1. Data Assimilation and the GAIM Model at the Air Force Weather Agency
Capt Matthew Sattler
Chief, Space Weather Integration Team
AFWA/A8TM
21 Jan 08

2. Overview
The Global Assimilation of Ionospheric Measurements (GAIM) Model at AFWA
Data Sensitivity Studies
A First Look at GAIM Model Metrics
Future Work in Data Assimilation
GAIM Background
- Why data assimilation for the ionosphere?
- Motivation for the GAIM model
- History of development
GAIM Operation at AFWA
- How the model runs
- How often
- Files produced
- Input/Output data
- Difficulties of runnin a model operationally - hotstart, continous operation, etc.
Data Sensitivity Studies
- Funding constraints for data sources
- Prioritization of data set ingest
- Data denied areas
- Impact to the warfighter
A first look at GAIM metrics
- why we need a comprehensive metrics program (3 reasons)
- method for forecast panel verification (mimic terrestrial techniques)
- ideas for GAIM model verification (forecast)
- GAIM performance by local time, latitude band, geographic area, Kp
- plots showing effects of latent data
Future work
- real-time verification of specifications
- more comprehensive data sensitivity studies
- characterize difference in GAIM model performance with additional data sets (e.g. UV data in Jan 08) 3 1

3. The GAIM Model
Developed at Utah State with ONR funding – NRL manages the program – Contractor integrated – In house visualization
IOC in December 2006
4° X 15° (lat x lon) horizontal grid spacing
83 vertical levels between 90 and 1400km
Vertical resolution: 4km in E-region, 20km in F-region
Regional Mode available with high enough data density
1° X 3.75° (lat x lon) grid spacing possible
Model consists of three components:
A conditional climatology background model called the Ionospheric Forecast Model (IFM) serves as a first guess field
Kalman Filter assimilation scheme to integrate real-time data
Gauss-Markov statistical timestep routine to produce a 24 hour forecast with 1 hr resolution
Why data assimilation for the ionosphere?
Motivation for the GAIM model
History of development, implementation, and upgrades
Future plans for the model
How the model runs
How often the model runs
Files produced
Inputs/Outputs
Challenges of running model operationally
Hot-starts
Continuous operation

4. GAIM Operation at AFWA GAIM model operation
IOC GAIM provides specification every 15 minutes with 24 hour forecast produced once per hour
Model is pure climatology above 60 degrees
Initial datasets for assimilation include:
127 ground-based TEC measurements (from JPL)
Up to 45 vertical profiles from ionosondes
In-situ electron densities from 5 DMSP satellites
DMSP UV sensor data to be added beginning in Jan 08
Matures data assimilation capability
Helps to remove continental-bias to data
Additional upgrades:
F-17 SSUSI/SSULI, and GUVI data – Dec 08
COSMIC data – Dec 09
Output supports HF communication and single frequency GPS Error analysis as well as the Space Weather Prediction Center (SWPC) and the intelligence community (IC)
A data assimilation model works best with lots of data; we are always working to integrate additional data types into the model
Each data type must carry a measurement of the uncertainty, which is used by the Kalman filter in the assimilation process
We currently ingest three data types: ground-based TEC measurements, vertical profile information from ionosondes, and in-situ electron densities from DMSP SSIES
Ground-based data is extremely important – provides crucial information about the bottom-side of the ionosphere. However, it carries a continental bias (also hard to get from data-denied regions)
Space-based data is expensive and doesn’t give as much information about the bottom-side profile, but can provide better top-side information and can observe data-denied regions. Field-of-view sensors, such as the UV sensors on DMSP, provide a significant increase in the number of observations available for assimilation. 3 1

5. GAIM at FOC
AFW expects to declare FOC in Dec 2010 with an upgrade to replace the G-M version of GAIM
Upgrade will replace IFM with Ionosphere Plasmasphere Model (physics based)
Uses Ensemble Kalman Filter technique to derive ionospheric driver information; this replaces the Gauss-Markov forecast technique with a physics-based approach
Top of model increases from 1400 to 30,000km
Still no data assimilated above 60 degrees, but background model improved
Expect resolution to double over the G-M version
Model will be MPI, expects to use about 50 processors (IOC uses 2 and is not truly scalable)
FOC in December 2010 (estimated)
Global grid resolution will be 2° X 7.5° (lat X lon)
Spatial resolution along B:
0.9km in E-region, 1.3km in F-region, 3.8km topside, 240km at 17,000 km
Regional/Localized Modes available with high enough data density (requires very dense network of observations)
25km X 25km grid spacing possible
Model consists of three components
A physics-based background model called the Ionosphere Plasmasphere Model (IPM) serves as a first guess field
Ensemble Kalman Filter assimilation scheme to integrate real-time data, derive ionospheric driver information
Ionospheric drivers then used to physically propagate observations forward in time (24 hour forecast) 3 1

6. Data Sensitivity Studies
GAIM output without streaming TEC
Funding constraints for data acquisition
Prioritization of data sets
Data denied areas
Warfighter impacts – important to assess the confidence in model output used to generate warfighter effects products
e.g. if we lose the streaming TEC data set, how much uncertainty will be added to the model output, and eventually to the decision maker?
Magnitude of differences is -3 to 3, or 6 TECU. 3 1

7. Data Sensitivity Studies
GAIM output without ionosondes
Magnitude of differences is -2.5 to 3, or ~6 TECU. 3 1

8. Data Sensitivity Studies
GAIM output without SSIES
F-13 through F-17 data missing – satellite tracks line up with differences
Magnitude of differences is -1.5 to 2.1, or ~3.5 TECU.
Differences as large as 5 TECU. 3 1

9. GAIM Model Metrics Motivation for a comprehensive metrics program
For the forecaster, model developer, and leadership
Method for GAIM forecast verification
Comparing against the closed assimilation panels
Using IFM (climatology) and previous day closed assimilation panels (persistence) as reference
Assess GAIM performance by:
Local time
Level of geomagnetic activity
Geographic area
Effects of latent data
Mention localtime effect – appears to perform best during noon/midnight; perform worst during dawn/dusk 3 1

10. GAIM Model Metrics GAIM performance: Forecast verification
Average skill scores and RMSE for each forecast hour for the month of December 3 1

11. GAIM Model Metrics GAIM performance: Latitudinal effect
Climatology plot uses the raw IFM output as the reference
Persistence plot uses the previous day assimilation as the reference
Does the RMSE plot indicate continental bias (lower RMSE in NH), or is this a seasonal effect (summer in the SH)? 3 1

12. GAIM Model Metrics GAIM performance: Geographic effect
These plots valid just prior to K=4 (K=1 at this time)
Can see the sunlit sector 3 1

13. GAIM Model Metrics GAIM performance: Geographic effect
These plots valid just prior to K=4 (K=1 at this time) 3 1

14. GAIM Model Metrics GAIM performance: Effects of latent data
Most likely attributed to latent DMSP SSIES data from F-13 (Europe), F-14 (Indonesia), F-16 (Pacific), and F-17 (S Africa) 3 1

15. Future Work Real-time verification of GAIM specifications
More comprehensive data sensitivity studies
Characterize difference in GAIM model performance when additional data sets are ingested (e.g. UV data in Jan 08) 1 3