Radiation Belt Tools and Climatology

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Radiation Belt Tools and Climatology
Completing the Data Environment

• Eric A. Kihn NOAA/NGDC
• Paul OBrien- Aerospace
• Robert Weigel GMU

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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 PIs time or a minor research project
• No sense of community support or access for those
  outside the field

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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

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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

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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

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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/wiki
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Climatology vs. Reanalysis

• Gives you min/max/mean
• Is derived from direct observations
• Is useful for quick look-up of environmental
  specs
• Doesnt 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

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Introduction to Reanalysis

• 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.

• 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

Figure courtesy US National Climate Reanalysis
Project
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Space Weather Analysis

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

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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

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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 l. 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 (pe) is
  multiplied by another function that penalizes
  deviations from the output of a statistical p(x)
  or physics-based model for x

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Example Statistical Reanalysis Results
_at_ GPS

• 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 dont appear to be realistic

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Coming Work in this Area

• GEM Focus Group -Space Radiation Climatology
  (2006 - 2011, P. O'Brien and G. Reeves)
  -(Paul.OBrien_at_aero.org, reeves_at_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.

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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.

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