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Level 2 Scatterometer Processing. Alex Fore Julian Chaubell Adam Freedman Simon Yueh. L2 Processing Flow. L1B geolocated, calibrated TOI σ 0. Average over block; filter by L1B Qual. Flags. L2 (lon, lat) L2 σ TOI + KPC. Ancillary Data: ρ HHVV, f HHHV , f VVHV Θ F (from rad or IONEX).

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level 2 scatterometer processing

Level 2 Scatterometer Processing

Alex Fore

Julian Chaubell

Adam Freedman

Simon Yueh

l2 processing flow
L2 Processing Flow

L1B geolocated, calibrated TOI σ0

Average over block; filter by L1B Qual. Flags

L2 (lon, lat)


  • Ancillary Data:
  • ΘF (from rad or IONEX)


Cross-Talk +

Faraday Rotation

Wind Retrieval

L2 wind + σwind

  • Ancillary Data:
  • -PALS HIGHWINDS 2009 data
  • Ancillary Data:
  • NCEP wind dir.

ΔTB retrieval


level 2 scatterometer cross talk and faraday rotation mitigation strategy

Level 2 Scatterometer Cross-Talk and Faraday Rotation Mitigation Strategy

Alex Fore

Adam Freedman

Simon Yueh

forward beam integration
Forward Beam Integration
  • We use Mueller matrix formalism
  • Mtot gives transformation from transmitted signal to received signal.
  • Model Srx for transmit H (SrxH) and transmit V (SrxV).
  • Received power for (H or V) is modeled as appropriate element of SrxH + that fromSrxV times instrument gain + noise.
simulated total 0 performance
Simulated Total σ0 Performance
  • Total σ0 performance is independent of any Faraday rotation corrections or cross-talk removal.
  • De-biased RMSE will be below 0.1 dB for high σ0 for all beams.
  • Total L2 σ0 as compared to a area-weighted 3 dB footprint model function σ0 computed in forward simulation.
  • Total is σ0 wind retrieval is our baseline algorithm.
  • In future we may use the area-gain weighted model function σ0
l2 faraday and cross talk mitigation process flow
L2 Faraday and Cross-Talk Mitigation Process Flow


(σHH, σHV, σVV)

Explicit fit trained on scale -model antenna patterns

Cross-Talk Correction

Cross-Talk Corrected:

(σHH, σHV, σVV)

2d non-linear minimization problem

Ancillary Inputs:

Faraday rotation angle




(σHH, σHV, σVV)

Faraday Rotation Correction


(ρHHVV, fHHHV, fVVHV ) per beam.



cross talk correction
Cross-Talk Correction
  • Training data:
    • Forward simulated data with nominal antenna model.
    • Forward simulated data where cross-talk explicitly set to zero in beam integration. (This was done in a way to conserve total σ0 at level 2).
  • Computing the Fit:
    • Perform a least-squares fit of the HV σ0 in the absence of cross-talk to a simple distortion model.
    • Perform a second least-squares fit to determine how to distribute the remaining σ0 into the co-polarized channels.
    • Yields an explicit 3 parameter (α, β, γ) fit for each beam

Simplified Distortion Model:

cross talk correction beam 1
Cross-Talk Correction - Beam 1

No cross-talk correction

With cross-talk correction


cross talk correction beam 2
Cross-Talk Correction – Beam 2

With correction

No correction


cross talk correction beam 3
Cross-Talk Correction - Beam 3

No correction

With correction


faraday rotation correction
Faraday Rotation Correction
  • Inputs:
    • Faraday rotation angle.
    • Observed HH, HV, VV σ0. (symmetrized cross-pol)
    • HH-VV correlation; ratio of HV to both HH and VV channels. This factor may need to be tuned depending on if cross-talk removal is or is not performed before Faraday rotation correction.
  • Method:
    • Non-linear measurement model.
    • Minimize cost function to solve for Faraday rotation corrected σ0 HH and σ0 VV. (called sigma true below).
    • Obtain σ0 HV via conservation of total σ0.

Measurement Model:

Cost Function

faraday rotation correction beam 1
Faraday Rotation Correction – Beam 1

No correction

With correction

No correction

With correction

faraday rotation correction beam 2
Faraday Rotation Correction – Beam 2

With correction

No correction

With correction

No correction

faraday rotation correction beam 3
Faraday Rotation Correction – Beam 3

No correction

With correction

No correction

With correction

open issues future work
Open Issues / Future Work
  • Antenna patterns:
    • The cross-talk from the theory and scale-model antenna patterns seems to be significantly different.
    • Will the cross-talk in the as-flown configuration differ from both the theory and scale-model patterns?
  • The error estimate for Faraday rotation correction needs to be analyzed for nominal ionospheric TEC, not worst case.
  • We need to develop a strategy to determine antenna patterns post-launch.
level 2 scatterometer wind retrieval

Level 2 Scatterometer Wind Retrieval

Alex Fore

Julian Chaubell

Adam Freedman

Simon Yueh

l2 wind retrieval process flow
L2 Wind Retrieval Process Flow

Baseline algorithm:

-total σ0 approach.

-Faraday rotation and cross-talk has no effect on total σ0 approach.

Ancillary Inputs:

-NCEP wind direction


-Total σ0

-antenna azimuth

-Kpc estimate

L2 Scat wind speed + error

Solve for wind speed

Newton’s Method:

1d root-finding problem

Newton’s Method

Wind Model Function

-input: wind speed, relative azimuth angle, incidence angle (or beam #)

-output: total sigma-0

l2 wind retrieval
L2 Wind Retrieval
  • We also compute a wind speed error due to the uncertainty in the scatterometer σ0,tot.
    • From the estimated kpc we have the variance of the observed σ0,tot.
    • We numerically compute dw/dσ0tot and propagate the error to a variance for wind.
simulated total 0 wind retrieval performance
Simulated Total σ0 Wind Retrieval Performance
  • Total σ0 performance is independent of any Faraday rotation corrections or cross-talk removal.
  • As compared to beam-center NCEP wind speed:
    • B1 total std: 0.205 m/s
    • B2 total std: 0.186 m/s
    • B3 total std: 0.226 m/s
  • By construction, when we derive the model function from the data there will be no bias.
open issues future work1
Open Issues / Future Work
  • Derivation of model function from the data.
  • Re-perform the analysis using averaged wind over 3-dB footprint as the truth for training
  • Comparison of predicted σwind to observed RMSE of retrieved wind as compared to beam center wind.
  • Use individual polarizations to retrieve winds after calibration of individual channels.
level 2 scatterometer delta tb estimation

Level 2 Scatterometer Delta TB Estimation

Alex Fore

Adam Freedman

Simon Yueh

pals highwinds 2009 campaign
PALS HIGHWINDS 2009 Campaign
  • NASA/JPL conducted HIGHWINDS 2009 campaign with following instruments:
    • POLSCAT, a Ku band scatterometer.
    • PALS, a L-band scatterometer and radiometer.
  • From POLSCAT we determine the wind speed, and then we consider the relationship to the observed L-band active and passive observations
    • From this data we can show the high correlation between radar σ0 and excess TB due to wind speed.
    • We also can derive the wind speed - radar σ0 model function as well as the wind speed – ΔTB model function.
pals highwinds results
  • We find very high correlation between wind speed and TB( > 0.95 ).
  • We also find a similarly high correlation between radar backscatter and TB.
    • Suggests radar σ0 is a very good indicator of excess TB due to wind speed.
    • Caveat: we need ancillary wind direction information for Aquarius: PALS results show a significant dependence on relative angle between the wind and antenna azimuth.
pals highwinds results 3
  • From all of the data we derived a fit of the excess TB wind speed slope as a function of Θinc.
l2 t b
  • L2 ΔTB will be the scatterometer wind speed times the PALS dTB/dw. (Note: not included in v1 delivery)
    • We estimate the ΔTB errors due to the wind RMSE numbers on previous slide.

PALS Tb relation:

comparison with previous measurements
Comparison with Previous Measurements
  • Horizontal polarization has very good agreement with the measurements from WISE ground-based campaign.
  • Large discrepancy for vertical polarization
    • Cause is uncertain
    • Wave effects?
  • WISE – Camps et al., TGRS 2004
  • Hollinger – TGE, 1971
  • Swift – Swift, Radio Science, 1974
open issues future work2
Open Issues / Future Work
  • The wind speed - ΔTB coefficients will be updated with Aquarius data after launch.