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**Land Data Assimilation**

Tristan Quaife, Emily Lines, Philip Lewis, Jon Styles.

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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

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**Canopy structure Task 2.2: Vegetation Structure**

Task 2.3: Optical RT modelling Canopy structure

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**Typical observation operator**

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

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**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)

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**Factors affecting reflectance**

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

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**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

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**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

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**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

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**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

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**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

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**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

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**Da assimilation with jules**

Task 2.1: Process model development Da assimilation with jules

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JULES

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JULES: Carbon Budget

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Fluxnet

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**Flux tower observations**

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**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

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**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

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**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 =

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Particle Filter

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**Non-assimilated variables**

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**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

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Next 6 months

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

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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)

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**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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