Land Data Assimilation

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

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

CANOPY STRUCTURE

Task 2.2: Vegetation StructureTask 2.3: Optical RT modelling

Soil

H

zCanopy

Typical observation operator

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

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

PROSAILCombines 4-stream canopy model SAIL

(Jac

quem

oud

& U

stin

2008

)

with leaf optics model PROSPECT

(Ver

hoef

et a

l. 20

07)

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

Factors affecting reflectance

Leaf area index (LAI) Leaf angle Leaf chlorophyll concentration

Photosynthetically active radiation (PAR) 400-700 nm

Simulations using PROSAIL

Observed vertical structureAssuming vertical homogeneity is often not valid for real canopies:

Within-crown measurements from a temperate evergreen broadleaf speciesCoomes 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

SOIL

Multi-layered PROSAIL

Canopy structural properties and leaf optical properties are constant within a layer

Properties vary between layers to represent vertical heterogeneity

Multi-layered PROSAIL

z=0

z=-1

Tu,1 Tu,1Td,1

Td,2Td,2

Rt,2 Rt,2

Rb,1 Rb,1

Rt,1

layer 1

layer 2

Reflectance/transmittance of two layers combined:

Vertical variation in leaf angle homogeneous canopy structure

decline in leaf angle with height

Top of canopy

Bottom of canopy

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

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

DA ASSIMILATION WITH JULESTask 2.1: Process model development

JULES

JULES: Carbon Budget

Fluxnet

Flux tower observations

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

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

Resampling step

α = min 1,L(y|x*)

L(y|x)

Draw z from U(0,1)

x = x* if z≤αx if z> α

Loop over all particles, xx* = random particley = observations

Particle Filter

Non-assimilated variables

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

NEXT 6 MONTHS

Immediate

• Finish experiments on vertical structure and implement in JULES

• Write up JULES Particle Filter experiments with Fluxnet data

• Initial experiments against EO data

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)

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