Aquarius

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Simulator Wish-List
Cal/Val/Algorithm Workshop 18-20 March GSFC
Gary Lagerloef Aquarius Principal Investigator
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Pre-launch Simulation Studies

• Test AVDS match-up processing and cal/val
  analyses.
• Detecting solar side lobe contamination
• Detecting solar flares
• Detecting unknown thermal calibration errors, per
  channel
• Detecting calibration drifts separately among
  each channel
• Further analysis of systematic errors in
  backscatter vs Tb corrections
• De-biasing L2 SSS prior to L3 gridding
• Differencing with a smoothed in situ field
• Crossover difference analyses
• 6pm-6am Faraday biases (Sab Frank latest
  simulator)
• Simulated Science data file for research
  community
• Validate Level 1 monthly 0.2 psu requirement
• Other .

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Test AVDS match-up processing and cal/val analyses

1. Test all the steps in the flowchart by match-ups
   with ADPS simulator data.
2. AVDS post processing, tabulation and analysis
   (box 11)
3. Science team review for functionality and utility

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From Frank Wentz at pre-CDR
Calibration Methodology
Retrieval Algorithm
SSS
TA_mea
Ancillary Data
Forward Model
SSS
TA_rtm
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Detecting solar side lobe contamination

• Apply match-ups by 10 latitude bands to fit
  and remove zonal biases in H V channels
  independently
• Test refine the methodology with simulated
  L1/L2 data that has realistic solar side lobe
  signals based on the scale model gain patterns.
• Develop and deliver an L2 algorithm module to
  run this process using the AVDS match-up data.

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Systematic Errors Instrument
Conceptual On-Orbit Behavior of Antenna
Temperature (or Backscatter Error) without
Temperature Dependent Calibration
1 year
Time
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Systematic Errors Instrument
Post-Calibration Systematic Errors in Antenna
Temperature (or Backscatter Error)
CBE 0.07 K RMS
CBE 0.1 K RMS
Residual Orbital Variations
Residual Seasonal Variations
Antenna Temperature (or Backscatter) Error
Residual Drift
Residual Bias

• Correlated errors due to the pre-launch
  measurement uncertainty of the calibration losses
• Captured mostly by on-orbit calibration by
  latitude zones (F.Wentz CDR presentation)
• Residual effect on gridded monthly accuracy was
  analyzed at CDR by J.Lilly

1 year
Time
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Systematic Wind Speed Correction Error

• Mean annual QuikSCAT vs SSM/I wind speed
  differences show large regional variations based
  on geophysical surface boundary layer processes.
• Serves as a K-band proxy for systematic
  differences between radar and radiometer
  sensitivities to roughness at L-Band.
• Peak differences gt1 m/s might translate to
  several tenths psu geographically correlated
  salinity error at L-band relative to a globally
  optimized retrieval.

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EOF Technique Applied to Wind Correction Bias

• We applied a method originally developed to
  estimate ocean dynamic height from vertical ocean
  temperature profiles, and effectively removed
  systematic errors common to the conventional
  methods (Lagerloef, G.S.E., 1994. An alternate
  method for estimating dynamic height from XBT
  profiles using empirical vertical modes. J.
  Phys. Oceanogr., 24, 205-213.)

• Define the matrix T as the predictor
  radar-based QSCAT wind, and matrix D as the
  predictand SSM/I wind field, and define anomalies
  D D-ltDgt and TT-ltTgt where lt gt is the scalar
  average over all space and time.
• The method produces a transform of T into an
  estimated matrix De whereby the result will be
  considered successful if the systematic
  differences De D ltlt T D
• D V A (1)
• R V\T or R V\T (2)
• W R\A (3)
• Ae R W (4)
• De V Ae (5)
• De De ltDgt (6)

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EOF De-Bias Results

• Fit using n10 modes (of possible 52), 72 of
  the total SSM/I variance.
• Systematic differences are reduced by an order of
  magnitude.

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Similar Results on Monthly Maps
N3 of 4 modes applied
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Application to Aquarius RD Plans

• Results are very encouraging, but application to
  Aquarius is problematical and will require more
  research and testing.
• Simulate global s0 and Tw simulated fields that
  contain systematic spatio-temporal variations in
  the s0/Tw relationship for each of the Aquarius
  incidence angles and polarizations.
• Develop and test the EOF algorithm over multiple
  sequences of simulated 7-day Aquarius orbit
  repeat cycles.
• Add simulated brightness temperature variations
  due to SSS, SST and other geophysical terms, then
  test methods using the simulator forward model to
  remove these effects and isolate Tw. The purpose
  is to simulate realistic Aquarius measurements
  and ensure that the desired SSS signals are not
  compromised by the correction methodology.

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De-biasing L2 SSS prior to L3 gridding

• Differencing with a smoothed in situ field
• Difference the derived SSS (L2) from each beam
  with a smoothed in situ field
• Remove residual bias, 1st 2nd orbit harmonics
  and higher orders as needed

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De-biasing L2 SSS prior to L3 gridding

• Crossover difference analyses
• Difference ascending and descending values at
  each crossover
• Apply least squares minimization to remove
  biases (borrowing from historical altimeter
  crossover analyses for orbit error removal)
  force SSS from all three beams to be self
  consistent.
• Apply to TH and TV differences to analyze
  geophysical errors wind speed, 6am-6pm biases,
  ionosphere Faraday rotation, solar side lobes,
  etc.
• Plethora of combinations Tapm - Tdqn where
  p,qH or V, m,n1,2,3

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Simulated Science data file for research community

• Properties
• 1-year active ocean and atmosphere fields
• Simulated radiometer and scatterometer data
• fully populated Level 2b science data file
• Publish by end of 2008 ?

Simulated SSS
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0.2 psu Validation Approach

• Match co-located buoy and satellite observations
  globally.
• Account for various surface measurement errors.
• Sort match-ups by latitude (SST) zones.
• Validate that the error allocations are met for
  the appropriate mean number of samples within the
  zone, or
• Calculate global rms over monthly interval

The Current Best Estimate (CBE) includes
instrument errors plus all geophysical
corrections such as surface roughness,
atmosphere, rain, galaxy, solar,
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Validation Testing with Simulator

• Seed ocean simulator with realistic in situ
  observations
• Simulate on-orbit match-ups
• Inject systematic calibration and geophysical
  error to Aquarius simulator
• Hierarchy of tests
• Calibration bias removal
• Algorithm coefficient tuning
• Systematic roughness correction bias removal
• Cross-over analyses gridding methodologies
• Validate 0.2 psu monthly gridded data error
• When to complete testing? Operational Readiness
  Review ??

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