estimating smos error structure using triple collocation.ppt
DESCRIPTION
TRANSCRIPT
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?
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 ?
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
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
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
Number of triplets for 2010
82) Datasets Chosen datasets Number of triplets
Difficulties for regions with mountains, forests, wetlands, …
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).
Std of AMSR-E(VUA) errors
103) Global maps of …
relative errors bias scaling factors
Good results in the same areas as SMOS.
Std of ECMWF errors
113) Global maps of …
relative errors bias scaling factors
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
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
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
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
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
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 ?