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Calibration and Validation Studies for Aquarius Salinity Retrieval

Calibration and Validation Studies for Aquarius Salinity Retrieval Shannon Brown and Sidharth Misra Jet Propulsion Laboratory, California Institute of Technology 7th Aquarius SAC-D Science Meeting 11-13, April 2012 Buenos Aires, Argentina. Project Overview.

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Calibration and Validation Studies for Aquarius Salinity Retrieval

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  1. Calibration and Validation Studies for Aquarius Salinity Retrieval Shannon Brown and Sidharth Misra Jet Propulsion Laboratory, California Institute of Technology 7th Aquarius SAC-D Science Meeting 11-13, April 2012 Buenos Aires, Argentina

  2. Project Overview • Developing methods to track Aquarius calibration over Antarctica and rainforest regions • Enables characterization of drift into gain and offset components • Ensures well calibrated brightness temperatures over full TB range • Important for other applications (e.g. soil moisture) • Follows approach developed for altimeter radiometers (Topex, Jason) • Investigation of roughness correction algorithms • Evaluating v1.2.3 roughness correction • Assessing dependence on sea state through match-ups with radar altimeters

  3. Aquarius Ocean Drift Horn 1 V-pol Horn 2 V-pol Horn 3 V-pol Horn 2 H-pol Horn 1 H-pol Horn 3 H-pol • Represents drift only at one TB – need another reference to determine whether it is a gain or offset drift • Offset drift means constant drift at all TBs (e.g. front end path loss drift) • Gain drift means drift largest at cold TBs that approaches zero for warm scenes (e.g. ND drift)

  4. Natural On-Earth Calibration Targets for Stability Tracking • Antarctica (mid-range TB ~200K) • Select areas with stable temperature (V-pol TB) and snow structure (H-pol TB) • Radiative transfer model used to determine L-band TB over time using in-situ temperature and higher frequency microwave observations as input 6 V-H • Rainforest (warm end TB ~280K) • Select depolarized heavily vegetated areas within the Aquarius swath • Use TMI and WindSat to determine canopy temperature to track Aquarius calibration

  5. Aquarius L-band Temporal Stability (Sep’11-Feb’12) q= 37.8o q= 45.6o q= 28.7o V-pol V-pol V-pol H-pol H-pol H-pol

  6. Ice Model Temperature profile determined from in-situ surface temperature data coupled with a heat transport model to determine T(z,t) AMSR-E 6.9-37 GHz V&H-pol observations constrain ice structure, ice dielectric model and thermal diffusivity MEMLS model (Wiesmann and Matzler, 1999) used to compute upwelling TB

  7. Drift over Antarctica • Computed Aquarius – Model TB for each channel -Aquarius minus Model over Antarctica - Scaled ocean drift Horn 2 H pol Horn 2 V pol Aquarius – model TB shows drift that is approximately half the ocean drift for all channels which is consistent with an instrument gain drift

  8. ~ Canopy Temperature Variations Rainforest Regions 2 K ~ Vegetation properties variations 0.05 K 6 V-H Amazon Region TMI 10 GHz De-polarization Congo Region TMI 10 GHz De-polarization 2 1 1 3 2 • Select regions in Amazon and Congo that exhibit small polarization signature at 6 and 10 GHz and contain Aquarius swath • Opaque canopy obscures the surface • Exhibit little polarization or incidence angle dependence • Brightness temperature closely tracks canopy physical temperature • 5 regions identified

  9. Drift over Amazon Rainforest regions show potential to monitor drift at the warm end • Used simple parametric model to estimate Aquarius TBs from TMI/WindSat 10.7 GHz TB time series to estimate residual drift over warm rainforest regions • TMI and WindSat data filtered for rain using flag based on 37-10 GHz TB difference • Applied 30-day smoothing • Negligible drift observed over warm regions – consistent with gain drift

  10. Roughness Correction Investigation

  11. Aquarius/Altimeter Match-ups Number of match-ups per 1o bin – all horns • Generating database of Aquarius match-ups with Jason-1/2 radar altimeters • Find altimeter observations that fall within Aquarius footprint separated by < 1 hour • Analyze roughness correction as a function of altimeter WS, SWH • Database used to evaluate v1.2.3 roughness correction

  12. V1.2.3 Surface Roughness Correction:Wind Speed + Scatterometer σ0 2-dimensional lookup table: [wind speed, σ0] → ΔTB Meissner and Wentz, Aquarius Cal/Val Workshop March 2012

  13. V1.2.3 Excess TB Compared to Altimeter Winds Excess TB-V Bias vs Alt. WS Excess TB-H Bias vs Alt. WS • Compared V1.2.3 specular TB (rad_TB_rc) to model using ancillary SST,SSS • Generally unbiased with respect to altimeter wind speed • 0.1K level biases observed in some channels for winds < 2m/s and >15m/s

  14. V1.2.3 Excess TB Compared to Altimeter Winds Excess TB-V StdDevvs Alt. WS Excess TB-H StdDevvs Alt. WS • Standard deviation typically between 0.3-0.5K (slightly higher for H-pol) • Standard deviation minimum near 9m/s - larger for low and high winds • More pronounced for H-pol

  15. V1.2.3 Excess TB Compared to Altimeter WS/SWH Horn 3 V-pol Horn 1 V-pol High winds/high waves Low winds/high waves Low winds/low waves High winds/low waves Binned v1.2.3 model differences vs altimeter WS and SWH Roughness correction underestimated for low winds/high waves Over-estimated for high winds/low waves More pronounced for V-pol

  16. All Channels Horn3 V-pol Horn1 V-pol Horn2 V-pol Horn3 H-pol Horn1 H-pol Horn2 H-pol Largest residual correlation with SWH in Horn1 (V&H) and Horn 3(V)

  17. V1.2.3 SSS Bias and StdDev Bias (psu) Std. Dev. (psu) Lowest residuals when scatterometer is viewing surface in the along-wind direction Highest residuals in cross-wind direction for high wind speeds

  18. V1.2.3 Excess TB vs WS & Dir Bias (psu) Std. Dev. (psu) • Highest residuals in excess TB when scatterometer viewing in cross-wind direction • Suggests that hybrid approach may reduce errors • Weight observations for L3 product based on relative wind direction • Supplement scatterometer correction in cross-winds case with ancillary data

  19. Summary • Method developed to track Aquarius radiometer TB drift over Antarctica and Amazon • References independent from ocean • Used to determine Aquarius drift is a gain drift • Evaluation of v.1.2.3 roughness correction shows little bias with respect to altimeter WS but some residual correlation with SWH • Highest residuals from scatterometer correction when Aquarius is viewing cross wind • Suggest improvements can be gained from hybrid algorithm relying more on ancillary data in cross-wind cases

  20. Separation of Gain vs Offset Drift • Level of gain vs offset drift will depend on which components are changing • Example: Drift in noise diode brightness creates gain drift • Largest drift for cold TBs, small drift at warmer TBs that are close to internal reference load temperature • ND drift will cause TB drift over Antarctica which is ~0.5 x ocean drift • TB drift will approach zero over warm regions

  21. Tracking Aquarius TB over Rainforest • Ferrazzoli and Guerriero (1999) and other modeling studies show that for high biomass, optical depth is >>1 and emissivities (defined by scattering from the forest) are within few %) between L-band , C-band and X-band • Track variation of L-band TB as a function of time from TMI and WindSat 10.7 GHz observations for morning passes (4-7 LST) • Annual surface temperature variations less than 2K in the morning over these regions so a few percent uncertainty in scaling factor not critical for estimating temporal variability

  22. Drift over Rainforest 5 Regions identified in Amazon and Congo which exhibit little polarization signature from 6.9-37 GHz See Brown and Misra talk Thursday at 16:10 for details • Heavily vegetated regions act like pseudo-blackbodies • Opaque canopy obscures the surface • Exhibit little polarization or incidence angle dependence • Brightness temperature closely tracks canopy physical temperature • Estimated canopy TB over rainforest regions as a function of time from TMI and WindSat 10.7 GHz observations for morning passes (4-7 LST) • Negligible drift observed over warm regions – consistent with gain drift

  23. Tracking Aquarius at Warm TBs V-pol Horn 1 All Regions • Differenced Aquarius TBs from TMI/WindSat 10.7 GHz TB time series to estimate residual drift over warm rainforest regions • TMI and WindSat data filtered for rain using flag based on 37-10 GHz TB difference • Applied 30-day smoothing • Congo region 1 showed largest temporal variability (correlated in both TMI and Aquarius data)

  24. Warm Reference weakly scattering canopy • Over warm regions (TB ~ 280K) we should see almost no drift in Aquarius TBs if it is a gain drift • Heavily vegetated regions act like pseudo-blackbodies (Brown and Ruf, 2005) • Opaque canopy obscures the surface • Exhibit little polarization or incidence angle dependence • Brightness temperature closely tracks canopy physical temperature • TB near 280K

  25. AMSR-E De-polarization Developing On-Earth TB Calibration References at L-band 37 V-H • Natural targets for L-band radiometer calibration over on-Earth dynamic range • Calm, flat ocean scenes – Cold reference • Ice sheets: Antarctica, Greenland – Mid-range reference • Land areas: flat, dry deserts; homogeneous heavily vegetated regions – Hot reference • Use to assess absolute calibration, monitor stability and assess residual instrument calibration errors 23 V-H 18 V-H 10 V-H 6 V-H

  26. Stability of Regions AMSR-E TBs Amazon Region 1 April 2008 – October 2010 AMSR-E 6.9 GHz TBV-TBH April 2008 – October 2010 0.05 K • TBs stable to ~2K over these regions over several years • C-band to Ka-band highly correlated • 6.9 GHz polarization difference stable to 0.05K • Vegetation microwave properties vary little with time

  27. TB with Model – All Channels TB after applying gain drift correction (v1.2.3) V1.1 TB not corrected for drift All channels in good agreement with model after applying gain drift correction based on ocean TA drift estimates

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