1. Radiation Belt Tools and Climatology Eric A. Kihn – NOAA/NGDC Paul O’Brien- Aerospace Robert Weigel – GMU Completing the Data Environment

2. State of the Data Environment Observational data is available, but often scattered, minimally documented and difficult to access Working with data often involves directly securing a PI’s time or a minor research project No sense of community support or access for those outside the field

3. Science Data Stewardship A focus on reaching a broader customer base An effort to reduce redundant functions on the data An effort to improve understandability through metadata A new focus on machine based access support multiple community based front- ends

4. Example Data Set: POES- MEPAD Data available at poes.ngdc.noaa.gov (78-08) Data in POES binary Has known contamination issues Preview (QC) plots in linear time 16 sec avg data as CDF or ASCII

5. Improved Data Product Data available at poes.ngdc.noaa.gov N15 and later Data in NetCDF The cross channel contamination has been removed (Green) Preview plots in L- shell and include Auroral Oval Full time resolution data Full metadata record in SPASE format

6. Data Matrix 1.1 Reanalysis 1.2 AMPTE 1.3 SAMPEX 1.4 GOES 1.5 POES 1.6 METOP 1.7 HEO 1.8 GPS 1.9 LANL GEO 1.10 Polar 1.11 CRRES 1.12 Akebono 1.13 SCATHA 1.14 ICO 1.15 S3-3 1.16 OV3-3 Details: http://virbo.org/wikihttp://virbo.org/wiki

7. Climatology vs. Reanalysis Gives you min/max/mean Is derived from direct observations Is useful for quick look-up of environmental specs Doesn’t contain the correlations between observables Gives you “a” state representation Is derived observation plus model Is useful for extracting scenarios Represents the physical correlations and boundaries

8. Introduction to Reanalysis Scientists around the world use the reanalysis data for: –Climate studies –Seasonal climate prediction –Climate variability studies –Initial/boundary conditions for regional/sub-grid-scale models –Diagnostic studies –Verification of climate models –Testbed for operational models The US atmospheric science community produces a standardized ‘reanalysis’ (via NCEP and NCAR) The reanalysis is built by going back as far as the data allows and running a consistent standard data assimilative physics-based global analysis model The reanalysis provides numerous climate and weather data for the entire globe on a standard grid. The reanalysis is run after the fact, when all data are available. July 29, 2004 Reanalysis: Air Temp at Sea Level (K) Figure courtesy US National Climate Reanalysis Project

9. Space Weather Analysis New HPI database (DMSP, NOAA) New 100 + magnetometer database. 210 MM, Canopus, Tromso, Greenland, Image, etc.. Complete IMF Record AMIE Runs @ 1.0 minute (1989-2003) GITM Runs (1991-2002) SIMM runs (1991-2001)

10. Pros and Cons of Reanalysis Pros The final product, a “Standard Solar Cycle” is conceptually simpler than a model that attempts to statistically characterize the temporal dependence Reanalysis can be stored on any coordinate system (even time-alt- lat-lon!) Specifications for different domains with their own natural coordinates can be combined on a single, common coordinate system (again, e.g., time-alt-lat-lon) Reanalysis captures real events rather than simulated ones, thus capturing realistic temporal correlations (especially useful for determining the frequency and duration of an effect) Cons Probably a lot more work than mean/worst case flux maps Smooths out spatial variations (artificially increases spatial correlations) May not accurately capture tails of distributions (we must be careful about this) (Much) larger database –This is much less of a concern now

11. What makes it “Reanalysis” Part of the fitting procedure is to determine the best estimate for the state x of the system conditioned on minimizing the error between the observations y and the estimates of those observations. The measurement matrix H relates the fluxes to the observations (which are typically count rates in a detector) It is important to note that in reanalysis we do NOT try to convert the observations into fluxes or phase space densities. Rather, we use the instrument response function to “predict” what the instrument would measure given the state x and penalize that state x for any deviation between those “predictions” and the observations. Knowledge of the instrument response (and its uncertainty) becomes paramount. The observation penalty function (p e ) is multiplied by another function that penalizes deviations from the output of a statistical p(x) or physics-based model for x:

12. Example Statistical Reanalysis Results Energetic Electron Statistical Reanalysis Inner and Outer belt electron flux from 100 keV to 7 MeV Derived from a static model of statistical variation This reanalysis covers a full solar cycle It was constrained with HEO-1 and HEO-3 data Prior to the launch of HEO-3, the specification is essentially useless at this energy (703 keV) There are also spectral features (e.g., bumps) that don’t appear to be realistic @ GPS

13. Coming Work in this Area GEM Focus Group -Space Radiation Climatology (2006 - 2011, P. O'Brien and G. Reeves) - (Paul.OBrien@aero.org, reeves@lanl.gov) ONERA – Salammbo model has data assimilative models for GPS and GEO satellites LANL-DREAM project is pursuing a more ambitious model that couples the radiation belt into a global model that includes the ring current, plasmasphere and convection electric fields.

14. Quality Control In activities like a reanalysis a lot of issues fall out New tools that more easily identify and retrieve satellite conjunctions will Better metadata and metadata accessibility should document instrument temporal changes Needs to be an on going community coordinated effort.

15. Virtual Radiation Belt Observatory (ViRBO) Data Models Reanalysis Data Models Software Documents End User ViRBO API Custom Interface Commercial Interface

16. Conclusions The new data stewardship paradigm will mean a fundamental shift in the way research is done and provide many opportunities to operations. The tremors are already past the data center level profoundly effecting the center missions. “Most researchers are accustomed to studying a relatively small data set for a long time, using statistical models to tease out patterns. At some fundamental level that paradigm has broken down.” – Nature June, 1999