SNB^3GEO

A Multi Input Single Output (MISO) NARMAX model is used to provide a forecast of the daily averaged electron flux at GEO. NARMAX is a black box methodology and was trained on electron flux data from GOES13. It should be noted that the two day ahead forecast will change as more data is obtained for the current day.

HELIOSPHERE
  13

Contributor

contributed at 2020-07-02

Authorship

Affiliation:  
University of Sheffield
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Classification(s)

Application-focused categoriesNatural-perspectiveSpace-earth regions

Model Description

English {{currentDetailLanguage}} English

Quoted from: https://ccmc.gsfc.nasa.gov/models/models_at_glance.php

Model Description

A Multi Input Single Output (MISO) NARMAX model is used to provide a forecast of the daily averaged electron flux at GEO. NARMAX is a black box methodology and was trained on electron flux data from GOES13. It should be noted that the two day ahead forecast will change as more data is obtained for the current day.

Model Input
The inputs to the model are the daily averaged L1 solar wind velocity and density, along with the fraction of time that the IMF is southward.

Model Output
Daily averaged >2 MeV integral electron flux at GEO (electrons/(cm^2 sr day))

References and relevant publications
Boynton, R. J., Balikhin, M. A., Billings, S. A., and Amariutei, O. A.: Application of nonlinear autoregressive moving average exogenous input models to geospace: advances in understanding and space weather forecasts, Ann. Geophys., 31, 1579-1589, doi:10.5194/angeo-31-1579-2013, 2013.

Relevant links
http://www.ssg.group.shef.ac.uk/USSW/UOSSW.html

CCMC Contact(s)
Masha Kuznetsova
301-286-9571

Developer Contact(s)
Richard Boynton, Space Instrumentation Group
phone: +44(0)1142225234

Michael Balikhin, Director of Space Instrumentation Group
phone: +44(0)1142225628

Stephen Billings, Director of Signal Processing and Complex Systems Team
phone: +44(0)1142225232

Department of Automatic Control and Systems Engineering
The University of Sheffield
Mappin Street
Sheffield, S1 3JD
United Kingdom

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How to Cite

Richard Boynton, Michael Balikhin, Stephen Billings (2020). SNB^3GEO, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/3b35d146-30c9-48d8-bb19-637a946386b2
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Contributor(s)

Initial contribute: 2020-07-02

Authorship

Affiliation:  
University of Sheffield
Is authorship not correct? Feedback

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