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Land Data Assimilation. Tristan Quaife , Emily Lines, Ian Davenport, Jon Styles, Philip Lewis Robert Gurney. Rationale . Satellite data one of the most powerful observational constraints on land surface models Synoptic spatial and temporal coverage

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land data assimilation

Land Data Assimilation

Tristan Quaife, Emily Lines, Ian Davenport, Jon Styles, Philip Lewis Robert Gurney.

  • Satellite data one of the most powerful observational constraints on land surface models
    • Synoptic spatial and temporal coverage
    • Direct measure of energy leaving the system
  • Highly derived satellite ‘products’ often physically inconsistent with assumptions in LSMs and difficult to quantify uncertainty
    • Hence want to use low-level data (e.g. radiance)
  • Most LSMs lack appropriate physical representation required for this
    • For example typically 1D turbid medium canopies
land components of earth system models
Land components of earth system models
  • Developed for NWP – extended for other studies
  • Fluxes added for carbon, nutrients, aerosols



Radiation interactions with

atm, veg. and soil

Precipitation &


Groundwater and

Channel flows

Developed to calculate the exchange of energy and water between the land surface and atmosphere

processes and timescales
Processes and timescales
  • Diurnal
    • Radiation and water balance
  • Seasonal
    • Phenology
    • Snow
  • Centennial
    • Vegetation dynamics
    • Soil turnover
eo data
EO data
  • Assimilation of EO data (cfstate estimation) is relevant at diurnal and seasonal timescales…
  • … for forecasting, seasonal forecasting, crop monitoring, carbon cycle …
  • Models not developed with EO in mind
  • Canopy models simple
    • Limited handling of canopy structure

– Can’t simulate BRFs

international landscape
International landscape
  • UK Community model – JULES / TRIFFID
  • US Community model – CLM
  • Meteo-France– ISBA
  • EC-Earth community model also uses TESSEL scheme
  • Build parsimonious land surface scheme
    • Water, energy and carbon fluxes
  • Invest complexity in the necessary physics to represent satellite observations correctly
    • Optical, thermal and passive microwave
  • Embed in DA scheme as early as possible
    • EOLDAS/Particle Filter
model concept
Model concept

Interception& Evaporation

Photosynthesis& Allocation


Soil column

Soil “skin” layer






force restore water heat
Force restore water/heat

Soil temperature

Soil & vegetation water

e.g. Noilhan and Planton (1989)

force restore model
Force restore model
  • The force restore approach predicts surface temperature & moisture for a small, finite depth
  • Depth can be tuned to match the response of thermal and passive microwave sensors
  • In this phase of project no plans to implement surface flows and routing
heat fluxes
Heat fluxes

Niwot Ridge, 2002, day of year 151

heat fluxes1
Heat fluxes

Niwot Ridge, 2002, day of year 170

problems with the 1d operator
Problems with the 1D operator
  • For any canopy that departs from 1D:
    • Cannot correctly describe the variation of path length with viewing and illumination geometry
    • Does not predict viewed amount of bare soil or how this varies with viewing geometry
  • Both of these are critical for correct modelling of satellite signals from large parts of the Earth’s surface
observation operator gort
Observation operator - GORT

Illuminated crown

Illuminated soil

Shaded crown

Shaded soil

geometric observation operator
Geometric Observation Operator

Shaded crown

Illuminated crown

Shaded soil

Illuminated soil

metropolis hastings

α = min 1,

u*=u + random proposition



Draw z from U(0,1)

u* if z≤α

u if z> α


mcmc calibration oregon
MCMC calibration (Oregon)


Leaf area index

Soil brightness

H/B ratio


Projected Crown Cover

Crown shape

Leaf chlorophyll

pdf lai vs chlorophyll
PDF – LAI vs. chlorophyll

Leaf chlorophyll

Leaf area index

pdf crown cover vs shape
PDF – Crown cover vs. shape

Crown cover

Crown shape

next steps immediate
Next steps - immediate
  • Improve model integration
    • Currently using Euler integration…
  • Couple GORT fully
    • Sun angle effects
    • Diffuse/direct
    • Interception of precipitation
next steps short term
Next steps – short term
  • Data Assimilation framework
    • Particle Filter
  • Assimilate optical data
    • MODIS, GlobAlbedo
  • Comprehensive testing
    • Flux tower sites
    • Neon sites
next steps medium term
Next steps – medium term
  • Add photosynthesis model
    • Farquhar based
  • Add allocation model
    • DALEC type
  • More testing…
  • Implement on large scale…