The Mesoscale Ionospheric Simulation Testbed (MIST) Regional Data Assimilation Model Joseph Comberiate Michael Kelly Ethan Miller June 24, 2013

Introduction High-resolution data assimilation can provide nowcast and forecast of ionospheric scintillation in region of interest MIST is Kalman filter model that can assimilate SSUSI UV data, GPS TEC measurements, and SCINDA S4 values in real time Developed a first-principles ionospheric physics model to update three-dimensional electron density field Goal is to provide red/yellow/green scintillation maps every 15 minutes starting at 8 PM local time Can use SSUSI F17 (terminator orbit) equatorial arc observations to extend plasma bubble forecast window

3 Plasma Depletions and UHF Scintillation UHF communication failures when lines of sight passed through plasma depletions Examples from 2002 GUVI data, no comm issues when depletions not present

SSUSI instrument on DMSP F18 satellite provides 3D map of ionospheric electron density Allows for identification of usable and non-usable satellites and timeframes supporting UHF SATCOM communications Scintillation map identifies lines of sight that will pass through depleted regions and experience scintillation Output is a map of regions on the ground that will experience communication outages with a given satellite 4 Scintillation Maps

5 SSUSI Scintillation Map Line of sight to GEO satellite at 80 longitude 10/16/ UT 7:57 PM LT

MIST: Upgrading Capabilities Assimilate ground-based scintillation data Fill in data gaps to provide forecast of entire region Physics-based model provides forecast for rest of night Time start: 8 PM LT Altitude range: km 20° lat., 15° lon. span Resolution: 20 km altitude, 0.33° longitude, 1° latitude Time increment: 15 min. 3D data cube: 13,500 cells

SSUSI UV brightness values assimilated from F18 swath at 8 PM LT Slant TEC derived from GPS RINEX files, assimilated over 15 minute intervals Assimilates S4 values from SCINDA receivers every 15 minutes S4 used to estimate NmF2 and Ne depletion Data Sources – SSUSI, SCINDA, GPS

Kalman Filter for Data Assimilation 1) x - Model State Vector Drive with current Kp, F10.7 Initialize with ionospheric model (PIM, IRI, RIB-G) 2) M - State Transition Matrix Model uses ionospheric physics to advance state by 15 min. 3) P - Model Error Covariance 4) Q - Transition Model Error Covariance Can be estimated from 50 repeated runs of model (PIM) Truncated covariance, sparse matrix to decrease computation time 5) y - Data Vector Data arranged into single vector 6) H - Measurement Matrix Measurement matrix relates observations to model state 7) R - Observation Error Covariance Assimilated data comes with errorbars Weights for multiple data types versus background electron density

Ionospheric Physics Solves for update to state vector every 15 minutes Assumes ionosphere of O + and electrons Solves momentum and continuity equations Transports electrons parallel and perpendicular to dipole magnetic field Includes recombination and Eastward drift E x B drift drives location of equatorial arcs, use Fejer- Scherliess model for vertical drifts

Outputs 10 Red, yellow, green scintillation maps for region of interest Available for all longitudes, +/- 40° latitude Electron density TEC maps

SSUSI, GPS, and SCINDA Left: GPS and SCINDA data assimilated over India Right: SSUSI, GPS, and SCINDA data assimilated over India

Bubble Forecast using SSUSI F17 Example – March 14, 2013 vs. March 15, 2013 F17 March 14 th – bright, separated arcs F17 March 15 th – weak, collapsed arcs F18 March 14 th – bubble F18 March 15 th – no bubble SSUSI Limb Scans - 6 PM Local Time 8 PM Local Time Observing equatorial arc features provides information on bubble formation for entire night. Nightly forecast can be created at 6 PM AuroraEquatorial ArcsAurora Altitude

Latitudinal Separation of Arcs Use GUVI bubble climatology to relate equatorial arc separation to bubble formation Latitudinal separation of arcs driven by ExB drift EPB occurrence maximized at 25°-30° separation Comberiate and Paxton 2010

North/South Electron Density Ratio Peak electron density asymmetry (dB) = More EPB occurrences when EIA peaks are symmetric Asymmetry in EIA peaks caused by meridional neutral winds

Summary Kalman filter model can assimilate SSUSI UV data, GPS TEC measurements, and SCINDA S4 values in real time Developed a first-principles ionospheric physics model to update the background ionosphere Output of model is red/yellow/green scintillation maps for region of interest, also can provide electron density and TEC maps SSUSI F17 observations in terminator orbit provide equatorial arc information for nightly forecast of bubble formation starting at 6 PM local time