Automatic Calculation of Characteristic Values for Sea … · Automatic Calculation of...
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Automatic Calculation of Characteristic
Values for Sea Ice Data Assimilation
Michael M. D. Ross RER Energy Inc.
Mark Buehner Meteorological Research Division, Environment Canada
Tom Carrieres Canadian Ice Service, Environment Canada
303A-4067 boul. Saint-Laurent • Montréal • Québec • H2W 1Y7 • www.RERinfo.ca
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International Workshop on Sea Ice Modelling and Data Assimilation
Ottawa, Ontario December 12, 2011
Acknowledgements
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• Worked performed under contract to Environment Canada
– Funding support from PERD and CSA-GRIP
• Assistance & collaboration from:
– Alain Caya
– Yi Luo
– Lynn Pogson
Presentation Outline
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• Define “Characteristic Values” (CVs)
• Example: CVs for NASA Team retrieval
• Motivation for automatic calculation of CVs
• Overview CV calculation code
• Some results for NASA Team CVs
• Automatic CV calculation beyond NASA Team retrieval
Definition of “Characteristic Values” (CVs)
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• Reference values relating observed quantities to physical
conditions
– In the world of retrievals, often called “tie-points”
– CV a more general term, can be applied to forward models for DA
• E.g., 100% first year ice is characterized by a specific set of
values for brightness temperature
Example: SSM/I Passive μ-wave Observations
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TB19 GHz Vertical TB19H
TB37V 2007-01-02 ~00Z
Example: NASA Team Retrieval*
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PR = 𝑇𝐵19𝑉−𝑇𝐵19𝐻
𝑇𝐵19𝑉+𝑇𝐵19𝐻
GR3719 = 𝑇𝐵37𝑉−𝑇𝐵19𝑉
𝑇𝐵37𝑉+𝑇𝐵19𝑉
*Cavalieri, Gloersen, & Campbell, 1984
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100% Multi-year Ice
100% First-
year Ice
Open Water
Spring, early summer:
emissivity of MYI
changes
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100% Multi-year Ice
100% First-
year Ice
Open Water
Spring, early summer:
emissivity of MYI
changes
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100% Multi-year Ice
100% First-
year Ice
Open Water
Late summer, autumn:
FYI changes to MYI
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100% Multi-year Ice
100% First-
year Ice
Open Water
Late summer, autumn:
FYI changes to MYI
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100% Multi-year Ice
100% First-
year Ice
Open Water
Late summer, autumn:
FYI changes to MYI
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100% Multi-year Ice
100% First-
year Ice
Open Water
Late summer, autumn:
FYI changes to MYI
NT is just test for a generalized code
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• NT is an outdated algorithm, so why bother?
– Simple test of a code intended for other observations/applications
• EC uses/plans to use a large number of different types of
observations, and for forward model or retrieval, CVs needed:
– Passive microwave: SSM/I, SSM/I/S, AMSR2
– Visible and infra-red radiometers: AVHRR
– Radar scatterometers: ASCAT
– Synthetic aperture radar: Radarsat-2, ASAR
Motivation for Automatic CV Calculation
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• Onerous to determine a set of CVs manually
– No guarantee of a standard approach being used
– New instruments, new versions of instruments
• The appropriate set of characteristic values may vary in time
and space – Region
– Season
– Instrument drift
– Solar position
– Atmospheric conditions
– Direction instrument is looking
GenerateCV: CV Calculation at Each Analysis
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Observations
“Average”
Classify
Background
Filter: Land, QC,
IC Gradient ,etc.
Pick 100% FYI, MYI,
and OW Points
Table
of CVs
Filtering Example
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Exclude points close to land and
where background IC is
changing (i.e., near ice edge)
Classification
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• Classification in up to 7 categories:
– Region
– Physical class (e.g., 100% Ice, 100% FYI, 100% MYI)
– Signal Type (e.g., TB37, GR3719)
– Atmospheric Influence
– Solar Zenith Angle
– Incidence Angle
– Relative Azimuth Angle
• CV tables can be very large – With just 3 classes in each of 7 categories, 37= 2187 lines
“Averaging”
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• We need to pick a representative value from the n
observations satisfying a given classification
– Average (e.g., average TB19H)
– Top or bottom ith percentile (e.g., nearly the highest TB19H)
– Observation value corresponding to ith percentile of some derived
index (e.g., the TB19H associated with nearly the highest PR ratio)
BlendCV: Combine CV Tables over a Window
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Blend
CVs @
Time 1 CVs @
Time 2
CVs @
Time m
Default
CVs
…
Blended
CVs
RetrieveCV: Supply appropriate CV to retrieval
or forward model
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RetrieveCV Module Blended
CVs
Retrieval Code
or
Forward Model Code
Appropriate
CV
Lat, long,
solar zenith,
etc.
Results from a DA experiment
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• 3Dvar system run for 2007 with analyses every 6 hours
• SSM/I data only observation
• CVs generated at every analysis and blended over 30 days
• CVs calculated for 9 geographical regions in North American
domain
Some Results (CVs) SSM/I data only, using auto-generated CVs in NT
FYI ice disappears and
CVs revert to defaults
What auto CV calculation can and can’t do
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• Can:
– Generate CVs specific to region, season, configuration, etc.
– Provide statistics (e.g., variance) useful in understanding obs
– Permit a retrieval or forward model to function as well as it can
• Cannot: – Miraculously eliminate misleading signals in the observations
• E.g., open water pooling on sea ice in summer is still a problem for PM
Moving beyond NT…
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• Passive microwave:
– Use of NT2 not obvious: • RTM means mapping observed TBs to weather-corrected TBs not obvious
– A forward model using RTM could be tailored to use of automatically
generated CVs
– Other retrievals?
• AVHRR
– Same code now works for AVHRR or NT
– Starting to generate results
– Yi Luo can tailor his approach for AVHRR forward model or retrieval to
use of automatically generated CVs
Upcoming work
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• Continue to work with Yi Luo on automatic CVs for AVHRR
• Work with Lynn Pogson on automatic CVs for Radarsat
• Look at blending window and choice of defaults
• Further tests with NT (can performance be improved?)
• Investigate possible ways of making it work with NT2 or other
PM retrievals
Conclusions
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• In a DA environment, automatic CV calculation is:
– Advantageous (more different data sets to work with)
– Easier (have background information)
• Advantages:
– CVs vary according to region, season, instrument configuration, etc.
– Accommodates introduction of new instruments & instrument drift
• Disadvantages:
– Making existing retrievals work with automatic CVs not always easy
– Requires monitoring that CVs don’t drift into unrealistic territory