Open AccessAn Ensemble Kalman Filter for the Thermosphere‐Ionosphere
Author(s) -
Codrescu S. M.,
Codrescu M. V.,
Fedrizzi M.
Publication year - 2018
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1002/2017sw001752
Subject(s) - thermosphere , ionosphere , ensemble kalman filter , aeronomy , plasmasphere , physics , kalman filter , forcing (mathematics) , data assimilation , airglow , geophysics , computational physics , atmospheric sciences , meteorology , extended kalman filter , computer science , magnetosphere , plasma , quantum mechanics , artificial intelligence
Abstract A principal limitation of physics‐based modeling in the Thermosphere‐Ionosphere system is the large uncertainty associated with the implementation of the external forcing of the system, including high‐latitude convection and particle precipitation, solar UV/EUV fluxes, and waves propagating from below. As measuring these quantities with sufficient spatial and temporal resolution is prohibitively costly, a more practical approach to improve model results is to assimilate more readily available measurements of the system. We discuss considerations in implementing an Ensemble Kalman Filter (EnKF) for a strongly forced system versus a chaotic system, overview an EnKF implementation for the strongly forced Coupled Thermosphere, Ionosphere, Plasmasphere, and Electrodynamics model, and present encouraging improvements to neutral density specification obtained by assimilating Challenging Minisatellite Payload (CHAMP) neutral density measurements. The model results show improvement in comparisons with both CHAMP and Gravity Recovery and Climate Experiment (GRACE) measurements during a geomagnetically quiet period at solar minimum.