Nitric oxide (NO) infrared radiation is an essential cooling source for the thermosphere, especially during and after geomagnetic storms. An accurate representation of the three‐dimension (3‐D) morphology of NO emission in models is critical for predicting the thermosphere state. Recently, the deep‐learning neural network has been widely used in space weather prediction and forecast. Given that the 3‐D image of NO emission from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) onboard the Thermosphere Ionosphere Energetics and Dynamics satellite contains a large amount of missing data which is unobserved, a context loss function is applied to extract the features from the incomplete SABER NO emission images. A 3‐D NO emission model (referred to as NOE3D) that is based on the convolutional neural network with a context loss function is developed to estimate the 3‐D distribution of NO emission. NOE3D can effectively extract features from incomplete SABER 3‐D images. Additionally, NOE3D has excellent performance not only for the training datasets but also for the test datasets. The NO emission climate variations associated with solar activities have been well reproduced by NOE3D. The comparison results suggest that NOE3D has better capability in predicting the NO emission than the Thermosphere‐Ionosphere Electrodynamics General Circulation Model. More importantly, NOE3D is capable of providing the variations of NO emission during extremely disturbed times.

中文翻译：

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热球一氧化氮排放的深度学习模型

一氧化氮（NO）红外辐射是热层的重要冷却源，尤其是在地磁风暴期间和之后。模型中NO排放的三维（3D）形态的准确表示对于预测热层状态至关重要。最近，深度学习神经网络已广泛用于空间天气预报和预测。假设热球电离层能量和动力学卫星上使用宽带放射线测量法（SABER）从大气探测中获得的NO排放的3D图像包含大量未观测到的丢失数据，则可以使用上下文损失函数来提取不完整的SABRE NO发射图像的特征。建立了基于带上下文损失函数的卷积神经网络的3-D NO排放模型（称为NOE3D），以估算NO排放的3-D分布。NOE3D可以有效地从不完整的SABRE 3D图像中提取特征。此外，NOE3D不仅对于训练数据集而且对于测试数据集都具有出色的性能。NOE3D很好地再现了与太阳活动有关的NO排放气候变化。比较结果表明，NOE3D具有比热球-电离层电动力学通用循环模型更好的预测NO排放的能力。更重要的是，NOE3D能够在极度混乱的时间内提供NO排放的变化。NOE3D可以有效地从不完整的SABRE 3D图像中提取特征。此外，NOE3D不仅对于训练数据集而且对于测试数据集都具有出色的性能。NOE3D很好地再现了与太阳活动有关的NO排放气候变化。比较结果表明，NOE3D具有比热球-电离层电动力学通用循环模型更好的预测NO排放的能力。更重要的是，NOE3D能够在极度混乱的时间内提供NO排放的变化。NOE3D可以有效地从不完整的SABRE 3D图像中提取特征。此外，NOE3D不仅对训练数据集而且对于测试数据集都具有出色的性能。NOE3D很好地再现了与太阳活动有关的NO排放气候变化。比较结果表明，NOE3D具有比热球-电离层电动力学通用循环模型更好的预测NO排放的能力。更重要的是，NOE3D能够在极度混乱的时间内提供NO排放的变化。比较结果表明，NOE3D具有比热球-电离层电动力学通用循环模型更好的预测NO排放的能力。更重要的是，NOE3D能够在极度混乱的时间内提供NO排放的变化。比较结果表明，NOE3D具有比热球-电离层电动力学通用循环模型更好的预测NO排放的能力。更重要的是，NOE3D能够在极度混乱的时间内提供NO排放的变化。