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Empirical Background Model Of Ionospheric Electric Potential (Cousins and Shepard, 2010)

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Empirical Background Model Of Ionospheric Electric Potential (Cousins and Shepard, 2010)

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  1. “Super” AMIEPyAssimilative Mapping of Ionospheric Electrodynamics with SuperDARN line-of-sight velocity and SuperMAG ground magnetic perturbationsLiam Kilcommons, Tomoko MatsuoUniversity of Colorado, Colorado Center for Astrodynamics Research (CCAR), Department of Aerospace Engineering Sciences (AES)

  2. The Bird’s Eye View Uncertainty Estimates For SuperDARN and SuperMAG observations Empirical Background Model Of Ionospheric Electric Potential (Cousins and Shepard, 2010) AMIE Algorithm Improved estimate of electric potential Uncertainty (Covariance) in Background Model (Cousins, 2013) Observations Maxwell’s Equations and Conductance Model (Ohm’s Law) involved in this step SuperDARN Line-of-Sight Ion Velocity SuperMAG Magnetic Perturbation Vectors

  3. AMIE Algorithm Observations (SuperMAG ASCII, SuperDARN Davitpy) Background model (CS10 Electric Potential) amie_obs.py amie_basis.py amie_cov.py amie_models.py amie_cov.py amie_models.py Observations at sampled locations, j Background model potential at grid locations, i Observation Error Covariance Cr[nj,nj] SuperMAG: Diagonal; Constant Variance SuperDARN: Diagonal; Variance of Obs Model Error Covariance Cb[244,244] (CS10 EOF Covariance) amie_core.py Optimal Interpolation / Kalman Update Calculate Gain Update State Update Covariance AMIE Result Electric Potential and Error Covariance #Calculate the gain matrix innerK = np.linalg.inv(np.dot(H,np.dot(Cb,H.T)) + Cr) K = np.dot(Cb,np.dot(H.T,innerK)) #Prior model state is weight of each basis function # from regression of potential from the background model # against basis function values for potential x_prior = self.x_prior #print np.count_nonzero(np.isnan(self.x_prior)) #Perform the update step x_posterior = x_prior + np.dot(K , (y-np.dot(H , x_prior)) )

  4. AMIEPy Architecture Included Data Tables Dependancies / External Libraries DavitPy Scikit-Learn ApexPython GeospacePy Lite AMIE Basis Function Tables OvationPyme Just SAM IGRFPy AMIEPy Modules (1-3 Classes Each) amie_obs.py Observation Ingest and Preparation Classes for retrieving/slicing SuperMAG, SuperDARN,...from DavitPy/ASCII amie_basis.py Read AMIE basis function tables, Express electrodynamic variables on input lat/lon grids as AMIE basis expansions amie_models.py Wrapper Classes Which Call External Models CS10 Electric Potential “OvationPyme” Conductance amie_taper.py Spatial Correlation Models Localize influence of observations on overall covariance (strong nearby, weak far away) amie_cov.py Prior Model Error Covariance Specification CS10 Electric Potential Model Error Covariance amie_core.py OI/Assimilation Code Result Encapsulation Result Plotting Result Dump (to HDF5)

  5. SuperDARN Observation Prep Glossary DavitPy IGRFPy Azimuth of Radar LOS Gridded SD ion velocity Just SAM Uncertainty in velocity Electric field operator amie_obs.py Class: SuperDARN Vertical Component of IGRF main B field amie_basis.py Class: BasisFunctionSet Function: get_electric_field() Observations ingested into AMIEPy: Line-of-sight electric field Uncertainty in observations: (nj - number of observations ) (Cousins et. al. 2013,2015)

  6. SuperMAG Observation Prep amie_basis.py Class: BasisFunctionSet Function: calc_potcoef_to_groundmag_coef() OvationPyme SuperMAG Daily ASCII (JHU website) amie_obs.py Class: SuperMAG amie_basis.py Class: BasisFunctionSet Function: get_ground_magnetic_perturbations() Glossary Magnetic perturbations at ground Observations ingested into AMIEPy: north Conductance east Ionospheric to ground magnetic potential operators down Uncertainty in observations (constant): Ground magnetic pertubation operator

  7. OvationPyme Conductance Right: OvationPyme Diffuse Aurora Electron Energy Flux (mW/m^2) Bottom: Hall and Pedersen conductance • Python implementation of Redmon et al. adaptation of Newell’s Ovation Prime (2010) • Approach borrowed from Cousins et. al. 2016** • Auroral Conductance Model: Ovation “Diffuse Aurora” & Robinson Formula • EUV (photon) conductance model: • Brekke and Moen* incoherent scatter • Background for AMIEPy: 4 Mho *Asgeir Brekke, Jøran Moen, Observations of high latitude ionospheric conductances, Journal of Atmospheric and Terrestrial Physics, Volume 55, Issue 11, 1993, Pages 1493-1512, ISSN 0021-9169, http://dx.doi.org/10.1016/0021-9169(93)90126-J. (http://www.sciencedirect.com/science/article/pii/002191699390126J) **Cousins, E. D. P., T. Matsuo, and A. D. Richmond (2015), Mapping high-latitude ionospheric electrodynamics with SuperDARN and AMPERE, J. Geophys. Res. Space Physics, 120, 5854–5870,

  8. AMIEPyCompared With DMSP How does ion drift velocity predicted by AMIEPy compare against DMSP ion drift velocity?

  9. Using DMSP Comparison to Optimize Uncertainty Assigned to SuperMAG 90th percentile residual for each 4-mintue AMIE window One week of data Comparison indicates we should set observation uncertainty to 50nT

  10. Thanks For Your Attention! Coming Soon To a GitHub Near You!

  11. Backup Slides

  12. DMSP Comparison St. Patricks Day Storm 3-15-2013 to 3-22-2013 Only data with |EDMSP|>5 mV/m All data

  13. DMSP Cross Track Ion Drift Postprocessing Fit a line to the data equatorward of 50 MLAT then subtract Before After

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