Assimilation of FORMOSAT-3/COSMIC electron density profiles into a coupled thermosphere/ionosphere model using ensemble Kalman filtering

[1] This paper presents our effort to assimilate FORMOSAT-3/COSMIC (F3/C) GPS Occultation Experiment (GOX) observations into the National Center for Atmospheric Research (NCAR) Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) by means of ensemble Kalman filtering (EnKF). The F3/C electron density profiles (EDPs) uniformly distributed around the globe which provide an excellent opportunity to monitor the ionospheric electron density structure. The NCAR TIE-GCM simulates the Earth's thermosphere and ionosphere by using self-consistent solutions for the coupled nonlinear equations of hydrodynamics, neutral and ion chemistry, and electrodynamics. The F3/C EDP are combined with the TIE-GCM simulations by EnKF algorithms implemented in the NCAR Data Assimilation Research Testbed (DART) open-source community facility to compute the expected value of electron density, which is ‘the best’ estimate of the current ionospheric state. Assimilation analyses obtained with real F3/C electron density profiles are compared with independent ground-based observations as well as the F3/C profiles themselves. The comparison shows the improvement of the primary ionospheric parameters, such as NmF2 and hmF2. Nevertheless, some unrealistic signatures appearing in the results and high rejection rates of observations due to the applied outlier threshold and quality control are found in the assimilation experiments. This paper further discusses the limitations of the model and the impact of ensemble member creation approaches on the assimilation results, and proposes possible methods to avoid these problems for future work.

[1]  T. Killeen,et al.  A high-resolution, three-dimensional, time dependent, nested grid model of the coupled thermosphere–ionosphere , 1999 .

[2]  Xiaoqing Pi,et al.  JPL/USC GAIM: On the impact of using COSMIC and ground‐based GPS measurements to estimate ionospheric parameters , 2010 .

[3]  Timothy Fuller-Rowell,et al.  US‐TEC: A new data assimilation product from the Space Environment Center characterizing the ionospheric total electron content using real‐time GPS data , 2004 .

[4]  Robert W. Schunk,et al.  Ionospheric dynamics and drivers obtained from a physics‐based data assimilation model , 2009 .

[5]  Robert W. Schunk,et al.  Utah State University Global Assimilation of Ionospheric Measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation , 2006 .

[6]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[7]  P. L. Houtekamer,et al.  Ensemble Kalman filtering , 2005 .

[8]  Xiaoqing Pi,et al.  A performance evaluation of the operational Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM) , 2005 .

[9]  R. Schunk,et al.  Duration of an ionospheric data assimilation initialization of a coupled thermosphere‐ionosphere model , 2007 .

[10]  Patricia H. Reiff,et al.  Empirical polar cap potentials , 1997 .

[11]  Anthony J. Mannucci,et al.  Comparison of COSMIC occultation‐based electron density profiles and TIP observations with Arecibo incoherent scatter radar data , 2009 .

[12]  Raymond G. Roble,et al.  A coupled thermosphere/ionosphere general circulation model , 1988 .

[13]  James A. Secan,et al.  Tomography of the ionosphere: Four‐dimensional simulations , 1998 .

[14]  Timothy Fuller-Rowell,et al.  Global Assimilation of Ionospheric Measurements (GAIM) , 2001 .

[15]  R. Daley Atmospheric Data Analysis , 1991 .

[16]  Thomas L. Gaussiran,et al.  Ionospheric Data Assimilation Three‐Dimensional (IDA3D): A global, multisensor, electron density specification algorithm , 2004 .

[17]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[18]  Xiaoqing Pi,et al.  Estimation of E × B drift using a global assimilative ionospheric model: An observation system simulation experiment , 2003 .

[19]  G. Evensen Data Assimilation: The Ensemble Kalman Filter , 2006 .

[20]  Tomoko Matsuo,et al.  Role of thermosphere‐ionosphere coupling in a global ionospheric specification , 2011 .

[21]  Geir Evensen,et al.  The ensemble Kalman filter for combined state and parameter estimation: MONTE CARLO TECHNIQUES FOR DATA ASSIMILATION IN LARGE SYSTEMS , 2009 .

[22]  Ying-Hwa Kuo,et al.  Comparison of COSMIC ionospheric measurements with ground-based observations and model predictions : Preliminary results , 2007 .

[23]  S. Cohn,et al.  Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .

[24]  Xiaoqing Pi,et al.  Data assimilation of ground GPS total electron content into a physics‐based ionospheric model by use of the Kalman filter , 2004 .

[25]  Raymond G. Roble,et al.  A thermosphere/ionosphere general circulation model with coupled electrodynamics , 1992 .

[26]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[27]  Ying-Hwa Kuo,et al.  Artificial plasma cave in the low‐latitude ionosphere results from the radio occultation inversion of the FORMOSAT‐3/COSMIC , 2010 .

[28]  Stanley C. Solomon,et al.  Meridional winds derived from COSMIC radio occultation measurements , 2007 .

[29]  G. Bust,et al.  Neutral wind estimation from 4‐D ionospheric electron density images , 2009 .

[30]  Jeffrey L. Anderson,et al.  THE DATA ASSIMILATION RESEARCH TESTBED , 2009 .

[31]  Jeffrey L. Anderson,et al.  The Data Assimilation Research Testbed: A Community Facility , 2009 .

[32]  Xinan Yue,et al.  Error analysis of Abel retrieved electron density profiles from radio occultation measurements , 2010 .

[33]  Larry J. Paxton,et al.  An empirical Kp-dependent global auroral model based on TIMED/GUVI FUV data , 2008 .

[34]  Robert W. Schunk,et al.  Development of a physics‐based reduced state Kalman filter for the ionosphere , 2004 .