Identification

Title

A deep learning model for the thermospheric nitric oxide emission

Abstract

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.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.org/ark:/85065/d70k2czv

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2021-03-01T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact