T. Quaife, P. Lewis, M. Williams, M. Disney and M. De Kauwe.

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Presentation transcript:

T. Quaife, P. Lewis, M. Williams, M. Disney and M. De Kauwe. Assimilating Earth Observation Data into a Vegetation Model using an Ensemble Kalman Filter T. Quaife, P. Lewis, M. Williams, M. Disney and M. De Kauwe.

DALEC Cf Csom/cwd Clit Cr Cw GPP Af Ar Aw Ra Lf Lr Rh D

DALEC NEP 1996-1998

Ensemble Kalman Filter Aa = A + A′A′THT(HA′A′THT + Re)-1(D - HA) H = observation operator A = state vector ensemble A′ = state vector ensemble – mean state vector D = observation ensemble Re = observation error covariance matrix

Strategies for assimilation Assimilate EO products Probably noisey Linear observation operator Assimilate reflectance Errors more easily characterised Non linear observation operator

The “Twin experiment” Use a more complex model to represent the “truth” Simulate observations from truth model Asses ability of DALEC/EnKF to make accurate predictions

SDGVM Max Evaporation Soil Moisture Litter Transpiration LAI Soil C & N NPP H2O30 Phenology Hydrology Century Growth

DALEC & SDGVM NEP

NEP - Assimilating modelled 30 day LAI

NEP - Assimilating 30 day FASIR LAI

LAI – no assimilation

LAI – SDGVM assimilation

LAI – FASIR assimilation

EnKF – augmented analysis Aa = A + A′Â′TĤT(ĤÂ′Â′TĤT + Re)-1(D - ĤÂ) Ĥ = augmented observation operator Â = augmented state vector ensemble Â = h( A )

Non-linear observation operator NDVI = a0 × ( 1 – e( -a1 × LAI ) ) Regressing the FASIR LAI against the FASIR NDVI: a0 = 0.678 a1 = 0.982

NEP - Assimilating FASIR NDVI

LAI - Assimilating FASIR NDVI

Conclusions Test exercise very promising Demonstrates ability to use non-linear observation operators Next step is to couple a full CRM to DALEC to enable assimilation of reflectance data Accurate characterisation of errors is critical Models very different Improve DALEC Seek other data