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Tim Fuller-Rowell, Mariangel Fedrizzi, Mihail Codrescu, Tomoko Matsuo, and Catalin Negrea

Prospects for real-time physics-based thermosphere ionosphere models for neutral density specification and forecast. Tim Fuller-Rowell, Mariangel Fedrizzi, Mihail Codrescu, Tomoko Matsuo, and Catalin Negrea. 2007 CHAMP/CTIPe Orbit Average Comparisons. 400 km.

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Tim Fuller-Rowell, Mariangel Fedrizzi, Mihail Codrescu, Tomoko Matsuo, and Catalin Negrea

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  1. Prospects for real-time physics-based thermosphere ionosphere models for neutral density specification and forecast Tim Fuller-Rowell, Mariangel Fedrizzi, Mihail Codrescu, Tomoko Matsuo, and Catalin Negrea NADIR MURI

  2. 2007 CHAMP/CTIPe Orbit Average Comparisons 400 km • Main purpose of this study is to simulate the model response to short-period variations in geomagnetic activity during the year. • To accommodate this goal the semi-annual variation of neutral density in CTIPe has been removed by introducing a semi-annual variation in electric field small-scale variability. • Electric fields can directly change Joule heating by varying the ion convection at high-latitudes (Deng and Ridley, 2007). An increase in Joule heating raises the neutral temperature, which enhances the neutral density at constant heights. • Electric field variability changes the distribution of Joule heating significantly, and can introduce interhemispheric asymmetries (Codrescu et al., 1995, 2000). New CTIPe run including seasonal variation in the E-field small scale variability R= 0.88 RMSE= 0.17 BIAS= -0.017 SD= 0.17 NADIR MURI

  3. Coupled Thermosphere Ionosphere Plasmasphere Modelwith self-consistent Electrodynamics • Global thermosphere 80 - 500 km, solves momentum, energy, composition, etc. Vx, Vy, Vz, Tn, O, O2, N2, …. • High latitude ionosphere 80 - 10,000 km, solves continuity, momentum, energy, etc. O+, H+, O2+, NO+, N2+, N+, Vi, Ti, …. • Plasmasphere, and mid and low latitude • ionosphere • Self-consistent electrodynamics • Forcing: solar UV and EUV, Weimer • electric field, TIROS/NOAA auroral • precipitation, tidal forcing NADIR MURI

  4. Magnetospheric Forcing of CTIPe ACE solar wind data defines magnetospheric sources Weimer electric field patterns: Auroral precipitation pattern: NADIR MURI

  5. Real-time CTIPe at NOAA Space Weather Prediction Center • Implemented in 2010 in test operational mode • Automated scripts extract solar wind data from operational database • ACE measurements of IMF, solar wind (SW) velocity and density, and either solar F10.7 or EUV flux to force the global circulation model • Due to the 30 minute propagation time of SW to the nose of the magnetosphere it provides a 10-20 minute forecast • Web page shows neutral temperature, electron density, mean molecular mass, nmF2, hmF2, TEC, and model inputs in a quick look format • Runs 100 times faster than real time (run once, sample 3-D density field) • Can use SRPM EUV solar radiation forecast and WSA-ENLIL geomagnetic activity forecast for 5-day prediction updates every 6 hours • Automated validation and comparison with GAIM, US-TEC, DLR, etc. • Extensive other validation • Can provide Joule heating index for JB2008 • http://helios.swpc.noaa.gov/ctipe/CTIP.html

  6. NmF2 and hmF2 against MH ionosonde O/N2 ratio compared with TIMED-GUVI SRPM (Fontenla et al., ) EUV proxy (F10.7, F10.781 day)

  7. Relationship between CTIPe Joule Heating and neutral density: Joule heating index Fedrizzi et al. (Space Weather, 2012) INPUT (Joule heating) IMPULSE RESPONSE (shaping filter) OUTPUT (Joule heating index) • Convolution of CTIPe Joule heating with the shaping filter results in a new parameter (Joule heating index) representing the integral of the product of the two functions.

  8. Bruce Bowman NADIR MURI

  9. NADIR MURI

  10. Real-time CTIPe Summary • CTIPe running at SWPC in real-time in test operational mode • Joule heating index can be provided as test operational index • Available at CCMC and used by ISS operations • Provided to AFRL • High vertical resolution version being validated (improved tidal propagation, MTM, etc.) NADIR MURI

  11. Robust and Practical Assimilative Methods Tomoko Matsuo NADIR MURI

  12. Assimilative Method I Optimal interpolation using CTIPe with maximum-likelihood covariance estimate of the neutral density improves the density specification up to 40-50 % in comparison to CTIPe predictions [Matsuo et al., SW, 2012]. Low-dimensional covariance is built with EOFs to describe large-scale correlation succinctly [Matsuo and Forbes, JGR, 2010; Lei et al., JGR, 2012]. Currently, the analysis is limited to 400km. Application to real-time CTIPe (STTR)

  13. Assimilative Method II EnKF using NCAR-TIEGCM [Matsuo et al., JGR, 2012; Lee et al., JGR, 2012; Matsuo and Araujo-Pradere, RS, 2011] EnKF is capable of improving the neutral density specification by assimilating CHAMP/GRACE in-situ neutral density and COSMIC Ne profiles. DART/TIEGCM is open to the community. Assimilation of HASDM data to TIEGCM (AFRL small univ) Data Assimilation Research Testbed (http://www.image.ucar.edu/DARes/DART) TIEGCM 1.94 (http://www.hao.ucar.edu/modeling/tgcm)

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