Remote Sensing, Remote Sensing, Land Surface Modelling Land Surface Modelling and Data Assimilationand Data Assimilation
Christoph Rüdiger, Jeffrey WalkerChristoph Rüdiger, Jeffrey WalkerThe University of MelbourneThe University of Melbourne
Jetse KalmaJetse KalmaThe University of NewcastleThe University of Newcastle
Garry WillgooseGarry WillgooseThe university of LeedsThe university of Leeds
Christoph Rüdiger & Jeffrey Walker
Overview
• Remote Sensing• Data Assimilation• Land Surface Modelling• Combining the Options
Christoph Rüdiger & Jeffrey Walker
Remote Sensing
Christoph Rüdiger & Jeffrey Walker
Remote Sensing
• Remote Sensing defined:• Measurement of energy reflections or
emissions of different spectra from a distance
• Modes of Remote Sensing in Hydrology:• Ground-based• air-borne • space-borne platforms
Christoph Rüdiger & Jeffrey Walker
• Visual Band (~400nm – 700nm)• Infrared Band (~0.7μm – 1000μm)• Microwave Band (~1cm – 30cm)• Radio Band (>30cm)
• Gravitational Measurements
Observed Wavelengths- Spectral Resolution -
Christoph Rüdiger & Jeffrey Walker
What Can Be Measured(some examples)
• Subsurface• Surface soil moisture, soil
temperature, gravitational effects
• Surface• Vegetation cover, vegetation density,
evapotranspiration, temperature, sea level, elevation, fires
• Atmosphere• Cloud cover, aerosols, wind,
temperature
Christoph Rüdiger & Jeffrey Walker
Remote Sensing- Spatial Resolution -
Study Catchment
Christoph Rüdiger & Jeffrey Walker
Current Missions
• Visual Band (~400nm – 700nm) • Modis, Landsat …
• Infrared Band (~0.7μm – 1000μm)• Landsat, GOES
• Microwave Band (~1cm – 30cm)• TRMM, AMSR-E
• Radio Band (>30cm)• TRMM
• Gravitational Measurements• Grace
Christoph Rüdiger & Jeffrey Walker
Limitations of Individual Bands
• Atmospheric interference (infrared).
• Radio interference (microwave).• Surface conditions, vegetation,
cloud and aerosol effects (all).• Penetration depth (all).• Other effects?
Rüdiger et al., in review
Christoph Rüdiger & Jeffrey Walker
Summary of Remote Sensing
• Advantages:• Observation of large areas• Observations of remote areas• Large quantity of environmental states can
be observed
• Limitations:• Either low resolution or low rate of repeat
overpasses• Influence of surface and atmospheric
conditions have to be filtered• Average values of observed states, need for
downscaling
Christoph Rüdiger & Jeffrey Walker
Data Assimilation
Christoph Rüdiger & Jeffrey Walker
Data Assimilation Defined
Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSMAnalogy: passenger giving instructions to a blindfolded driver on the autostrada at peak hour
Christoph Rüdiger & Jeffrey Walker
Analogy 1
Initial state
Up
date
Up
date
Up
date
Up
date
Up
date
Up
date
Christoph Rüdiger & Jeffrey Walker
Data Assimilation Defined
Definition 1: using data to force a model ie. precipitation and evapotranspiration to force a LSM
Definition 2: using state observations to make a correction to the forecast model state ie. surface soil moisture obs. to correct forecastsAnalogy: driver can see through his blindfold for 1/10th second every 30 seconds
Analogy: passenger giving instructions to a blindfolded driver on the autostrada at peak hour
Christoph Rüdiger & Jeffrey Walker
Analogy 2In
itia
l st
ate
Avail. Info ForecastAvail. Info
Forecast
Fore
cast
Avail.
Info
Christoph Rüdiger & Jeffrey Walker
What is the Usefulness of Data Assimilation
• Organises data (model acts as interpolator)
• Complements data (fills in unobserved regions)
• Supplments data (provides unobserved quantities)
• Quality controls data• Calibrates data
Christoph Rüdiger & Jeffrey Walker
Some Methods of Data Assimilation
1. Direct Insertion2. Statistical Correction3. Optimal Interpolation (OI)4. Variational over Space and Time
(4DVAR)5. Sequential Data Assimilation (eg.
Kalman Filter)
Christoph Rüdiger & Jeffrey Walker
Continuous or Sequential DA?
• Continuous (ie. variational)• Regression schemes• Adjoint derivation
• In general:• Minimisation of objective function
Time
Sta
te V
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Christoph Rüdiger & Jeffrey Walker
Continuous or Sequential DA?
• Sequential (ie. Kalman filter)
Predict:
Observe:Correct:
Time
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Christoph Rüdiger & Jeffrey Walker
Extended or Ensemble KF?
Time
Sta
te V
alu
e
EKF
Co
vari
anc
e
Time
Sta
te V
alu
e
EnKF
Co
vari
anc
e
Christoph Rüdiger & Jeffrey Walker
DA as a Spatial Interpolator
Matric Head
De
pth
TrueProfile
ModelPrediction
ModelUpdate
DirectReplacement
WithObservations
Matric Head
De
pth
TrueProfile
ModelPrediction
StatisticallyOptimal
Model Update
ObservationDepth
ModelModel with 4DDA
Observation
Soil Moisture Soil Moisture
Houser et al., WRR 1998
Christoph Rüdiger & Jeffrey Walker
Summary of Data Assimilation
• Advantages• Variational:
• Computationally inexpensive• Does not need prior knowledge of system states
or errors• No linearisation of model needed• Can obtain model sensitivity values
• Sequential:• Update of states at every observation point• Model size depends on computer not
mathematics• Advantage over variational schemes for
distributed models
Christoph Rüdiger & Jeffrey Walker
Summary of Data Assimilation
• Limitations• Variational:
• Regression scheme can become unstable• Adjoint derivation can be a complex
problem• Long-term forecasts become inaccurate
• Sequential:• Models have to be linearised to certain
extent• Can be computationally infeasible• Error estimates can cause problems
Christoph Rüdiger & Jeffrey Walker
Hydrological Modelling
Christoph Rüdiger & Jeffrey Walker
Hydrological Modelling
• Different models available• Soil moisture models• Land surface models• Atmospheric models• Land surface – atmosphere models • General Circulation models
• Different approaches for modelling:• Lumped• Distributed• Semi-distributed
Christoph Rüdiger & Jeffrey Walker
Difference between model approaches
distributed
semi-distributed or lumped
Christoph Rüdiger & Jeffrey Walker
Semi-distributed model
Kalma et al., 1995
Christoph Rüdiger & Jeffrey Walker
Two Models
Liang et al., 1998
Koster et al., 2000
Christoph Rüdiger & Jeffrey Walker
Drought monitoringDrought monitoring
Flood predictionFlood prediction
Irrigation policiesIrrigation policies
Weather forecastingWeather forecasting
Importance of Land Surface States
(soil moisture, soil temperature, snow)
Christoph Rüdiger & Jeffrey Walker
Importance of Land Surface States
(soil moisture, soil temperature, snow)• Early warning systems
• Flood prediction – infiltration, snow melt
• Socio-economic activities• Agriculture – yield forecasting, management
(pesticides etc), sediment transport• Water management – irrigation
• Policy planning• Drought relief• Global change
• Weather and climate• Evapotranspiration – latent and sensible heat• Albedo
Christoph Rüdiger & Jeffrey Walker
Soil Moisture vs Sea Surface Temp
• Knowledge of soil moisture has a greater impact on the predictability of summertime precipitation over land at mid-latitudes than Sea Surface Temperature (SST).
Koster et al., JHM 2000
Christoph Rüdiger & Jeffrey Walker
Importance of Soil Moisture
Koster et al., JHM 2000
(JJA)
Christoph Rüdiger & Jeffrey Walker
Combining the Efforts
Christoph Rüdiger & Jeffrey Walker
The Situation
Remote SensingSatellite
Surface SoilMoisture
Soil MoistureSensors
Logger Soil Moisture Model[q , D ( ), ( )] f s(z)
Christoph Rüdiger & Jeffrey Walker
The Problem With LSMs
• Same forcing and initial conditions but different predictions of soil moisture!
Houser et al., GEWEX NEWS 2001
Christoph Rüdiger & Jeffrey Walker
Why do we need improvement?
Koster et al., JHM, 2000
Christoph Rüdiger & Jeffrey Walker
How Do We Measure Soil Moisture
Christoph Rüdiger & Jeffrey Walker
Case Study – Variational DA
Assimilation of Streamflow and Surface Soil Moisture Observations
Christoph Rüdiger & Jeffrey Walker
Bayesian Regression
Kuczera, 1982
Christoph Rüdiger & Jeffrey Walker
Results from assimilation with "true" forcing (profile mc)
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Results “Experiment 1”Results from assimilation with "true" forcing (runoff)
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Results from assimilation with "true" forcing (runoff)
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Discharge Soil Moisture
Results from assimilation with "true" forcing (profile mc)
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Christoph Rüdiger & Jeffrey Walker
Results “Experiment 1”Results from assimilation with "true" forcing (runoff)
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01/08/03 02/08/03 03/08/03
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Christoph Rüdiger & Jeffrey Walker
Assimilation with "wrong" forcing data (profile mc)
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Results “Experiment 2”
Assimilation with "wrong" forcing data (runoff)
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Assimilation with "wrong" forcing data (runoff)
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Discharge Soil Moisture
Assimilation with "wrong" forcing data (profile mc)
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Assimilation with "wrong" forcing data (profile mc)
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Christoph Rüdiger & Jeffrey Walker
Results Experiment 2 cont’d
Root Zone Soil Moisture Surface Soil Moisture
Christoph Rüdiger & Jeffrey Walker
Summary of Variational Approach
• Retrieval of initial states possible to high accuracy.
• Only few iterations necessary.• Limitations when additional errors
are involved.• Long forecasting window will lead
to less accurate results.• First estimate of initial states can
be important
Christoph Rüdiger & Jeffrey Walker
Case Study – Sequential DA
Assimilation of Surface Soil Moisture
Christoph Rüdiger & Jeffrey Walker
Direct Insertion Every Hour
Day 1
No Update0 cm
1
4
10 cm
True
Day 3
-600 0-100
0
Matric Head (cm)
De
pth
(cm
)
Day 5 Day 7
Christoph Rüdiger & Jeffrey Walker
Kalman Filter Update Every Hour
Hour 1
No Update
0 cm1 cm
4 cm
10 cm
True
Hour 4
-600 0-100
0
Matric Head (cm)
De
pth
(cm
)
Hour 8 Hour 12
Christoph Rüdiger & Jeffrey Walker
Effects of Extreme Events
0
60
250 500
So
il M
ois
ture
(%
v/v)
Day of Simulation
Depth 30 - 60 cm
Depth 10 - 30 cm
TrueOpen LoopOriginal KFModified KF
Depth 1 - 10 cm
Christoph Rüdiger & Jeffrey Walker
Number of Observations• All observations
0
60
Aug/27 Sep/7 Sep/19
Connector TDROpen LoopKalman-Filter
Soi
l Moi
stur
e (%
v/v)
1997
Depth 0-520 mm
0
60
Aug/27 Sep/7 Sep/19
Connector TDROpen LoopKalman-Filter
Soi
l Moi
stur
e (%
v/v)
1997
Depth 0-520 mm
• Single Observation
Christoph Rüdiger & Jeffrey Walker
Summary of Sequential DA
• Require a statistical assimilation scheme (ie. a scheme which can potentially alter the entire profile).
• Simulation results may be degraded slightly if simulation and observation values are already close.
• The updating interval is relatively unimportant when using a calibrated model with accurate forcing.
Christoph Rüdiger & Jeffrey Walker
Final Words
• Other assimilation work• Complete the global SMMR assimilation – Ni et al.• SMMR/AMSR assimilation Australia – Walker et al.• Continental snow assimilation – Sun et al.• TRMM assimilation – Entin et al.• G-LDAS – Rodell et al.
• Runoff assimilation – Rüdiger et al.
• Evapotranspiration assimilation – Pipunic et al.
Christoph Rüdiger & Jeffrey Walker
Ciao di Rocco
Thankyou!Thankyou!
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