A deep learning model for the thermospheric nitric oxide emission

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.