Transcript Slide 1

GlobModel
The GlobModel study,
initial findings and objectives of the day
Zofia Stott
13 September 2007
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Objective of presentation/contents
Background to the GlobModel study
Preliminary conclusions of the study
Objectives of the day
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Background to the GlobModel study
EO data-model fusion is a relatively new area for ESA
DUE Glob-projects
Summer schools
Ad hoc collaborations, eg with ECMWF
Fact finding
Programmes, initiatives, organisations, people
• European focus
• Also international programmes, eg IGBP, WCRP
• Analogies with US where appropriate
Analysis
Report
Opinion seeking
What are the issues for the European community?
Strategy and implementation plan for ESA
Where should ESA be involved?
How should ESA be involved?
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Workshop
Background to the GlobModel study
Scope
Numerical Weather Prediction
Re-analysis
New (pre)-operational services, eg GMES Fast Track services
• Ocean forecasting
• Chemical weather forecasting
Global change and Earth system science
EO data-model fusion
Data assimilation
Ancillary surface data fields
Model validation
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Background to the GlobModel study
GlobModel hypothesis
Understanding, forecasting and predicting the
behaviour of the Earth system depends on
• Data and models working together
• Satellite data are key
Progress is accelerated by collaboration between
the science base and operational services
Objective is to create a “virtuous circle”
• High scientific return
• Linked to new operational services
• Leading to investment in both new research and
operational missions
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Background to the GlobModel study
Specific requirements/issues
• The role of OSSE and OSE in quantifying the impact of particular
data streams
• Concerns about data continuity over the next 10 years
• Areas where new or improved instruments are required
• Novel data products specifically tailored for model assimilation (eg
radiances V retrievals V gridded fields)
• Improved techniques for EO data-model fusion (eg development
of new data assimilation techniques, observation operators)
• Intercomparison and cross validation of different data sets
• Improved model development environments which include
consideration of EO data issues
• Standardisation and harmonisation of EO data formats, data
discovery and data access
• Improved quality control
• Software tools to support the use of EO data streams
• Real time delivery and long term curation
• Provision of high level products, eg model independent
reanalyses
• Shared high performance computing environment
• Training.
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Preliminary recommendations – OSE, OSSE
Instruments have been ranked:
Geopotential 500 hPa Southern Hemisphere
Control:
all
Baseline:
conv only
Reference:
baseline +
all AMVs
Geo AMV: reference
– Modis AMVs
Others:
reference +
instrument
SH RMS error 500 hPa geopotential
120
100
80
(m) 60
40
20
0
Day2
Day5
Day7
control
amsua
airs
hirs
scat
ssmi
amsub
csr
geo amv
reference
baseline
AIRS (1 instrument) and AMSU-A (3-4 instruments) constellation clearly
emerge from the pack
“The Global Observing System”, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007
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Preliminary recommendations – access to
operational systems
Make operational systems more readily
available for research
Mutual benefit
Scientists work on topics of interest to operational
agencies
Benefit from operational facilities (models, computer
resources, expert help)
Operational agencies benefit from latest research
results
Increases chances to technology transfer from
research base to operations
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Preliminary recommendations –
integrated data systems
Increase emphasis on integrated data systems
for new services
Optimise in situ and satellite components
• Eg What is the balance between Argo floats and
altimeters?
GODAE/GHRSST/Medspiration projects optimising
sea surface temperature retrievals could be taken
as an example of good practice
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Preliminary recommendations
Develop observation operators
Fundamental link between data and models
Essential to ensure early take up of data into
operational systems
Commit to long term continuity of re-analysis
Develop the use of EO data in the land and
cryosphere components of the Earth system
models
Develop “climate” quality data sets
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Preliminary recommendations - people
Ensure that the right mix of people/institutions
are brought together
Experts on satellite data processing, retrievals
Experts on operational data assimilation systems
Experts on Earth system modelling in the research
community
Members of satellite instrument and/or science
teams
Participants in the cal-val effort
Members of the satellite data management teams.
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Preliminary conclusions – provide a
science focus
Address the big science issues
Develop regional climate models able to identify “tipping
points” in the climate system
Understand link between physical and biological feedbacks in
carbon cycle
Understand links between climate change and atmospheric
composition
Develop coupled sea-ice and ocean circulation models
Develop improved ability to model hydrological cycle and
predict high impact weather
Develop ecosystem and biodiversity models
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Objectives of the day - Splinter sessions
Where are we today?
What are the key issues?
What is your vision for Earth system modelling in 10
years time?
What will we be able to do which we cannot do today? Eg
• Forecast on an annual/decadal and regional basis?
• Forecast high impact weather?
• Identify and monitor all climate tipping points?
What role should EO play in achieving our goals?
What programmes and projects would you recommend
to ESA to fulfil your objectives?
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Backup slides
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NWP I
Developments driven by operational requirements of
forecasting centres
New services
Seasonal and inter annual forecasts
High impact weather
New and improved services, based on
Better models
Better data
Satellite data are key
Innovation needs close links between R&D and
operations
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NWP II
Pull through of satellite data for NWP, in Europe
Strong for meteorological data sources
Eg via EUMETSAT SAFs
Weaker for non EUMETSAT data
• Ad hoc
• But good examples of transfer from research to operational status
eg scatterometer, GOME, altimetry
Key satellite requirements
•
•
•
•
•
Low level (1B/C) radiances
Some retrievals (eg Atmospheric Motion Vectors)
Surface gridded fields
Real time delivery (<1 hour)
BUFR, GRIB
High priority issues
•
•
•
•
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Improved coupled models
Use of satellite radiances over land, cloud
Hydrological cycle
Improved surface representation/assimilation
NWP III
Increasing experience of OSE, OSSE
Quantify impact of satellite data on NWP
Comparison of Europe with USA
JCSDA
• NASA/NOAA initiative
• To accelerate take up of new data sources
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NWP IV
Number of satellite sources used at ECMWF
55
50
45
AEOLUS
SMOS
TRMM
CHAMP/GRACE
COSMIC
METOP
MTSAT rad
MTSAT winds
JASON
GOES rad
METEOSAT rad
GMS winds
GOES winds
METEOSAT winds
AQUA
TERRA
QSCAT
ENVISAT
ERS
DMSP
NOAA
40
35
30
No. of sources
25
20
15
10
5
0
1996
1997
1998
1999
2000
2001
2002 2003
Year
2004
2005
2006
2007
2008
2009
“The Global Observing System”, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007
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NWP V
Control:
all
Baseline:
conv only
Reference:
baseline +
all AMVs
Geo AMV: reference
– Modis AMVs
Others:
reference +
instrument
Instruments have been ranked:
Geopotential 500 hPa Southern Hemisphere
SH RMS error 500 hPa geopotential
120
100
80
(m) 60
40
20
0
Day2
Day5
Day7
control
amsua
airs
hirs
scat
ssmi
amsub
csr
geo amv
reference
baseline
AIRS (1 instrument) and AMSU-A (3-4 instruments) constellation clearly
emerge from the pack
“The Global Observing System”, Jean-Noël Thépaut,
Data Assimilation Training Course, ECMWF Reading, 25 April- 4 May 2007
19
NWP VI
Messages from NWP
NWP key for operational data assimilation
• 40 years of infrastructure and capability
Need to work effectively with NWP centres
• EUMETSAT, ECMWF, national met offices
No equivalent of GMAO or JCSDA in Europe
• No systematic mechanisms for accelerating transfer of
research data sources to operations
• ADM, SMOS already identified by ECMWF
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Reanalysis I
Long term (eg 40 years) global data sets of past
climate using data assimilation
Reliant of latest NWP model + historical data
ECMWF leads in Europe
Key for
Understanding climate trends
Improving both models and data (biases)
Challenges
Need for improved coupled models
Inhomogeneities in data records
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Reanalysis II
Messages from reanalysis
Long term missions needed
• Repeats
• Overlaps
Long term curation of data – a major challenge
European reanalysis projects are
• “Add on” to existing activities, not core business
• Funding ad hoc
No sustained European effort in reanalysis
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“New” (pre)-operational forecasting I
Ocean forecasting
Chemical weather forecasting
Learning from current practice in NWP
Reliant on NWP either through loosely or tightly coupled
models
GMES Core Services providing a European delivery
structure
Far less technically mature than NWP
Requirements less precise
Techniques more experimental
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“New” (pre)-operational forecasting II
Data types
Ocean forecasts
• Broad correspondence between GMES Sentinel 3 and
ocean forecasts (altimetry, SST, ocean colour)
• Also ocean salinity (SMOS), sea ice thickness (Cryosat),
gravity/geoid (GRACE/GOCE), wind/waves (scatterometer)
Chemical weather forecasting
• Broad correspondence between GMES Sentinels 4/5 and
chemical weather forecasting
• Also METOP, MSG, ENVISAT, AURA instruments
PLUS NWP outputs (forcing fields)
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“New” (pre)-operational forecasting III
Messages
Continued development through close research/operational
interactions
Models immature in key areas of user interests, eg
• boundary layer chemical forecasts
• coupled physical-biogeochemical models and assimilation of
ocean colour data
Need for better comparison between data and models
Standards, data formats are still evolving etc
• GMES and INSPIRE are addressing this
Tools, training, common research hub to exchange data and
models
Important to work with emerging structures
Eg EUROGOOS for ocean forecasting
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Earth system science I
Developing GCMs
What’s new
• Shorter timescales (from centuries to decades), more local
impacts (from global to regional)
Representation of energy and hydrological cycle
Ocean variability and climate change signals
Developing land surface models in GCMs
Developing models of coupled atmosphere/
ocean/cryosphere
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Earth system science II
Global carbon cycle
Quantifying surface fluxes
Quantifying role played by fire
Identifying weights of key processes in tropics for post-Kyoto
negotiations
Atmospheric composition
Understanding interactions between climate change and atmospheric
composition
Cryosphere
Strongest signals of climate change, but key processes poorly
represented in models
Predictability of high impact weather
Monitoring, understanding, predicting behaviour of ecosystems
Impacts of natural resource depletion
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