Using Flux Observations to Improve Land-Atmosphere Modelling: A One-Dimensional Field Study

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Using Flux Observations to Improve
Land-Atmosphere Modelling A One-Dimensional
Field Study
Robert Pipunic, Jeffrey Walker Andrew Western
The University of Melbourne Cathy Trudinger
Ying Ping Wang CSIRO Marine and Atmospheric
Research Supported by an Australian
Postgraduate Award Scholarship and University of
Melbourne CSIRO Collaborative Research Support
Scheme
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Synthetic Twin Experiments
Pipunic et al., 2007. Remote Sensing of
Environment, In Press.
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Kyeamba Creek Experimental Site
4-way radiometer, incoming outgoing shortwave
longwave radiation 30min averages recorded
3D sonic anemometer open path gas analyser for
LE H, 3m above ground 10Hz measurements, 30min
averages recorded
Barometric pressure sensor 1 reading per hour
Air temperature relative humidity probe, 2m
above ground 30min averages recorded
Wind direction and speed 30min averages recorded
Tipping rain gauge bucket 30min totals recorded
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Below the Ground
CS615 Soil Moisture Probes Measuring every 30
mins
Soil temperature probes Measuring every 30 mins
2cm
8cm
5cm
10cm
20cm
30cm
Soil heat flux plates 30min averages recorded
50cm
60cm
90cm
(Not to scale)
100cm
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CSIRO Biosphere Model (CBM) / CABLE
Short Wave Radiation
Precipitation

• Canopy model (Wang Leuning, 1998)
• LE, H and CO2 for a sunlit and a shaded leaf
  canopy
• LE and H calculated from both vegetation and bare
  soil based on fraction of transmitted radiation
  through canopy.

Long Wave Radiation
CO2
Wind
LE
H
G
Snow
L1
L2
L3

• Six computational soil layers using the soil and
  snow scheme by Kowalczyk et al. (1994)
• Uniform properties for all layers
• Individual volumetric moisture and temperature -
  moisture governed by Richards equation.

L4
L5
L6
(Not to scale)
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Ensemble Kalman Filter
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Ensemble Member Generation
Perturbing meteorological variables
Random number generated at each time step in
series, zero mean
Random number generated once for each ensemble
and applied to whole series, zero mean
Turner et al., 2007. Remote Sensing of
Environment, In Press.
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Assimilation Over 1 Year Period (2005)

• LEH assimilated on MODIS timescale twice a day
  where SW radiation is gt500Wm-2 (representing no
  cloud cover).
• Surface soil moisture on SMOS timescale every
  3 days.

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Initial Conditions
Using spin-up with best available parameters (1
January 2005)
? Observed Spin-up
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LE and H Diurnal Results
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LE and H Daily Average Results
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Root Zone Soil Moisture and Temperature
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Summary of Results
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Conclusions

• LE and H assimilation results are better than SM
  results for estimating LE and H, but slightly
  worse for soil moisture
• The land surface model used exhibits soil
  moisture and temperature biases when using
  standard parameters and forcing this is likely
  to be typical of most NWP land models
• Temperature and moisture biases need to be
  accounted for using a bias-aware assimilation
  approach

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www.cahmda3.info Abstracts due 1 July 2007