remote sensing, land surface modelling and data assimilation

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


Remote Sensing


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


• Visual Band (~400nm – 700nm)• Infrared Band (~0.7μm – 1000μm)• Microwave Band (~1cm – 30cm)• Radio Band (>30cm)

• Gravitational Measurements

Observed Wavelengths- Spectral Resolution -


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


Remote Sensing- Spatial Resolution -

Study Catchment


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


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


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


Data Assimilation


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


Analogy 1

Initial state

Up

date

Up

date

Up

date

Up

date

Up

date

Up

date


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


Analogy 2In

itia

l st

ate

Avail. Info ForecastAvail. Info

Forecast

Fore

cast

Avail.

Info


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


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)


Continuous or Sequential DA?

• Continuous (ie. variational)• Regression schemes• Adjoint derivation

• In general:• Minimisation of objective function

Time

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Continuous or Sequential DA?

• Sequential (ie. Kalman filter)

Predict:

Observe:Correct:

Time

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Extended or Ensemble KF?

Time

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EKF

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anc

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Time

Sta

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alu

e

EnKF

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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


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


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


Hydrological Modelling


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


Difference between model approaches

distributed

semi-distributed or lumped


Semi-distributed model

Kalma et al., 1995


Two Models

Liang et al., 1998

Koster et al., 2000


Drought monitoringDrought monitoring

Flood predictionFlood prediction

Irrigation policiesIrrigation policies

Weather forecastingWeather forecasting

Importance of Land Surface States

(soil moisture, soil temperature, snow)


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


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


Importance of Soil Moisture

Koster et al., JHM 2000

(JJA)


Combining the Efforts


The Situation

Remote SensingSatellite

Surface SoilMoisture

Soil MoistureSensors

Logger Soil Moisture Model[q , D ( ), ( )] f s(z)


The Problem With LSMs

• Same forcing and initial conditions but different predictions of soil moisture!

Houser et al., GEWEX NEWS 2001


Why do we need improvement?

Koster et al., JHM, 2000


How Do We Measure Soil Moisture


Case Study – Variational DA

Assimilation of Streamflow and Surface Soil Moisture Observations


Bayesian Regression

Kuczera, 1982


Results from assimilation with "true" forcing (profile mc)

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DateV

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Results “Experiment 1”Results from assimilation with "true" forcing (runoff)

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Date

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Results from assimilation with "true" forcing (runoff)

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01/08/03 08/08/03 15/08/03 22/08/03 29/08/03

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truedeg

Discharge Soil Moisture


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degassim

Results from assimilation with "true" forcing (runoff)

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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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assim


Assimilation with "wrong" forcing data (profile mc)

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Results “Experiment 2”

Assimilation with "wrong" forcing data (runoff)

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Discharge Soil Moisture


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degr.


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Results Experiment 2 cont’d

Root Zone Soil Moisture Surface Soil Moisture


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


Case Study – Sequential DA

Assimilation of Surface Soil Moisture


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


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


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


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


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.


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.


Ciao di Rocco

Thankyou!Thankyou!

remote sensing, land surface modelling and data assimilation

Documents

interpolatorcomplements

cmradio band

nminfrared band

mmicrowave band

goesmicrowave band

landsat infrared band

surface conditions

amsreradio band