1. Ionospheric Research at USU R.W. Schunk, L. Scherliess, J.J. Sojka, D.C. Thompson & L. Zhu Center for Atmospheric & Space Sciences Utah State University Logan, Utah 84322 Presented at: University of New Mexico May 16, 2006

2. OUTLINE 1.Current Ionosphere Data Assimilation Models 2.Thermosphere Modeling 3.Tracking a TID

3. 1. Current Ionosphere Data Assimilation Models Gauss-Markov Kalman Filter Model of the Ionosphere Full Physics Kalman Filter Model of the Ionosphere Ensemble Kalman Filter Model of High-Latitude Electrodynamics

4. GAIM Basic Approach We use a physics-based ionosphere-plasmasphere-polar wind model and a Kalman Filter as a basis for assimilating a diverse set of real-time (or near real-time) measurements. GAIM provides both specifications and forecasts on a global, regional, or local grid. Global RegionalLocal

5. GAIM Assimilates Multiple Data Sources Data Assimilated Exactly as They Are Measured o Bottomside N e Profiles from Digisondes (20) o Slant TEC from up to 1000 Ground GPS Receivers o N e Along Satellite Tracks (4 DMSP satellites) o Integrated UV Emissions o Occultation Data (CHAMP, SAC-C, IOX)

6. Gauss-Markov Kalman Filter Model Specification of the Global Ionosphere

7. Ionosphere Forecast Model (IFM) Provides background ionosphere Global physics-based model 90 - 1400 km 15 - minute output cadence O +, H +, NO +, N 2 +, O 2 +, T e, T i –Only use N e Kalman solves for deviations from background

8. Gauss-Markov Kalman Filter Model Operational Version Delivered July 15, 2004. oNRL oAFWA oNorthrop Grumman oAFRL oCCMC oCISM, BEI, UCAR-ESMF oNOAA

9. Full Physics Kalman Filter Model Ionosphere Specification with Middle & Low Latitude Drivers

10. Global Ionosphere-Plasmasphere-Polar Wind Model 3-D Time-Dependent Parameters –NO +, O 2 +, N 2 +, O +, H +, He + –T e, T i –u ||, u  Auxiliary Parameters –N m F 2 –h m F 2 –N m E –h m E –TEC Grid System –Global –Regional –Localized –90-30,000 km –Realistic Magnetic Field (IGRF) Spatial Resolution Along B –0.9 km in E-Region –1.3 km in F-Region –3.8 km in Topside –240 km at 17,000 km

11. Full Physics GAIM Output Continuous Reconstruction of Global N e Distribution oIonosphere-Plasmasphere-Polar Wind o90-30,000 km Quantitative Estimates of the Accuracy of Reconstruction Auxiliary Parameters oN m F 2, h m F 2, N m E, h m E oSlant and vertical TEC Model Drivers oHigh-Latitude Convection and Precipitation oLow-Latitude Electric Fields oGlobal Neutral Winds oGlobal Neutral Composition

14. Ensemble Kalman Filter for High- Latitude Electrodynamics High-Resolution Specification of Convection & Precipitation Drivers

15. Physics-Based Model of High-Latitude Electrodynamics Time-Dependent Ionosphere Model 0 3-D Density Distributions (NO+,O2+,N2+,O+,H+,He+) 0 3-D Te and Ti Distributions 0 Ion Drifts Parallel & Perpendicular to B 0 Hall & Pedersen Conductances M-I Electrodynamics Model 0 MHD Transport Equations & Ohm’s Law 0 Alfven Wave Propagation 0 Active Ionosphere 0 10 km & 5 sec Resolutions 0 Potential, E-field, Currents, Joule Heating Magnetic Induction Model 0 Calculates B Perturbations in Space & on Ground 0 Includes Earth’s Induction Effect

16. Data Assimilated Ground Magnetic Data from 100 Sites Cross-Track Velocities from 4 DMSP Satellites Line-of-Sight Velocities from the SuperDARN Radars In-situ Magnetic Perturbations from the 66 IRIDIUM Satellites

17. Output of the Electrodynamics Model (High Resolution) Electric Potential Convection Electric Field Energy Flux and Average Energy of Precipitation Field-Aligned and Horizontal Currents Hall and Pedersen Conductances Joule Heating Rates 3-D Electron and Ion Densities 3-D Electron and Ion Temperatures TEC Ground and Space Magnetic Disturbances

18. We obtain the entire High Latitude Electrodynamic Environment Kalman FilterClimate (No Data)‘Truth’

19. 2. Thermosphere General Circulation Model Numerical Solution of Neutral Gas Continuity, Momentum, and Energy Equations Time-Dependent, High-Resolution, Global Model 25 Non-Uniform Altitude Layers from 97-500 km 0.5 deg in latitude, 3 deg in longitude 50 km resolution in polar region Flux-Corrected-Transport (FCT) Numerical Method Rotating Coordinate System fixed to Earth Ma and Schunk (1995)

20. Thermosphere Model Inputs Global Ionosphere oDensities oVelocities oTemperatures Tidal and Gravity Wave Forcing from Below

21. Global Thermosphere Response to Ionospheric Structures Propagating Plasma Patches oMa & Schunk (1995, 1997a, 1997b, 2001) Sun-Aligned Polar Cap Arcs oMa & Schunk (1997) Theta Aurora oDemars & Schunk (2005) Equatorial Plasma Bubbles oSchunk & Demars (2003, 2005) Cusp Neutral Gas Upwelling oDemars & Schunk (2006) Supersonic Neutral Winds oSchunk & Demars (2006)

22. Qaanaaq, Greenland, October 29, 1989 All-Sky Images (630 nm) 2 - Minute Interval

24. Equatorial Spread-F and Bubbles JULIA Coherent Scatter Radar Hysell and Burcham (1998)

26. 3. Tracking a TID

27. In this “test,” the following are variables –TID Equatorward Speed –TID Width in Latitude –TID Amplitude History In this “test,” the assumptions are –TID is a perturbation on the background ionosphere. –TID perturbed densities are very noisy –TID moves along a meridian. –Observations lie along the meridian. Propagation with simple advective model A Model TID

28. 1-D TID/TAD  1-D Propagation  Propagation along meridian  Density Perturbations  Network of ~10 observatories

29. 1-D TID/TAD  1-D Propagation  Propagation along meridian  Density Perturbations  Network of ~10 observatories

30. 1-D TID/TAD  1-D Propagation  Propagation along meridian  Density Perturbations  Network of ~10 observatories

31. 1-D TID/TAD  1-D Propagation  Propagation along meridian  Density Perturbations  Network of ~10 observatories

32. The Observations  10 Stations  Density Perturbations  100% Noise

33. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=1

34. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=2

35. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=3

36. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=4

37. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=5

38. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=6

39. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=10

40. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=15

41. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations T=20

42. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Guessed Velocity 50% Off  Reconstruction of TID Density Perturbations

43. Kalman Filter Reconstruction  Reconstruction of TID Density Perturbations  # of Stations = 10  100% Noise  True Velocity = 2  Guessed Velocity 50% Off

44. Kalman Filter Reconstruction  # of Stations = 10  100% Noise  True Velocity = 2  Reconstruction of TID Density Perturbations  Determination of TID Velocity T=150