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EnKF Ionosphere and Thermosphere Data Assimilation Algorithm Through a Sparse Matrix Method
Author(s) -
He Jianhui,
Yue Xinan,
Wang Wenbin,
Wan Weixing
Publication year - 2019
Publication title -
journal of geophysical research: space physics
Language(s) - English
Resource type - Journals
eISSN - 2169-9402
pISSN - 2169-9380
DOI - 10.1029/2019ja026554
Subject(s) - thermosphere , data assimilation , ionosphere , ensemble kalman filter , kalman filter , algorithm , covariance matrix , meteorology , computer science , environmental science , geophysics , extended kalman filter , physics , artificial intelligence
In this work, we constructed an ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation system using the National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model (NCAR‐TIEGCM) as the background model. We use a sparse matrix method to avoid significant matrix related calculation and storage. A series of observing system simulation experiments have been conducted to assess the performance of the system. The results show that the system optimizes ionosphere drivers efficiently by assimilating electron densities through their covariance. The short‐term forecast capability is enhanced significantly, and the effect of initial condition correction lasts for longer than 24 hr. To our knowledge, this is the first study to demonstrate that the EnKF‐based global ionosphere and thermosphere data assimilation can be conducted without using a supercomputer. This workstation‐based EnKF ionosphere and thermosphere data assimilation system benefits both scientific studies and near‐real‐time operation.