estimating smos error structure using triple collocation delphine leroux, cesbio, france yann kerr,...
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Estimating SMOS error structure using triple
collocation
Delphine Leroux, CESBIO, FranceYann Kerr, CESBIO, FrancePhilippe Richaume, CESBIO, France
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Soil moisture products at global scale
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AMSR-E (NSIDC)
ERS-ASCAT
(TU Wien)
Model output
(ECMWF)
AMSR-E
(VUA)
TMI (VUA)
SSM/I (VUA)
Aquarius
SMAP
How to evaluate SMOS ???
SMOS?
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Inter comparison between SMOS soil moisture and …
o Ground measurements (point scale)
o Other global products (point scale)
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Statistics -> triple collocationo Global scale ?
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Structure
1. Triple Collocation method-> Theory and requirements
2. Chosen datasets-> Characteristics and differences
3. Global maps of relative errors-> Maps of errors-> Maps of bias and scale factors
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Triple Collocation – theory (Caires et al., 2003)
Starting equation
Taking the anomalies
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Final equation
Maps of the std of the errors
Maps of the bias Maps of the scale factors
1) Triple Collocation
Theory Requirements
r: bias s: scale factor ε: error
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Triple Collocation - requirements
oStrong assumptions : Mutually independent errors
(ε) No systematic bias between
the datasets
o Requirements : 100 common dates
(Scipal et al., IGARSS 2010)
o Results : Relative errors
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-> choose properly the 3 datasets-> TC applied to the anomalies and not to the variables directly
-> including the 6 closest grid nodes
1) Triple Collocation
Theory Requirements
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Datasets
Frequency (GHz)
Incidence angle (°)
Instrument resolution (km)
Crossing time (A/D)
Grid resolution (km)
SMOS 1.4 0-55 40 6am / 6pm
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AMSR-E 6.9 – 10.7 - …
55 57-6.25 1:30pm/ 1:30am
25
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AMSR-E soil moisture derived with the VUA algorithm (Vrije University of Amsterdam)
ECMWF product from SMOS Level 2 product (at SMOS resolution and crossing time)
2) Datasets Chosen datasets Number of triplets
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Number of triplets for 2010
82) Datasets Chosen datasets Number of triplets
Difficulties for regions with mountains, forests, wetlands, …
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Std of SMOS errors
93) Global maps of …
relative errors bias scaling factors
Good results in North America, North Africa, Middle East, Australia.Land contamination in Asia (Richaume et al., RAQRS, 2010).
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Std of AMSR-E(VUA) errors
103) Global maps of …
relative errors bias scaling factors
Good results in the same areas as SMOS.
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Std of ECMWF errors
113) Global maps of …
relative errors bias scaling factors
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Comparison over continents
123) Global maps of …
relative errors bias scaling factors
RELATIVE ERRORS!
SMOS is often between or close to the two values except in Asia
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133) Global maps of …
relative errors bias scaling factors
Bias : AMSR-E(VUA) - SMOS
Very high bias for high latitudes (mainly due to the vegetation)Mean bias around 0.1
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143) Global maps of …
relative errors bias scaling factors
Bias : ECMWF - SMOS
High bias for high latitudes but more homogeneousMean bias around 0.2-0.3
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Scale factor AMSR-E(VUA)
153) Global maps of …
relative errors bias scaling factors
Scale >1 higher dynamic than SMOSScale <1 lower dynamic than SMOS
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Scale factor ECMWF
163) Global maps of …
relative errors bias scaling factors
Unlike the bias maps, there is no obvious structure for the scale factor
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Conclusions
o As part of the validation process, triple collocation compares 3 different datasets at a global scale : SMOS, AMSR-E/VUA and ECMWF
o SMOS and AMSR-E/VUA have the same performance areas, but ECMWF and VUA give the best results
o SMOS algorithm is still improving and it can be considered as a good start
o Further work : apply triple collocation to other triplets (SMOS-NSIDC-ASCAT, etc…) and apply it with 2011 data
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Thank you for your attention
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Any questions ?