CEDAR
Coupling, Energetics and Dynamics of Atmospheric Regions Program
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Neutral atmosphere data assimilation with WAM-IPE for global ionosphere TEC specification

Alex Medema - Ann and H.J. Smead Aerospace Engineering Sciences, University of Colorado Boulder
Eric Sutton - Space Weather Technology, Research, and Education Center (SWx TREC), University of Colorado Boulder
Jeffrey Thayer - Ann and H.J. Smead Aerospace Engineering Sciences, University of Colorado Boulder, Space Weather Technology, Research, and Education Center (SWx TREC), University of Colorado Boulder
Tzu-Wei Fang - Space Weather Prediction Center, National Oceanic and Atmospheric Administration
First Author's Affiliation
1. Ann and H.J. Smead Aerospace Engineering Sciences, University of Colorado Boulder
Abstract text:

The NOAA/NWS Whole Atmosphere Model (WAM) is a global neutral atmosphere model extending from the ground through the thermosphere. It has been coupled to the Ionosphere-Plasmasphere-Electrodynamics (IPE) model to provide combined operational forecasts of the upper atmosphere and ionosphere. We use the Iterative Driver Estimation and Assimilation (IDEA) data assimilation scheme to ingest neutral density observations from the Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) mission to provide corrections to WAM-IPE external solar drivers as parameterized by F10.7 and Kp. The revised driver estimates propagate through the neutral atmosphere model and subsequently adjust the specification of the ionosphere. To investigate the thermosphere-ionosphere coupling performance in the context of the applied data assimilation technique, the ionosphere specifications are compared with the Madrigal database total electron content (TEC) maps and with control WAM-IPE model output using observed solar drivers. Where found, improvements to the TEC specification may be attributed to the strong neutral-ion coupling in the ionosphere-thermosphere region.

Poster PDF
DATA-05_Medema_Wednesday.pdf (2.68 MB)
Student in poster competition
Poster category
DATA - Data Assimilation, Data Analytics, Methods and Management
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