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Land Data Assimilation Tristan Quaife, Emily Lines, Philip Lewis, Jon Styles.

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Presentation on theme: "Land Data Assimilation Tristan Quaife, Emily Lines, Philip Lewis, Jon Styles."— Presentation transcript:

1 Land Data Assimilation Tristan Quaife, Emily Lines, Philip Lewis, Jon Styles.

2 Last 6 month highlights Implemented vertical heterogeneity in vegetation structure for land surface model RT schemes and observation operators Implemented a particle filter for JULES

3 CANOPY STRUCTURE Task 2.2: Vegetation Structure Task 2.3: Optical RT modelling

4 Typical observation operator 1D-RT model of the canopy Very simple canopy structure: Vertical homogeneity in leaf size, arrangement and reflective properties

5 Calculates the reflectance and transmittance of a single leaf using a plate model dependent on: Internal leaf mesophyll structure Chlorphyll a+b and carotenoid content (μg/cm 2 ) Dry matter content (g/cm 2 ) Equivalent water thickness (cm) Brown pigment PROSAIL Combines 4-stream canopy model SAIL (Jacquemoud & Ustin 2008) with leaf optics model PROSPECT (Verhoef et al. 2007) Calculates the diffuse and direct reflectance and transmittance of the whole canopy using: Solar/viewing angle Leaf area index (m 2 /m 2 ) Leaf angle distribution Soil reflectance Leaf reflectance/transmittance

6 Factors affecting reflectance Leaf area index (LAI)Leaf angle Leaf chlorophyll concentration Photosynthetically active radiation (PAR) nm Simulations using PROSAIL

7 Observed vertical structure Assuming vertical homogeneity is often not valid for real canopies: Within-crown measurements from a temperate evergreen broadleaf species Coomes et al Leaves are often more upright at the top of the canopy and flatter at the bottom Higher proportion of LAI found higher in the canopy, and leaves have higher mass/unit leaf area (LMA) Whole-stand measurements from a temperate evergreen broadleaf forest Holdaway et al Whole-stand measurements from an temperate broadleaf forest Wang & Li 2013 Leaf chlorophyll and water concentrations highest at the top of the canopy

8 SOIL Multi-layered PROSAIL Canopy structural properties and leaf optical properties are constant within a layer Properties vary between layers to represent vertical heterogeneity

9 Multi-layered PROSAIL z=0 z=-1 T u,1 T d,1 T d,2 R t,2 R b,1 R t,1 layer 1 layer 2 Reflectance/transmittance of two layers combined:

10 Vertical variation in leaf angle homogeneous canopy structure decline in leaf angle with height Top of canopy Bottom of canopy

11 Variation in leaf chlorophyll Top of canopy Bottom of canopy Small decrease in reflectance in PAR region homogeneous canopy structure decline in leaf chlorophyll with height

12 Does this matter for LS models? fAPAR is key biophysical variable for calculating primary productivity Vertical structural heterogeneity affects light levels through the canopy Land surface schemes (e.g. JULES) typically account for variable nitrogen, but not leaf angle or pigment properties

13 DA ASSIMILATION WITH JULES Task 2.1: Process model development

14 JULES

15 JULES: Carbon Budget

16 Fluxnet

17 Flux tower observations

18 Resampling Particle Filter We have implemented a resampling particle filter for JULES Uses the Metropolis-Hasting’s algorithm to perform the resampling Implementation is very flexible – Requires no modification to the JULES code – Easy to adapt for different observations and different model configurations

19 Stochastic forcing Add noise into desired state vector elements In following examples: – Daily stochastic forcing (JULES time step = 30min) – Truncated normal distribution – Soil carbon – Soil moisture (4 vertical levels) Easy to change all of the above characteristics

20 Resampling step α = min 1, L(y|x*) L(y|x) Draw z from U(0,1) x =x = x* if z≤α x if z> α Loop over all particles, x x* = random particle y = observations

21 Particle Filter

22 Non-assimilated variables

23 Pros/Cons Pros: Fully non linear Robust to changes in JULES Easy to switch to other analysis schemes – e.g. Ensemble Kalman Filter Cons: Slow: approx 5 mins/particle/year – but algorithm is inherently parallelisable

24 NEXT 6 MONTHS

25 Immediate Finish experiments on vertical structure and implement in JULES Write up JULES Particle Filter experiments with Fluxnet data Initial experiments against EO data

26 Next 6 months Further modify JULES Sellers scheme to predict viewed crown and ground (for assimilation of long wavelength data) Build 2-stage Data Assimilation algorithm: – EOLDAS for Leaf Area temporal trajectory and other slow processes (optical data) – Particle Filter for assimilating observations related to diurnal cycle (thermal, passive microwave)

27 EOLDAS & JULES phenology JULES phenology routine is effectively separate from the rest of the model – Used to prescribe LAI profile, but not influenced by other parts of the model state – Consequently can be optimised stand-alone – Ideal application for EOLDAS – Use modified Sellers scheme as observation operator


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