Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin...
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Transcript of Galactic noise model adjustment Jean-Luc Vergely (ACRI-ST) Jacqueline Boutin (LOCEAN) Xiaobin Yin...
Galactic noise model adjustment
Jean-Luc Vergely (ACRI-ST)
Jacqueline Boutin (LOCEAN)
Xiaobin Yin (LOCEAN)
Galactic model versus SMOS measurements
Modelled signal is underestimated. Bias of about 1 K.
Joe Tenerelli (CLS, 2011)
Aim of this study
• To better understand rugosity in L band
• To diagnose where the bias comes from
• To give leads in order to correct the bias
• To estimate the corrections for some operating points
The way to approach the galactic contribution
• Semi-empirical approach : to perform a new model.
• Formalization of the problem and simplification.
• Extraction of the reflected signal from relevant orbits.
• Estimation of the parameters of the new model.
Formalization of the problem (1)
Model :
2
0
00 )sin(_._.)cos(4
1_Tbgal_refl
sigal
igalsssvhvv
i
ddHTbgalVTbgalV
2
0
00 )sin(_._.)cos(4
1_Tbgal_refl
sigal
igalssshvhh
i
ddVTbgalHTbgalH
By hypothesis : St3 and St4 = 0
Formalization of the problem (2)
Weighting by the antenna lobe :
2
0
2/
2/
''' ),()( Tbgal_refl),( _lobeTbgal_refl i
i
i iiiilobeiii ddP
Ground-antenna transformation
_sol_VTbgal_refl
_sol_HTbgal_refl
cossin
sincos
_antenne_YTbgal_refl
_antenne_XTbgal_refl22
22
aa
aa
Approximation
Assumption : Incident galactic signal is unpolarized :
Tbgal_H=Tbgal_V=Tbgal
With :
2
0)sin(,,,,_Tbgal_refl
sigal
igalsssssiissV ddTbgalAV
iissvhiissvviissV ,,,,,,,,, 00
2
0)sin(,,,,_Tbgal_refl
sigal
igalsssssiissH ddTbgalAH
iisshviissvviissH ,,,,,,,,, 00
Antenna lobe affects in theory directly the bistatic coefficients at the ground level.
Approximation : to apply antenna lobe on the galactic map.
Inversion of the forward model (1)Data : SMOS Tbgal_refl_X and Tbgal_refl_Y – flat sea and roughness contribution – OTT –
atmospheric contribution
Forward model :
2
0)sin(,)1(._XYTbgal_refl
sigal
igalsssssvH ddTbgalbbA
With b=cos²(a) or b=sin²(a), a being the rotation angle ground->antenna
Inversion shall be done at the antenna level : bayesian approach as for SSS retrieval.
UNKNOWN : H and V
Inversion of the forward model (2)
Different inversion schemes :
-at small rotation angle (TB close to the track) : TBX=TBH and TBY=TBV. Possibility to retrieve independently σH and σV.
-at high rotation angle : necessity to retrieve σH and σV simultaneously
-with different parameterizations : different priors. Constraints or not on the integral. Constraint of positivity (non linear process).
-considering axisymmetric bistatic coefficients which do not depend on :
a/ WS azimuth
b/ azimuth direction of the incidence plane according to the celestial sphere.
c/ SSS and SST
Tests using simulated data
Finding specular reflection points with same relative geometry and same WS
Using SMOS data after averaging
Deconvolution with strong a priori knowledge
Residual TBs
Assumptions :
incident galactic noise is not polarized. WEF applied before reflection
Bistatic retrieval :
non parametric Bayesian approach with a priori correlation length.
SMOS data selection
• 28 descending half orbits in the south pacific in the
period 12/09/2010 – 12/10/2010 => strong galactic signal is expected.
• Selection of data : no contamination by land, TB valid, geometric rotation < 10° : TBH and TBV processed independently.
• Place the data in (ra, dec, WS, theta) super cube : average and standard deviation in each cell of the cube.
Comparison of SMOS data with current model : orbit with low wind speed.
Data presentation (4)
X polar Y polar
X polar
Data presentation (6)
Y polar
SMOS data according incidence angle (selection in ra/dec toward GC, afFOV)
Data presentation (7)
Definition of 20 cells in (WS, theta) space :
WS : 4 intervals [1.5m/s 4.5m/s], [1.5m/s 4.5m/s], [1.5m/s 4.5m/s], [1.5m/s 4.5m/s]
θ : 5 intervals [0° 10°], [10° 20°], [20° 30°], [30° 40°], [40° 50°]
Sampling in the (ra, dec) space : 0.5 °x 0.5 ° boxes
Cumul of 28 descending orbitsGalactic plane crosses the orbits at different x_swath positions :
12/10/201029/09/201014/09/2010
RA
DEC
Data presentation (8)Data averaging in cells. Polar X
Average data. WS = 3m/s , θ=15° Average data. WS = 6m/s, θ=45°
Data presentation (9)Data averaging in cells : Statistic properties.
Data number. WS = 3m/s , θ=15° Data number. WS = 6m/s, θ=45°
Data presentation (10)Data averaging in cells : Statistic properties.
Exemple of TB histogram from one cell in the cube (ra,dec,WS,theta)
Std of histogram is expected to be close to the radiometric noise. Effective std is between 2.3 and 3 K for 100 data => error of the mean is about 0.3 K
Bias in the current model : where it comes from ?
• Bias in the modelled bistatic coefficients ?• Wrong assumptions (lobe, axisymmetry) ?• Bias in the ECMWF auxiliary data (wind speed) ?• Bias in the OTT correction ?• Bias in the galactic map ?• Bias in the L1 reconstruction ?• Bias due to the target heterogeneity ?• Other sources ?