Open AccessObserving System Impact on Ionospheric Specification Over China Using EnKF Assimilation
Author(s) -
He Jianhui,
Yue Xinan,
Hu Lianhuan,
Wang Junyi,
Li Mingyuan,
Ning Baiqi,
Wan Weixing,
Xu Jiyao
Publication year - 2020
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2020sw002527
Subject(s) - thermosphere , nowcasting , ionosphere , data assimilation , incoherent scatter , total electron content , environmental science , meteorology , international reference ionosphere , ensemble kalman filter , computer science , remote sensing , kalman filter , geology , geophysics , extended kalman filter , geography , tec , artificial intelligence
Abstract Accurate ionospheric specification for the current and future is one of the key tasks in operational space weather. In this work, we have assessed the effect of a dense ground network consisting of different radio instruments either developed or under developing on ionosphere nowcasting and forecasting over China and adjacent region (0–60°N and 70–140°E) through observing system simulation experiments. The data assimilation system is an Ensemble Kalman filter (EnKF) ionosphere and thermosphere data assimilation algorithm. The National Center for Atmospheric Research Thermosphere Ionosphere Electrodynamics General Circulation Model is used as a background model. Effects of different observation types, including the slant total electron content from Beidou System geostationary satellite and Global Position System and electron density observations from ionosondes and incoherent scattering radar on the ionospheric nowcasting and forecasting, are examined by the accurate specification of ionosphere key parameters (total electron content and 3‐D electron density). We found that simultaneously assimilating different observation types can greatly improve the quality of ionosphere specification. Furthermore, updating the thermospheric state variables in the coupled thermosphere‐ionosphere forecast model in the assimilation step plays an important role in improving the ionosphere forecasting. The ionosphere forecasting capability can last longer (>24 hr) in each observing system simulation experiment due to the adjusted thermosphere states. This study can provide a reference for observing system design over the China region and future ionosphere forecasting operation.