ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July...
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Transcript of ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July...
![Page 1: ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELS Tristan Quaife DARC seminar 11 th July 2012.](https://reader035.fdocuments.us/reader035/viewer/2022062404/5515f93f55034694308b4867/html5/thumbnails/1.jpg)
ASSIMILATING EARTH OBSERVATION DATA INTO VEGETATION MODELSTristan QuaifeDARC seminar 11th July 2012
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Some context – the residual sink
http://www.whrc.org/global/carbon/residual.html
PgC
yr-1
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Some context – Cox et al. 2000
The red lines represent the fully coupled climate/carbon-cycle simulation, and the blue lines are from the 'offline' simulation which neglects direct CO2-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, 184-187
Ch
an
ge
in
ve
ge
tati
on
ca
rbo
n (
GtC
)
0 0
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The Land Surface DA problem At first glance similar to NWP DA
problem. Of the form:
xt+1=M(xt, p, dt) 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
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Assimilating products
Data Assimilation Scheme(KF, EnKF, 4DVAR, etc)
MODEL
Assumptions
Observations
Observations
Assumptions
Assumptions
For example: soil moisture from SMOS or photosynthesis (GPP) from MODIS
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MODIS GPP/PSN
http://www.ntsg.umt.edu/remote_sensing/netprimary/
MODIS data
Climate data
Look up table
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Data Assimilation Scheme(KF, EnKF, 4DVAR, etc)
Observations
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):1347-1364
e.g. reflectance, backscatter, etc…
Assimilating low level data
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Vegetation
Foliage
Humus
LitterRoots
Wood
GPP
Af
Ar
Aw
Ra
Lf
Lr
Lw
Rh
DMet Data
Soil
DALEC
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Ensemble Kalman Filter
Aa = A + A′A′THT(HA′A′THT + Re)-1(D - HA)
H = observation operatorA = state vector ensembleA′ = state vector ensemble – mean state vectorD = observation ensembleRe = observation error covariance matrix
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EnKF – augmented analysis
Aa = A + A′Â′TĤT(ĤÂ′Â′TĤT + Re)-1(D - ĤÂ)
Ĥ = augmented observation operator = augmented state vector ensemble
 = h(A) A
h is a canopy reflectance model
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Simple observation operator
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Observation operator
Source: N Gobron, JRC
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Shaded crownIlluminated
crown
Illuminated soil
Shaded soil
Geometric Observation Operator
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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):1347-1364
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Assimilating reflectance into DALEC
No assimilation
Assimilating MODIS surface reflectance bands 1 and 2
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Carbon balance for 2000-2002
15 65
gC/m2/year
4.5 km
Flux Tower
Spatial average = 50.9
Std. dev. = 9.7
(gC/m2/year)
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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 CO2 observations
Have taken a different approach for some problems: Use models driven by satellite data Assimilate available ground data
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Mountain pine beetle
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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
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Posterior PDF
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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.
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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
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f = kernel weightK = kernel valuen = number of kernels
λ = wavelengthρ = BRFΩ = view geometryΩ' = illumination geometry
Kernel driven BRDF model
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f = (KTC-1K)-1KTC-1ρ
• 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
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MODIS data product (MOD43)
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MODIS data product (MOD43)
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f = (KTC-1K + γ2BTB)-1KTC-1ρ
B is the required constraint. It imposes:
Bf = 0
and the scalar γ is a weighting on that constraint.
Constrained formulation
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Constraint matrix
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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. 2445-2450.
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EOLDAS
European Space Agency Project to improve data retrievals and inter-sensor calibration
Variational scheme using the following cost function:
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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
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Spatial DA example – Synthetic Truth
NDVISource: P Lewis & J Gomez-Dans, UCL
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Spatial DA example – Observations
NDVISource: P Lewis & J Gomez-Dans, UCL
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Spatial DA example – Posterior
NDVISource: P Lewis & J Gomez-Dans, UCL
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Multi-scale DA using a particle filter
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Multi-scale DA using a particle filter
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, D08117.
Leaf Area Index:
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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
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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…
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Any Questions?