Retrospective-Cost-Based Adaptive Input and State Estimation for the Ionosphere-Thermosphere

The upper atmosphere is a strongly driven system in which the global state is rapidly altered by the solar drivers. One of the main drivers of the upper atmosphere is the solar irradiance in the extreme ultraviolet and x-ray bands. The solar irradiance in these bands is proxied by ground-based measurements of F10.7, which is the solar irradiance at the wavelength of 10.7 cm. The problem of estimating F10.7 and physical states in the upper atmosphere is considered by assimilating the neutral density measurements in the global ionosphere–thermosphere model and using retrospective-cost adaptive input and state estimation. Retrospective-cost adaptive input and state estimation is a non-Bayesian estimator that estimates the input by minimizing the difference between the estimator output and the output of the physical system. In this paper, we use retrospective-cost adaptive input and state estimation to estimate F10.7 using simulated data as well as real satellite data.

[1]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[2]  Bodo W. Reinisch,et al.  International Reference Ionosphere 2000 , 2001 .

[3]  Dennis S. Bernstein,et al.  Retrospective Cost Optimization for Adaptive State Estimation, Input Estimation, and Model Refinement , 2013, ICCS.

[4]  Jean-Pierre Barbot,et al.  State and unknown input estimation for linear discrete-time systems , 2005 .

[5]  A. Hedin.  MSIS‐86 Thermospheric Model , 1987 .

[6]  Dennis S. Bernstein,et al.  Maneuvering target tracking using retrospective-cost input estimation , 2016, IEEE Transactions on Aerospace and Electronic Systems.

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

[8]  Dieter Bilitza,et al.  International reference ionosphere , 1978 .

[9]  David Angeli,et al.  A Lyapunov approach to incremental stability properties , 2002, IEEE Trans. Autom. Control..

[10]  Peter K. Kitanidis,et al.  Unbiased minimum-variance linear state estimation , 1987, Autom..

[11]  Yue Deng,et al.  Assessment of the non‐hydrostatic effect on the upper atmosphere using a general circulation model (GCM) , 2008 .

[12]  Dennis S. Bernstein,et al.  Retrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphere , 2011, Stat. Anal. Data Min..

[13]  Mario di Bernardo,et al.  Global Entrainment of Transcriptional Systems to Periodic Inputs , 2009, PLoS Comput. Biol..

[14]  A. Hedin.  Extension of the MSIS Thermosphere Model into the middle and lower atmosphere , 1991 .

[15]  Timothy Fuller-Rowell,et al.  Height-integrated Pedersen and Hall conductivity patterns inferred from the TIROS-NOAA satellite data , 1987 .

[16]  A. Hedin,et al.  A revised thermospheric model based on mass spectrometer and incoherent scatter data - MSIS-83 , 1983 .

[17]  D. Stauffer,et al.  Optimal determination of nudging coefficients using the adjoint equations , 1993 .

[18]  Shreyas Sundaram,et al.  On Delayed Observers for Linear Systems with Unknown Inputs , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[19]  Dennis S. Bernstein,et al.  Data assimilation and driver estimation for the Global Ionosphere-Thermosphere Model using the Ensemble Adjustment Kalman Filter , 2013 .

[20]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[21]  A. Ward,et al.  GRACE: Gravity Recovery and Climate Experiment , 2002 .

[22]  A. Ridley,et al.  Role of variability in determining the vertical wind speeds and structure , 2011 .

[23]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[24]  Bart De Moor,et al.  Unbiased minimum-variance input and state estimation for linear discrete-time systems , 2007, Autom..

[25]  J. M. Lewis,et al.  The use of adjoint equations to solve a variational adjustment problem with advective constraints , 1985 .

[26]  Dennis S. Bernstein,et al.  Retrospective-Cost-Based Adaptive State Estimation and Input Reconstruction for the Global Ionosphere-Thermosphere Model , 2012 .

[27]  Philippe Courtier,et al.  Interactions of Dynamics and Observations in a Four-Dimensional Variational Assimilation , 1993 .

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

[29]  Gábor Tóth,et al.  The Global Ionosphere-Thermosphere Model , 2006 .

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

[31]  Yi Xiong,et al.  Unknown disturbance inputs estimation based on a state functional observer design , 2003, Autom..

[32]  A. Ridley,et al.  Modeling the thermospheric response to solar flares , 2008 .

[33]  D. Weimer,et al.  Improved Ionospheric Electrodynamic Models and Application to Calculating Joule Heating Rates , 2005 .

[34]  Philippe Courtier,et al.  Use of cloud‐cleared radiances in three/four‐dimensional variational data assimilation , 1994 .

[35]  James Cutler,et al.  Adaptive state estimation for nonminimum-phase systems with uncertain harmonic inputs , 2011 .

[36]  Paul B. Hays,et al.  An empirical model of the Earth's horizontal wind fields: HWM07 , 2008 .

[37]  Eduardo Sontag.  Contractive Systems with Inputs , 2010 .

[38]  P. Richards,et al.  EUVAC: A solar EUV Flux Model for aeronomic calculations , 1994 .

[39]  Ionel M. Navon,et al.  An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation , 1992 .