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

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

• 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

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

• 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

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

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

OutputsOutputs

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• Red, yellow, green scintillation maps for region of interest

• Available for all longitudes, +/- 40° latitude

• Electron density

• TEC maps

SSUSI, GPS, and SCINDASSUSI, GPS, and SCINDA

Left: GPS and SCINDA data assimilated over India

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

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

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

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-

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