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

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100% Multi-year Ice

100% First-

year Ice

Open Water

Rain and wind roughening misleads PR19…

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…but not GR3719…

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100% Multi-year Ice

100% First-

year Ice

Open Water

“Weather filter”: Set IC = 0

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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

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100% Multi-year Ice

100% First-

year Ice

Open Water

During October:

return to normal

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100% Multi-year Ice

100% First-

year Ice

Open Water

During October:

return to normal

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)

Picking points with 100% FYI, MYI and OW

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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

Some Results (CVs) CVs do not “walk away” even with only SSM/I data

IMS Verification: Defaults vs w/ AutoCVs

Context: NT vs NT2

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

Questions? Comments? Suggestions?

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