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
Task 2.2: Vegetation Structure
Task 2.3: Optical RT modelling
CANOPY STRUCTURE
Typical observation operator
1D-RT model of the canopy
Very simple canopy structure: Vertical homogeneity in leaf
size, arrangement and reflective properties
PROSAIL
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)
Combines 4-stream canopy model SAIL
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
(Jacquemoud & Ustin 2008)
with leaf optics model PROSPECT
Factors affecting reflectance
Leaf area index (LAI)
Simulations using PROSAIL
Leaf angle
Leaf chlorophyll
concentration
Photosynthetically
active radiation
(PAR) 400-700 nm
Observed vertical structure
Assuming vertical homogeneity is often not valid for real canopies:
Leaves are often more
upright at the top of the
canopy and flatter at
the bottom
Within-crown measurements
from a temperate evergreen
broadleaf species
Coomes et al. 2012
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
Leaf chlorophyll and water
concentrations highest at the top
of the canopy
Whole-stand measurements from an
temperate broadleaf forest
Wang & Li 2013
Multi-layered PROSAIL
Canopy structural
properties and leaf
optical properties are
constant within a layer
Properties vary
between layers to
represent vertical
heterogeneity
SOIL
Multi-layered PROSAIL
Rt,1
z=0
layer 1
Td,1
Tu,1
Rt,2
z=-1
𝑇𝑑∗ = 𝑇𝑑,2 𝐼 − 𝑅𝑏,1 𝑅𝑡,2
Rt,2
Rb,1
layer 2
Tu,1
Td,2
Reflectance/transmittance
of two layers combined:
Rb,1
Td,2
−1
𝑇𝑑,1
𝑅𝑏∗
= 𝑅𝑏,2 + 𝑇𝑑,2 𝐼 − 𝑅𝑏,1 𝑅𝑡,2
−1
𝑅𝑡∗ = 𝑅𝑡,1 + 𝑇𝑢,1 𝐼 − 𝑅𝑡,2 𝑅𝑏,1
𝑇𝑢∗ = 𝑇𝑢,1 𝐼 − 𝑅𝑡,2 𝑅𝑏,1
𝑅𝑏,1 𝑇𝑢,1
−1
−1
𝑅𝑡,2 𝑇𝑑,1
𝑇𝑢,2
Vertical variation in leaf angle
homogeneous canopy structure
decline in leaf angle with height
Top of
canopy
Bottom of
canopy
Variation in leaf chlorophyll
homogeneous canopy structure
decline in leaf chlorophyll with height
Top of
canopy
Bottom of
canopy
Small decrease in reflectance in PAR region
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
Task 2.1: Process model development
DA ASSIMILATION WITH JULES
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
Loop over all particles, x
x* = random particle
y = observations
α = min 1,
L(y|x*)
L(y|x)
Draw z from U(0,1)
x=
x* if z≤α
x if z> α
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