High Resolution Soil Moisture Estimation via Data Assimilation Using NASA Land Information System
Land Data Assimilation
description
Transcript of Land Data Assimilation
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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 StructureTask 2.3: Optical RT modelling
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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
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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
(Jacq
uem
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
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Factors affecting reflectance
Leaf area index (LAI) Leaf angle Leaf chlorophyll concentration
Photosynthetically active radiation (PAR) 400-700 nm
Simulations using PROSAIL
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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
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SOIL
Multi-layered PROSAIL
Canopy structural properties and leaf optical properties are constant within a layer
Properties vary between layers to represent vertical heterogeneity
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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:
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Vertical variation in leaf angle homogeneous canopy structure
decline in leaf angle with height
Top of canopy
Bottom of canopy
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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
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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 JULESTask 2.1: Process model development
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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,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
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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