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The Mesoscale Ionospheric Simulation Testbed (MIST) Regional Data Assimilation Model
The Mesoscale Ionospheric Simulation Testbed (MIST) Regional Data Assimilation Model
Joseph ComberiateMichael KellyEthan Miller
June 24, 2013
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IntroductionIntroduction
• 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
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Plasma Depletions and UHF ScintillationPlasma 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
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• 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
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Scintillation MapsScintillation Maps
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SSUSI Scintillation MapSSUSI Scintillation Map
Line of sight to GEO satellite at 80 longitude
10/16/20111527 UT7:57 PM LT
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MIST: Upgrading CapabilitiesMIST: 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: 230-530 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
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• 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, GPSData Sources – SSUSI, SCINDA, GPSData Sources – SSUSI, SCINDA, GPSData Sources – SSUSI, SCINDA, GPS
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Kalman Filter for Data AssimilationKalman Filter for Data AssimilationKalman Filter for Data AssimilationKalman 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 Covariance4) 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 vector6) H - Measurement Matrix•Measurement matrix relates observations to model state7) R - Observation Error Covariance•Assimilated data comes with errorbars•Weights for multiple data types versus background electron density
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Ionospheric PhysicsIonospheric PhysicsIonospheric PhysicsIonospheric 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
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OutputsOutputs
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• Red, yellow, green scintillation maps for region of interest
• Available for all longitudes, +/- 40° latitude
• Electron density
• TEC maps
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SSUSI, GPS, and SCINDASSUSI, GPS, and SCINDA
Left: GPS and SCINDA data assimilated over India
Right: SSUSI, GPS, and SCINDA data assimilated over India
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Bubble Forecast using SSUSI F17Bubble Forecast using SSUSI F17Bubble Forecast using SSUSI F17Bubble Forecast using SSUSI F17
Example – March 14, 2013 vs. March 15, 2013 F17 March 14th – bright, separated arcs
F17 March 15th – weak, collapsed arcs
F18 March 14th – bubble
F18 March 15th – 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
Aurora
Equatorial Arcs Aurora
Altit
ude
Altit
ude
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Latitudinal Separation of ArcsLatitudinal 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
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North/South Electron Density RatioNorth/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
S
N10 e
elog 10-
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SummarySummary
• 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