1. Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop 18-20 March GSFC

2. 2 G. 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 ….

3. 3 G. Lagerloef, et al. Salinity Satellite Mission 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. Search Radius Filter ??

4. 4 G. Lagerloef, et al. Salinity Satellite Mission Retrieval Algorithm T A_mea SSS Forward Model SSS Ancillary Data T A_rtm Calibration Methodology From Frank Wentz at pre-CDR

5. 5 G. Lagerloef, et al. Salinity Satellite Mission 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 patternProjected 7-day map using analytical model gain pattern

6. 6 G. Lagerloef, et al. Salinity Satellite Mission Conceptual On-Orbit Behavior of Antenna Temperature (or Backscatter Error) without Temperature Dependent Calibration Systematic Errors; Instrument Time 1 year Seasonal Variations Orbital Variations Long-Term Component Drift Fixed Pre-Launch Bias Antenna Temperature (or Backscatter) Error 0 K (0 dB) CBE 0.65 K RMSS CBE 0.12 K RMS

7. 7 G. Lagerloef, et al. Salinity Satellite Mission Systematic Errors; Instrument Post-Calibration Systematic Errors in Antenna Temperature (or Backscatter Error) Time 1 year Residual Seasonal Variations Residual Orbital Variations Residual Drift Residual Bias Antenna Temperature (or Backscatter) Error CBE 0.1 K RMS 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

8. 8 G. Lagerloef, et al. Salinity Satellite Mission 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.

9. 9 G. Lagerloef, et al. Salinity Satellite Mission 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- and 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’ + (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- and 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’ + (6)

10. 10 G. Lagerloef, et al. Salinity Satellite Mission 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.

11. 11 G. Lagerloef, et al. Salinity Satellite Mission Similar Results on Monthly Maps N=3 of 4 modes applied

12. 12 G. Lagerloef, et al. Salinity Satellite Mission 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 σ 0 and Tw simulated fields that contain systematic spatio- temporal variations in the σ 0 /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.

13. 13 G. Lagerloef, et al. Salinity Satellite Mission 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, 1 st & 2 nd orbit harmonics and higher orders as needed

14. 14 G. Lagerloef, et al. Salinity Satellite Mission 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 T H and T V 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

15. 15 G. Lagerloef, et al. Salinity Satellite Mission 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

16. 16 G. Lagerloef, et al. Salinity Satellite Mission 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, …

17. 17 G. Lagerloef, et al. Salinity Satellite Mission 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 ??

18. 18 G. Lagerloef, et al. Salinity Satellite Mission