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Land Data Assimilation

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Presentation on theme: "Land Data Assimilation"— 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 Canopy structure

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

5 PROSAIL Combines 4-stream canopy model SAIL
Calculates the diffuse and direct reflectance and transmittance of the whole canopy using: Solar/viewing angle Leaf area index (m2/m2) Leaf angle distribution Soil reflectance Leaf reflectance/transmittance (Verhoef et al. 2007) with leaf optics model PROSPECT 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/cm2) Dry matter content (g/cm2) Equivalent water thickness (cm) Brown pigment – canopy RT model: no vertical variation in structure (Jacquemoud & Ustin 2008)

6 Factors affecting reflectance
Leaf chlorophyll concentration Photosynthetically active radiation (PAR) nm Leaf area index (LAI) Leaf angle 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. 2012 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. 2008 Whole-stand measurements from an temperate broadleaf forest Wang & Li 2013 Leaf chlorophyll and water concentrations highest at the top of the canopy

8 Multi-layered PROSAIL
Canopy structural properties and leaf optical properties are constant within a layer Properties vary between layers to represent vertical heterogeneity To allow simulation of different reflective properties at different heights within a canopy, create a model consisting of layered slabs of homogeneous canopy using SAIL structure. SOIL

9 Multi-layered PROSAIL
z=0 z=-1 Tu,1 Td,1 Td,2 Rt,2 Rb,1 Rt,1 layer 1 layer 2 Reflectance/transmittance of two layers combined: 𝑇 𝑑 ∗ = 𝑇 𝑑,2 𝐼− 𝑅 𝑏,1 𝑅 𝑡,2 −1 𝑇 𝑑,1 𝑅 𝑏 ∗ = 𝑅 𝑏,2 + 𝑇 𝑑,2 𝐼− 𝑅 𝑏,1 𝑅 𝑡,2 −1 𝑅 𝑏,1 𝑇 𝑢,1 𝑅 𝑡 ∗ = 𝑅 𝑡,1 + 𝑇 𝑢,1 𝐼− 𝑅 𝑡,2 𝑅 𝑏,1 −1 𝑅 𝑡,2 𝑇 𝑑,1 𝑇 𝑢 ∗ = 𝑇 𝑢,1 𝐼− 𝑅 𝑡,2 𝑅 𝑏,1 −1 𝑇 𝑢,2 Combining reflectance and transmittance of multiple layers

10 Vertical variation in leaf angle
homogeneous canopy structure decline in leaf angle with height Diffuse fluxes only. Simulation results show effect on canopy reflectance* of vertical variation in leaf area and angle compared to homogeneous canopy with same total LAI (3), same average leaf angle (40°) Top of canopy Bottom of canopy

11 Variation in leaf chlorophyll
homogeneous canopy structure decline in leaf chlorophyll with height Simulated effect on canopy reflectance of vertical variation in leaf chlorophyll concentration compared to homogeneous canopy with same average concentration (80μg/cm2) Top of canopy Bottom of canopy Small decrease in reflectance in PAR region

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

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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