simulator wish-list gary lagerloef aquarius principal investigator cal/val/algorithm workshop 18-20...

Simulator Wish-List

Gary LagerloefAquarius Principal Investigator

Cal/Val/Algorithm Workshop18-20 March

GSFC

2G. Lagerloef, et al.

Salinity Satellite Mission

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 ….



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

AVDS Tabulated Data; Specifications TBD

Buoy Obs.

Buoy Obs.

Search Radius

Search Radius

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Retrieval AlgorithmTA_mea SSS

Forward Model SSS

Ancillary Data

TA_rtm

Calibration Methodology

From Frank Wentz at pre-CDR



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.

Latitude vs time using scale model gain pattern

Projected 7-day map using analytical model gain pattern



Conceptual On-Orbit Behavior of Antenna Temperature (or Backscatter Error) without Temperature Dependent Calibration

Systematic Errors; Instrument

Time

1 year

SeasonalVariations

OrbitalVariations

Long-TermComponent Drift

Fixed Pre-LaunchBias

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CBE 0.65 K RMSSCBE 0.12 K RMS



Systematic Errors; Instrument

Post-Calibration Systematic Errors in Antenna Temperature (or Backscatter Error)

Time

1 year

Residual SeasonalVariations

Residual OrbitalVariations

Residual Drift

ResidualBias

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•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

•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

CBE 0.07 K RMS



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 >1 m/s might translate to several tenths psu geographically correlated salinity error at L-band relative to a globally optimized retrieval.



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-<D> and T’=T-<T> where < > 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 << 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’ + <D> (6)

• 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-<D> and T’=T-<T> where < > 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 << 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’ + <D> (6)



EOF De-Bias Results

• Fit using n=10 modes (of possible 52), ~72% of the total SSM/I variance.

• Systematic differences are reduced by an order of magnitude.



Similar Results on Monthly Maps

N=3 of 4 modes applied



Application to Aquarius R&D Plans

Results are very encouraging, but application to Aquarius is problematical and will require more research and testing.

1. Simulate global σ0 and Tw simulated fields that contain systematic spatio-temporal variations in the σ0/Tw relationship for each of the Aquarius incidence angles and polarizations.

2. Develop and test the EOF algorithm over multiple sequences of simulated 7-day Aquarius orbit repeat cycles.

3. 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.



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



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,q=H or V, m,n=1,2,3



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



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, …

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



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 ??



0 1 ,

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S i S i Bv sur

S i Bh sur S i

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simulator wish-list gary lagerloef aquarius principal investigator cal/val/algorithm workshop 18-20...

Documents

analysis of systematic

systematic differences

rmscorrelated errors

orbit calibration

calibration lossescaptured

avds post processing

backscatter errorcbe

solar flares