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ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July 2012.

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Presentation on theme: "ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July 2012."— Presentation transcript:

1 ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July 2012

2 Some context – the residual sink PgCyr -1

3 Some context – Cox et al The red lines represent the fully coupled climate/carbon-cycle simulation, and the blue lines are from the 'offline' simulation which neglects direct CO 2 -induced climate change. The figure shows simulated changes in vegetation carbon (a) and soil carbon (b) for the global land area (continuous lines) and South America alone (dashed lines). Cox P et al. (2000) Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature. 408, Change in vegetation carbon (GtC) 00

4 The Land Surface DA problem At first glance similar to NWP DA problem. Of the form: x t+1 =M(x t, p, d t ) But… Observation time scales tend to be much shorter than many of the key process In general M is not fully understood and typical for many parameters to be determined empirically

5 Assimilating products Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) MODEL Assumptions Observations Assumptions For example: soil moisture from SMOS or photosynthesis (GPP) from MODIS

6 MODIS GPP/PSN MODIS data Climate data Look up table

7 Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) Observations MODEL Assumptions Observation Operator Assumptions Quaife T, Lewis P, De Kauwe M, Williams M, Law BE, Disney MI and Bowyer P (2008) Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sensing of Environment. 112(4): e.g. reflectance, backscatter, etc… Assimilating low level data

8 Vegetation Foliage Humus LitterRoots Wood GPP AfAf ArAr AwAw RaRa LfLf LrLr LwLw RhRh D Met Data Soil DALEC

9 Ensemble Kalman Filter A a = A + AA T H T (HAA T H T + R e ) -1 (D - HA) H = observation operator A = state vector ensemble A = state vector ensemble – mean state vector D = observation ensemble R e = observation error covariance matrix

10 EnKF – augmented analysis A a = A + A T Ĥ T (Ĥ T Ĥ T + R e ) -1 (D - ĤÂ) Ĥ = augmented observation operator  = augmented state vector ensemble  = h(A) A h is a canopy reflectance model

11 Simple observation operator

12 Observation operator Source: N Gobron, JRC

13 Shaded crown Illuminated crown Illuminated soil Shaded soil Geometric Observation Operator

14 Modelled vs. observed reflectance MODIS Band 1 (red) MODIS Band 2 (NIR) Quaife T, Lewis P, De Kauwe M, Williams M, Law BE, Disney MI and Bowyer P (2008) Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter. Remote Sensing of Environment. 112(4):

15 Assimilating reflectance into DALEC No assimilation Assimilating MODIS surface reflectance bands 1 and 2

16 Carbon balance for gC/m 2 /year 4.5 km Flux Tower Spatial average = 50.9 Std. dev. = 9.7 (gC/m 2 /year)

17 Parameter sensitivity Problem using optical EO data is most vegetation model parameters are not sensitive to it Broadly this is true for all EO data May change with advent of CO 2 observations Have taken a different approach for some problems: Use models driven by satellite data Assimilate available ground data

18 Mountain pine beetle

19 Mountain pine beetles Science question: what is the impact of MPB on carbon balance of ecosystem? Problem: most veg models are not adequately parameterised for mountain forests tend to exhibit quite different photosynthetic responses to temperature than other forests Use simple photosynthesis model driven by EO data Assimilate ground observations using standard MCMC-MH Bayesian parameter estimation

20 Posterior PDF

21 Mountain pine beetle Moore DJP, Trahan NA, Wilkes P, Quaife T, Desai AR, Negron JF, Stephens BB, Elder K & Monson RK (submitted 2012) Changes in carbon balance after insect disturbance in Western U.S. Forests.

22 A re-think… Started to think about how we could approach the land surface problem a little differently First, most land surface models do not have RT physics that is consistent with EO observations Make this a design goal of vegetation models A good place to start given volume of EO data Second, there may be additional constraints that are applicable specifically to the land surface Generally does not undergo rapid change

23 f= kernel weight K = kernel value n= number of kernels λ = wavelength ρ = BRF Ω = view geometry Ω' = illumination geometry Kernel driven BRDF model

24 f = (K T C -1 K) -1 K T C -1 ρ Formulation used for the NASA MODIS BRDF/albedo product (MCD43) Requires an 16 day window (Terra + Aqua) that is moved every 8 days Standard Least Squares

25 MODIS data product (MOD43)

26

27 f = (K T C -1 K + γ 2 B T B) -1 K T C -1 ρ B is the required constraint. It imposes: Bf = 0 and the scalar γ is a weighting on that constraint. Constrained formulation

28 Constraint matrix

29 Constrained result Quaife T and Lewis P (2010) Temporal Constraints on Linear BRDF Model Parameters. IEEE Transactions on Geoscience and Remote Sensing, 48 (5). pp

30 EOLDAS European Space Agency Project to improve data retrievals and inter-sensor calibration Variational scheme using the following cost function:

31 EOLDAS variational assimilation Lewis P, Gomez-Dans J, Kaminski T, Settle J, Quaife T, Gobron N, Styles J & Berger M (2012), An Earth Observation Land Data Assimilation System (EOLDAS), Remote Sensing of Environment. Leaf Area Index Chlorophyll Time

32 Spatial DA example – Synthetic Truth NDVI Source: P Lewis & J Gomez-Dans, UCL

33 Spatial DA example – Observations NDVI Source: P Lewis & J Gomez-Dans, UCL

34 Spatial DA example – Posterior NDVI Source: P Lewis & J Gomez-Dans, UCL

35 Multi-scale DA using a particle filter

36 Hill TC, Quaife T & Williams M (2011) A data assimilation method for using low-resolution Earth observation data in heterogeneous ecosystems, J. Geophys. Res., 116, D Leaf Area Index:

37 Current ESA project Project with Reading and UCL Builds on the existing EOLDAS framework Constructing a land surface scheme that includes trace gas and energy fluxes Key aim is to have the broadest possible range of EO observations available for DA Design goal to invest most complexity in the physics required for the observation operator

38 Routes to collaboration inside DARC DALEC code setup in flexible framework Already has EnKF & PF – easy to add more Easy to add non-linear observation operators Lots of test data available EOLDAS code available Official public release very soon Very general, but also very slow Lots of data for vegetation type problems available… ask me…

39 Any Questions?


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