Transcript Radiation Belt Tools and Climatology

Radiation Belt Tools and
Climatology
Completing the Data Environment
Eric A. Kihn – NOAA/NGDC
Paul O’Brien- Aerospace
Robert Weigel – GMU
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
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 frontends
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
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 Lshell and include
Auroral Oval
•Full time resolution
data
•Full metadata record
in SPASE format
Data Matrix
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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
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
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.
July 29, 2004 Reanalysis:
Air Temp at Sea Level (K)
Figure courtesy US National Climate Reanalysis Project
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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
Space Weather Analysis
Init Conditions
IMF
Kp
Dst
10.7 cm Flux
HPI
Magnetometer
GOES
AMIE
High Lat Elec
GITM
TEC, FoF2,Neutral Winds
Magnetic, Electric Potential, Etc.
SIMM
Particle Data
Kp
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)
SWR
DATA
Pros and Cons of Reanalysis
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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-altlat-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)
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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
What makes it “Reanalysis”
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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)
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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
@ 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 don’t appear to be
realistic
Coming Work in this Area
• GEM Focus Group -Space Radiation Climatology (2006 2011, P. O'Brien and G. Reeves) ([email protected], [email protected])
• 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.
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.
Virtual Radiation Belt Observatory
(ViRBO)
Custom Interface
Reanalysis
Data
ViRBO API
Models
Models
End User
Documents
Data
Data
Software
Commercial Interface
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