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Global Environmental Modelling and Prediction

Using Earth Observations from Space

Alan O’ Neill

Data Assimilation Research Centre

University of Reading

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Current & future satellite coverage

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2020 Vision

• By 2020 the Earth will be viewed from space with better than 1km/1min resolution

• Computer power will be over 1000 times greater than it is today

• To exploit this technological revolution, the world must be digitised

• Data assimilation will create “ Digiworld”

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An analogy:recording music

• Goal: produce high-quality, well balanced CD of Berlin Philharmonic to play on standard home equipment

• Method: Distribute microphones around the Royal Albert Hall & record output from each

• Problems– Each mike picks up only part of the sound

– Some mikes are biased

– Some are noisy

– Some record only intermittently

– Customers don’ t want one CD for each mike

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What is data assimilation?

Data assimilation is the technique whereby observational data are combined with output from a numerical model to produce an optimal estimate of the evolving state of the system.

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Why We Need Data Assimilation

• range of observations• range of techniques• different errors• data gaps• quantities not measured• quantities linked

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Numerical ModelDAS

DATA ASSIMILATION SYSTEM

O

Data Cache

A

A

B

F

model

observations

Error Statistics

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Some Uses of Data Assimilation

• Operational weather and ocean forecasting

• Seasonal weather forecasting

• Land-surface process

• Global climate datasets

• Planning satellite measurements

• Evaluation of models and observations DARC

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Operational geostationary satellites

GOES water vapour imagery

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Impact on NWP at the Met Office

Mar 99. 3D-Var

and ATOVS

Jul 99. ATOVS over Siberia,

sea-ice from SSM/I

Oct 99. ATOVS as radiances,

SSM/I winds

May 00. Retune

3D-Var

Feb/Apr 01. 2nd satellites,

ATOVS + SSM/I

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Ozone from Mipas

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Ozone from MIPAS Sep 2003

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Ocean temp at equator: Oct 2002

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ECMWF Seasonal Forecasts

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Seasonal Forecasts for Europe (DJF 1997/98)

Forecast probability of above average temperatures

Measured temperature anomaly

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CO “ colors” , day 1

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CO “ colors” , day 65

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CO “ colors” , day 85

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Regional Scale: Regional Scale: WWalnut alnut GGulch (Monsoon 90)ulch (Monsoon 90)

Model

Model with 4DDA

Observat ion

Tombstone, AZ

0% 20%

Houser et al., 1998

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MERIS ocean colour

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Conclusions

• Earth observations from space are allowing us to build highly sophisticated global environmental monitoring and prediction systems

• These systems will form the basis for many policy and commercial decisions

• But the scientific, computing and organisational challenges are enormous

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