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 – Direct measure of energy leaving the system
Rationale
• 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
• Developed to calculate the exchange of energy and water between the land surface and atmosphere
Radiation interactions withatm, veg. and soil
Groundwater andChannel flows
Precipitation &evaporation
Evapo-transpiration
• Developed for NWP – extended for other studies
• Fluxes added for carbon, nutrients, aerosols
Processes and timescales
• Diurnal– Radiation and water balance
• Seasonal– Phenology– Snow
• Centennial– Vegetation dynamics– Soil turnover
EO data
• Assimilation of EO data (cf state 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
• UK Community model – JULES / TRIFFID• US Community model – CLM • Meteo-France – ISBA • ECMWF – [C/H]-TESSEL • EC-Earth community model also uses TESSEL
scheme
Concept• 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
Soil column
Photosynthesis& Allocation
Interception& Evaporation
Vegetationprocesses
Soilprocesses
Carbon Water SW LW
Soil “skin” layer
Force restore water/heat
rr v tr r
W veg P E E Rt
2
2
1g g tr
w
w P E Et d
1 2
1
gg g g geq
w
w C CP E w wt d
22s
T sT C G T Tt
22
1s
T T Tt
Soil temperature
Soil & vegetation water
e.g. Noilhan and Planton (1989)
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
Niwot Ridge, 2002, day of year 151
Heat fluxes
Niwot Ridge, 2002, day of year 170
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
Shaded crown
Illuminated crown
Illuminated soil
Shaded soil
Shaded crownIlluminated
crown
Illuminated soil
Shaded soil
Geometric Observation Operator
Short-wave partitioning
Short-wave partitioning
Short-wave partitioning
Short-wave partitioning
Metropolis-Hastings
α = min 1,P(B|u*)P(A)
P(B|u)P(A)
Draw z from U(0,1)
u+= u* if z≤αu if z> α
u*=u + random proposition
MCMC calibration (Oregon)PD
FPD
F
Leaf area index Soil brightness H/B ratio
Projected Crown Cover Crown shape Leaf chlorophyll
PDF – LAI vs. chlorophyll
Leaf area index
Leaf
chl
orop
hyll
PDF – Crown cover vs. shape
Crown shape
Crow
n co
ver
PDF – fapar vs. albedo
Albedo
fapa
r
Forward modelled BRF
Forward modelled BRF
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
• Data Assimilation framework– Particle Filter
• Assimilate optical data– MODIS, GlobAlbedo
• Comprehensive testing– Flux tower sites – Neon sites
Next steps – medium term
• Add photosynthesis model– Farquhar based
• Add allocation model– DALEC type
• More testing…• Implement on large scale…
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