太阳物理量和地球物理量中,太阳辐照度的精确演化是预测地球大气中性和电离组分演化的基础,本文建立了一个人工神经网络模型来近实时地计算太阳辐照度的演化。该模型是基于这样的假设,即太阳辐照度的变化很大一部分是由于太阳磁场的结构的演变。我们采用一个层递归网络(LRN)的双极磁结构(输入)和太阳辐照度(输出)的演变之间的复杂关系进行建模。双极磁结构的演变是从近实时的太阳磁盘磁图和强度图像。根据太阳盘面的覆盖面积对磁结构进行了识别和分类。我们通过比较模型的输出和SORCE航天器上的仪器对太阳辐照度的观测来约束模型。在这里,我们专注于SORCE仪器覆盖的光谱的两个区域。通过将数据集分为两组来测试网络的泛化:训练集和验证集。我们已经发现,模型误差是波长相关的。在115 ~ 180 nm波段的24小时预报误差小于5%,在180 ~ 310 nm波段的预报误差可达20%。网络的性能随着预测期的增加而逐渐降低,这大大限制了我们可以使用所讨论的架构实现的最大预测期。提出的模型使我们能够预测总的和光谱的太阳辐照度提前三天。

英文摘要:

Among several heliophysical and geophysical quantities, the accurate evolution of the solar irradiance is fundamental to forecast the evolution of the neutral and ionized components of the Earth's atmosphere.We developed an artificial neural network model to compute the evolution of the solar irradiance in near-real time. The model is based on the assumption that that great part of the solar irradiance variability is due to the evolution of the structure of the solar magnetic field. We employ a Layer-Recurrent Network (LRN) to model the complex relationships between the evolution of the bipolar magnetic structures (input) and the solar irradiance (output). The evolution of the bipolar magnetic structures is obtained from near-real time solar disk magnetograms and intensity images. The magnetic structures are identify and classified according to the area of the solar disk covered. We constrained the model by comparing the output of the model and observations of the solar irradiance made by instruments onboard of SORCE spacecraft. Here we focus on two regions of the spectra that are covered by SORCE instruments. The generalization of the network is tested by dividing the data sets on two groups: the training set; and, the validation set. We have found that the model error is wavelength dependent. While the model error for 24-hour forecast in the band from 115 to 180 nm is lower than 5%, the model error can reach 20% in the band from 180 to 310 nm. The performance of the network reduces progressively with the increase of the forecast period, which limits significantly the maximum forecast period that we can achieve with the discussed architecture. The model proposed allows us to predict the total and spectral solar irradiance up to three days in advance.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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