Simulation based METOP-A/AVHRR SST algorithms Sonia Péré, Hervé Roquet, Pierre LeBorgne Centre de...

Simulation based METOP-A/AVHRR SST algorithms

Sonia Péré, Hervé Roquet, Pierre LeBorgne Centre de Météorologie Spatiale, Météo-France, Lannion, France

SST from polar orbiters,OSI-SAF WORKSHOP,Lannion 5-6 March 2013

Outline

Objectives Data Nighttime algorithms

– Residual errors

– Validation errors

Daytime algorithms– Residual errors

– Validation errors

Conclusion


Objectives

SAFREE (Francois et al, 2002) has been extensively used for OSI-SAF algorithm determination.

Alternate (ECMWF) profile data bases can be used (Hervé’s talk)– « Chevallier_Ccmax »: Cloud Cover <0.4

– « Chevallier_RH »: RH< 95% (levels 54-91): « clear sky » only

– ECMWF output sampling + cloudiness filtering (« CEPMMT »)

To prepare the upgrade of polar orbiter SST chains:Our objective is a first testing of the impact of profile database on algorithm performances through:

Internal evaluation (on simulations) MDB validation + comparison with « optimal » algorithm

The formalisms considered are those used operationally for METOP-A processing.


Data

SAFREE 402 profiles Chevallier_CCmax 1499 profiles Chevallier_RHmax 1183 profiles CEPMMT 2108 profiles

METOP-A MDB period: 2011/12 to 2012/12– Global area; qual 3-5; no sdi

– Subset used for « optimal » algo determination: no sdi+ validation box cov. > 0.6


Geographical distribution

Chevallier_CCmax Chevallier_RHmax


Tskin –T11 distribution vs T11-T12 at secant =1

Color scale: Wwater vapour content

SAFREE CEPMMT Chevallier_CCmax

Chevallier_RHmax

Cloudiness inducedexcessive water vapour content


Nighttime algorithms

sst=((a+(b*(sec-1)))*bt37)+((c+(d*(sec-1)))*(bt108-bt120))+(e*(sec-1))+f

algo bias std nbcas

SAFREE 0.306 5535

cepmmt 0.304 10205

Chev+RHmax 0.329 5440Residual st dev

algo bias std nbcas

SAFREE 0.256 0.320 5535

cepmmt 0.276 0.308 10205

Chev+RHmax 0.166 0.334 5440

Operational applied to simulation

Optimal (mdb)

0.385 143456

Coefficients obtained by regression on simulated bTS + noise


Residual errors vs lat

SAFREE Chevallier Rhmax CEPMMT

Nightime algorithm residual errors (tsalgo-tskin)


zone Global

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

optimal cepmmt chev_Rhmax safree

bias

std

Nighttime algorithm: MDB validation results

215 232 cases



optimal -0,039 0,412 215532cepmmt -0,149 0,431 215532chev_Rhmax -0,077 0,431 215532safree -0,14 0,441 215532

Glo

Validation map : Optimal




Glo

Validation map : SAFREE



Glo


Validation map : Chevallier_RH



Glo


Validation map : CEPMMMT


Err vs msr Err vs t11-t12

err vs satzenErr vs Satzen Err vs lat

Algo optimalChev_RhmaxSAFREECEPMMT


Daytime algorithms

Sst_NLC=(a+b*(sec-1))*bt108+(c+d*clim+e*(sec-1))*(bt108-bt120)+f*(sec-1)+g

Algo NLC bias std nbcas

SAFREE 0.821 4635

CEPMMT 0.611 10025

Chev+rhmax 0.818 5440Residual st.dev.

Algo NLC bias std nbcas

SAFREE 0.015 0.853 4635

CEPMMT 0.040 0.659 10025

Chev+RHmax -0.051 0.888 5440

Operational applied to simulation

optimal 0.539 143456


Daytime algorithm residual errors (tsalgo-tskin)

Distribution map of residual errors : SAFREE



Distribution map of residual errors : Chevallier RH



Distribution map of residual errors : CEPMMT


Residual errors vs lat

SAFREE Chevallier Rhmax CEPMMT



zone Global

-0,5-0,4-0,3-0,2-0,1

00,10,20,30,40,50,60,7

optimal cepmmt chev_Rhmax safree

bias

std

Daytime algorithm: MDB validation results

nbcas: 318528


bias std nbcasoptimal 0,004 0,544 318528cepmmt -0,284 0,575 318528chev_Rhmax -0,128 0,618 318528safree -0,009 0,628 318528

Glo


Validation map : Optimal




Glo

Validation map : Safree



Glo


Validation map : Chevallier RH max



Glo


Validation map : CEPMMT


Err vs msr Err vs t11-t12

Err vs Satzen Err vs lat

Algo optimalChev_RhmaxSAFREECEPMMT


Conclusions

CEPMMT («natural» sampling) produces a more robust algorithm than Chevallier («variability» driven)

CEPMMT derived algorithms shows standard deviations close to optimal

All algorithms show regional errors and need a bias correction when applied in the « true » world

Further work– Introduce simulation adjustment before regression

– Analyze other formalisms?

Simulation based METOP-A/AVHRR SST algorithms Sonia Péré, Hervé Roquet, Pierre LeBorgne Centre de...

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