A comparison of the effects of initializing different thermosphere- ionosphere model fields on storm time plasma density forecasts

Alex T. Chartier, David R. Jackson, Cathryn N. Mitchell

Research output: Contribution to journalArticle

14 Citations (Scopus)
84 Downloads (Pure)


Data assimilation has been used successfully for real-time ionospheric specification, but it has not yet proved advantageous for forecasting. The most challenging and important ionospheric events to forecast are storms. The work presented here examines the effectiveness of data assimilation in a storm situation, where the initial conditions are known and the model is considered to be correct but the external solar and geomagnetic drivers are poorly specified. The aim is to determine whether data assimilation could be used to improve storm time forecast accuracy. The results show that, in the case of the storm of Halloween 2003, changes made to the model's initial thermospheric conditions improve electron density forecasts by at least 10% for 18 h, while changes to ionospheric fields alone result in >10% forecast accuracy improvement for less than 4 h. Further examination shows that the neutral composition is especially important to the accuracy of ionospheric electron density forecasts. Updating the neutral composition gives almost all the benefits of updating the complete thermospheric state. A comparison with real, globally distributed observations of vertical total electron content confirms that updating the thermospheric composition can improve forecast accuracy. Key Points Neutral composition is important for storm-time ionospheric forecasting Changes to thermospheric initial conditions improve ionospheric forecasts Changes to ionospheric fields alone are lost after 12 hours
Original languageEnglish
Pages (from-to)7329-7337
Number of pages9
JournalJournal of Geophysical Research: Space Physics
Issue number11
Early online date22 Oct 2013
Publication statusPublished - 1 Nov 2013



  • ionosphere
  • thermosphere
  • data assimilation
  • forecasting
  • modeling
  • storm

Cite this