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The Impact of Perturbing Eddy Diffusion and Upper Boundary on the Ionosphere EnKF Assimilation System
Author(s) -
He Jianhui,
Yue Xinan
Publication year - 2021
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1029/2021ja029366
Subject(s) - thermosphere , data assimilation , ionosphere , ensemble kalman filter , forcing (mathematics) , atmospheric sciences , meteorology , environmental science , physics , computational physics , geophysics , kalman filter , mathematics , extended kalman filter , statistics
Accurate representation of ensemble members of coupled Ionosphere‐Thermosphere (I‐T) is crucial to the ionosphere forecast in the ensemble Kalman filter (EnKF) data assimilation system. In this paper, besides the model drivers used in previous studies, including the solar 10.7 cm radio flux (F10.7), auroral hemispheric power (HP) and cross‐tail potential drop (CP), the eddy diffusion coefficient (ED), and nighttime vertical O + flux at the top boundary are also perturbed to generate ensemble members. Based on our developed EnKF data assimilation system, the impact of perturbing different model external forcing parameters on ensemble generation and forecast capability of ionosphere and thermosphere has been investigated in detail through a series of sensitivity tests and data assimilation experiments. This system uses the National Center for Atmospheric Research (NCAR) Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) as a background model. The findings are summarized as follows: (a) Associated with perturbing two additional model forcing parameters, better ensemble members of ionosphere and thermosphere states of the background model can be generated during both daytime and nighttime. (b) The improvement of the forecast capability of the ionosphere and thermosphere variables can be further enhanced. This study can provide a reference for ensemble generating strategy for the coupled I‐T EnKF data assimilation in the future.