Presentation on theme: "Mat Disney and Shaun Quegan EO data assimilation in land process models No one trusts a model except the man who wrote it; everyone trusts an observation."— Presentation transcript:
Mat Disney and Shaun Quegan EO data assimilation in land process models No one trusts a model except the man who wrote it; everyone trusts an observation except the man who made it (Harlow Shapley)
Ciais et al IGOS-P Integrated Global Carbon Observing Strategy Concept for Global Carbon Data Assimilation System NB carbon and water are inextricably linked, so this is a more generalised vegetation – soil – water- atmosphere scheme
Terrestrial Component + Water components: SWE soil moisture Which model(s) should go here?
Land process models Land models need to deal with transfers of - energy - matter - momentum between the land surface and the atmosphere. Three classes of land (coupled carbon-water) models: Models driven by radiation (light use efficiency models) Dynamic Vegetation Models: climate driven Simple box models Some models emphasise hydrology (not discussed here)
Light Use Efficiency models Incoming PAR CO2 GPP LUE Absorption fAPAR Photo- synthesis Respiration NPP The LUE may depend on biome, soil moisture, temperature, nutrients, age, Efficiency coefficient: LUE Measured by satellites Modeled or measured by satellites
Notes on LUE models Models built by ecologists tend to focus on leaves as the functional element (e.g. Leaf Area Index). Models built by remote sensors tend to focus on radiation. LUE models are driven by EO data, rather than geared to assimilating data.
Properties of DVMs DVMs originally designed to examine long-term trends under climate change so… Data-independent, except for varying climate data and static soil texture data Comprehensive description of biophysics All processes internalised, parameterised Complex, non-linear, non-differentiable, (discontinuities, thresholds) Expensive to run
Soil texture The Structure of a Dynamic Vegetation Model Parameters Climate SnSn S n+1 DVM Processes Testing
How EO data can affect DVM calculations Parameters DVM Climate Soils SnSn S n+1 Processes Observable Land cover Forest age Phenology Snow water Burnt area Testing: Radiance fAPAR Possible feedback fAPAR
Calibration– boreal budburst Offline setting of global parameters can be thought of as a form of DA, but is better described as model calibration. In the following e.g, we use new EO observations that are unaffected by snow-melt to parameterise the spring warming boreal phenology model.
Start of budburst T0T0 min(0, T – T 0 ) > Threshold, budburst occurs. The sum is the red area. Optimise over the 2 parameters, Threshold and T 0 (minimum effective temperature). When The SDGVM budburst algorithm
The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO Day of year
Testing SDGVM with EO data SDGVM can predict satellite observations since it contains a canopy model and the concept of radiation interception
Model skill Skill BadGood 1999 SDGVM fAPAR AVHRR NDVI
Are derived parameters the problem? Is the problem the SDGVM or the derived parameter from the EO signal? The next slide shows the fAPAR derived from Seawifs (JRC) and from MODIS for a site in the UK. The large bias between the two is a general feature of these two datasets.
Biases in derived parameters
Assimilating products Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) MODEL Assumptions Observations Assumptions For example: soil moisture from SMOS, surface temperature, LAI from MODIS
Low-level vs derived products similar products give substantially different values; assumptions used to derive products usually inconsistent with biospheric models; Product uncertainties are poorly known Can we use low-level products (Reflectance? BOA radiance? TOA radiance?)
Assimilating reflectance Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) Observations MODEL Assumptions Observation Operator Assumptions Assumptions in the observation operator are made to be consistent with those in the model e.g. reflectance, backscatter, etc…
Observation operators This approach needs observation operators: translate ecosystem model state vector into observable properties e.g. reflectance data assimilated into DALEC; predicting radar coherence in ERS Tandem data from the SPA model; relating snowpack properties to SSM/I radiometer data; recognising burnt area and severity of burn.
Which is the right model? Complex DVM-type models never designed for DA So, pursuing another approach with a simplified box model designed from the start for DA – DALEC
The Structure of a Data Assimilation Model (DALEC) CfCf CrCr CwCw ClCl CsCs GPP W S1 W S2 W Sn ET Ppt Q RhRh RaRa Stocks and fluxes of carbon (left) and water (right) EO data (e.g. LAI, VI, reflectance) Observation model Ensemble Kalman Filter Blue lines indicate integration of EO data with DALEC
Observation operator: simple RT model + snow
Canopy foliage results No assimilation Assimilating MODIS (bands 1 and 2)
Canopy foliage results Assimilating MODIS inc. snow Assimilating MODIS exc. snow Quaife, Williams, Disney et al. RSE in press
EO land cover and carbon Quaife, Quegan, Disney et al., submitted All EO land cover the same? DGVMs use land cover indirectly – How do we translate land cover classes to PFTs?
EO land cover and carbon Quaife, Quegan, Disney et al., submitted
How do we find best model-data framework? Use God models to test assumptions of simpler models – DVMs + DALEC-type models Model-data fusion inter-comparison e.g. REFLEX: Regional Flux Estimation Experiment – – Compare strengths/weaknesses of various model- data fusion techniques – Quantify errors/biases introduced when extrapolating fluxes in both space and time using a model constrained by model-data fusion methods.
Key issues for DA in land models 1 Models – Simple enough for effective DA but complex enough to capture biophysics – Suitable interface with observation operators – preferably differentiable
Key issues for DA in land models 2 Data – Same meaning of observed parameters as used in models – Proper characterisation of uncertainty i.e. PDFs – Use OOs to make best use of all available data e.g. optical, LiDAR, RADAR, thermal …. We are still searching for the best model-data structure.
Key issues for DA in land models 3 DA through observation operators not only answer, for various practical reasons. Also pursue general concepts of how EO data can reduce the uncertainty in land models – Calibration, testing etc.
Severity of disagreement – AVHRR/SDGVM r > OR r.m.s.e < 0.2 r 0.2 r
Severity of disagreement – example Mid Europe
Severity of disagreement – example SW China
Lesson 1.The DVM as currently formulated only supports a simple observation operator. This allows meaningful estimates of time series of observables; absolute values of the observables are of dubious value. 2.These time series permit the model to be interrogated with satellite data, and model failures to be identified.
Detecting incorrect land cover Pearsons product moment Crop class incorrectly set Crop class correctly set Temporal correlation
Lesson Forward operators may prove a powerful tool in land cover mapping
Impact on Carbon Calculations Picard et al.,GCB, day advance: NPP increases by 10.1 gCm -2 yr days advance: 38% bias in annual NPP Observations Phenology model Dynamic Vegetation Model Carbon Calculation calibrate Calibrated model is unbiased, unlike methods based on NDVI
Model needs to be region specific, here include chilling requirement ? Comparison Model-EO: RMSE
NDVI predicted by SDGVM
NBP LEACHED Litter Disturbance ATMOSPHERIC CO 2 BIOPHYSICS Soil Photosynthesis GROWTH Biomass GPPNPP Thinning Mortality Fire A Dynamic Vegetation Model (SDGVM)
Assimilating reflectance Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) MODEL Observations Assumptions But how do we use a non- linear observation operator? The real world
May 99 August 99 Comparing model and measured fAPAR Seawifs SDGVM
Model and predicted fAPAR Average over the whole of Europe for 1999 and 2000 Note: if SDGVM were driven by the Seawifs values, most model forests would die
Experiments State and parameter estimation. DE1 and EV1 sites, 3 years driving data, all available obs As 1. but using synthetic data (DE2 and EV2) Within site forecasting. Another year of driving data for DE1 and EV1, but no observations As 3. but using synthetic data (DE2 and EV2) Between site extrapolation. DE3 and EV3 sites, 4 years driving data, MODIS LAI only
Integrated flux predictions Flux (gC.m -2 ) Assimilated data Total Standard Deviation NEP Assimilation exc. snow Assimilation inc. snow Williams et al. (2005) GPP Assimilation exc. snow Assimilation inc. snow Williams et al. (2005)
REFLEX data sets Paired sites to test extrapolation/estimation – Brasschaat (DE2) and Vielsalm (EV2) (MF) – Hainich (DE3) and Hesse (DE1) (DBF) – Loobos (EV1) and Tharandt (EV3) (ENF) Meteorological drivers, fluxes, MODIS LAI and stocks – Attempting to estimate uncertainty in fluxes and MODIS LAI
REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS MDF Full analysis Model parameters DALEC model Training Runs Deciduous forest sites Coniferous forest sites Assimilation Output
REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS MDF Full analysis Model parameters DALEC model Testing site forecasts with limited EO data MDF MODIS Analysis FluxNet data testing Assimilation