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

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

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

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

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

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

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

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

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

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

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

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

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

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

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