an integrated approach sst retrieval and assimilationicoads.noaa.gov/advances/harris.pdf · goes-8...
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SST Retrieval and Assimilation:An Integrated Approach
SST Retrieval and Assimilation:An Integrated Approach
•Why are we looking at this, and why now?
•What are the main issues?
•Possible solutions
•The way forward
•Why are we looking at this, and why now?
•What are the main issues?
•Possible solutions
•The way forward
Andy Harris, NOAA/NESDIS/ORA
With thanks to:Chris Merchant, U. EdinburghChelle Gentemann, Remote Sensing SystemsJean Thiébaux, NOAA/NCEPGary Wick, NOAA/ETLJo Murray, RAL
Andy Harris, NOAA/NESDIS/ORA
With thanks to:Chris Merchant, U. EdinburghChelle Gentemann, Remote Sensing SystemsJean Thiébaux, NOAA/NCEPGary Wick, NOAA/ETLJo Murray, RAL
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The technology is in place:
• SST retrievals from polar-orbiting IR,geostationary IR and now MW
• Computing power is sufficient to permitoperational application of more sophisticatedretrieval methods
• Each data source has its strengths andweaknesses
• An optimally combined product would satisfymany users
The technology is in place:
• SST retrievals from polar-orbiting IR,geostationary IR and now MW
• Computing power is sufficient to permitoperational application of more sophisticatedretrieval methods
• Each data source has its strengths andweaknesses
• An optimally combined product would satisfymany users
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“End user” communities that have requirements• Climate monitoring• Operational oceanography• Numerical weather prediction• Fisheries
“End user” communities that have requirements• Climate monitoring• Operational oceanography• Numerical weather prediction• Fisheries
The requirements are different, but are theymutually exclusive?
• Climate monitoring needs observation systemto be stable to better than 0.1 K/decade
• Timeliness for NWP, operationaloceanography. Less emphasis on absoluteaccuracy (±0.5 K)
• Fisheries. Often need fronts, and sometimesabsolute temperatures
The requirements are different, but are theymutually exclusive?
• Climate monitoring needs observation systemto be stable to better than 0.1 K/decade
• Timeliness for NWP, operationaloceanography. Less emphasis on absoluteaccuracy (±0.5 K)
• Fisheries. Often need fronts, and sometimesabsolute temperatures
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What do we mean by optimal?
Minimize the “cost” of assimilating the data…
What do we mean by optimal?
Minimize the “cost” of assimilating the data…
( ) ( ) ( )( ) ( ) ( )( )J x x x x y x y xb b o o= − − + − + −− −1
2
1
2T T
B H E F H1 1
An equation of 2 halves: background (or 1st guess)error; observation and forward model error
So, we need to know how good our retrievals areand how good our background value is
SST retrieval has traditionally been performedby direct regression against in situ. Is this thebest way?
An equation of 2 halves: background (or 1st guess)error; observation and forward model error
So, we need to know how good our retrievals areand how good our background value is
SST retrieval has traditionally been performedby direct regression against in situ. Is this thebest way?
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What is the main advantage of remote sensing?
• Provides data in remote regions where in situobservation are sparse or non-existent
To utilize remotely-sensed data to an optimumlevel, we need to be able to specify accuracy inthese remote regions
• This requires independent data in order togain the necessary confidence
Can retrieval accuracy be improved by the additionof other data sources?
• Inclusion of water vapor can probably only bedone at a rudimentary level using directregression Studies have demonstrated little
What is the main advantage of remote sensing?
• Provides data in remote regions where in situobservation are sparse or non-existent
To utilize remotely-sensed data to an optimumlevel, we need to be able to specify accuracy inthese remote regions
• This requires independent data in order togain the necessary confidence
Can retrieval accuracy be improved by the additionof other data sources?
• Inclusion of water vapor can probably only bedone at a rudimentary level using directregression Studies have demonstrated little
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• The chief advantage of radiative transfer is thatit allows specification of the retrieval algorithmwithout bias towards the data-rich regions
• The in situ data can then act as a randomindependent sampling of the retrieval conditions.
• If the observed errors agree with the modeledones, then high confidence can be placed on themodeled errors in data-sparse regions
• Additional advantage is that other sources oferror can be accounted for explicitly, andexternal data (e.g. atmospheric profiles) can beincorporated
•This doesn’t mean it’s easy to do…
• The chief advantage of radiative transfer is thatit allows specification of the retrieval algorithmwithout bias towards the data-rich regions
• The in situ data can then act as a randomindependent sampling of the retrieval conditions.
• If the observed errors agree with the modeledones, then high confidence can be placed on themodeled errors in data-sparse regions
• Additional advantage is that other sources oferror can be accounted for explicitly, andexternal data (e.g. atmospheric profiles) can beincorporated
•This doesn’t mean it’s easy to do…
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Modeling must be accurate:• Spectroscopy (mainly continuum)• Representative input data (atmospheric
profiles)• Noise characteristics of real data• Filter functions
Sensor calibration must be accurate
• However, RT does give rise to possibility ofcorrecting calibration in a more predictablefashion
Cloud masking, aerosols
Surface effects (skin vs. bulk)
Modeling must be accurate:• Spectroscopy (mainly continuum)• Representative input data (atmospheric
profiles)• Noise characteristics of real data• Filter functions
Sensor calibration must be accurate
• However, RT does give rise to possibility ofcorrecting calibration in a more predictablefashion
Cloud masking, aerosols
Surface effects (skin vs. bulk)
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GOES-8 SST retrievals using radiative transferGOES-8 SST retrievals using radiative transfer
Note that operational retrievals are 0.73 K r.m.s. on same data with +ve bias
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Diurnal thermocline is avery non-linear effect:
• Wind mixing energy isproportional to U*3
• The heat contained inthe upper layer is thesame, but the heatcapacity is different
• Skin effect isgenerally a moretractable problem
Diurnal thermocline is avery non-linear effect:
• Wind mixing energy isproportional to U*3
• The heat contained inthe upper layer is thesame, but the heatcapacity is different
• Skin effect isgenerally a moretractable problem
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Diurnal cycle modelingcan now be done withreasonable confidence,but solar flux must beaccurate and have goodtime resolution.
Diurnal cycle modelingcan now be done withreasonable confidence,but solar flux must beaccurate and have goodtime resolution.
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Other considerations
• Cloud masking – is it time to dispense withthresholds and serial application of tests?
• Should fast-forward RT models be used?
• What about archive data?
• Aerosols (stratospheric & tropospheric)
• ‘Blending’ of POES & GOES
Test the schemes – facilitate the feedback loopbetween validation and algorithm development
Other considerations
• Cloud masking – is it time to dispense withthresholds and serial application of tests?
• Should fast-forward RT models be used?
• What about archive data?
• Aerosols (stratospheric & tropospheric)
• ‘Blending’ of POES & GOES
Test the schemes – facilitate the feedback loopbetween validation and algorithm development
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Microwave dataMicrowave dataNow have the capability to retrieve SSTs from TMI togood accuracy, except in precipitation and “near” land.AMSR will extend coverage
• Spatial resolution is of order 50 km, but oversampled
• Larger errors where emissivity modeling is moredifficult (high windspeed)
• Some calibration challenges
• Physically-based retrieval of SST and otherparameters (cloud liquid water, TCWV, windspeed)
• Immune to aerosol problems
Errors in IR and MW SST retrievals are essentially
Now have the capability to retrieve SSTs from TMI togood accuracy, except in precipitation and “near” land.AMSR will extend coverage
• Spatial resolution is of order 50 km, but oversampled
• Larger errors where emissivity modeling is moredifficult (high windspeed)
• Some calibration challenges
• Physically-based retrieval of SST and otherparameters (cloud liquid water, TCWV, windspeed)
• Immune to aerosol problems
Errors in IR and MW SST retrievals are essentially
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Case study using GOESCase study using GOES
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Relaxation of cloud masking thresholds has resulted inlower detection rateRelaxation of cloud masking thresholds has resulted inlower detection rate
Also some correlation with water vaporAlso some correlation with water vapor
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Assimilation techniquesAssimilation techniques
Adaptive, anisotropic analysisstructure functions can bedeveloped from study of theobservation – backgroundincrement field
Where does the 1st guess(background) field come from?
• Previous analysis orpredictive model. Someusers will want to use theirown model
A predictive model can be usedt i il t ti
Adaptive, anisotropic analysisstructure functions can bedeveloped from study of theobservation – backgroundincrement field
Where does the 1st guess(background) field come from?
• Previous analysis orpredictive model. Someusers will want to use theirown model
A predictive model can be usedt i il t ti
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Example of high resolution (9-km) multi-scale OIusing night-time AVHRR PathfinderExample of high resolution (9-km) multi-scale OIusing night-time AVHRR Pathfinder
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SummarySummaryA single, optimal SST product (or methodology) thatmaximizes the strengths of each input dataset whilstminimizing the impact of the deficiencies requires:
• A common retrieval framework, with known errorcharacteristics (seasonally and geographicallyvarying)
• Modeling of surface effects (accurate fluxes)
• Assimilation methods to take account ofcharacteristics of input data (e.g. non-gaussian)
• This may require a predictive model withappropriate geophysical constraints & forcing
A l b t d bl t k!
A single, optimal SST product (or methodology) thatmaximizes the strengths of each input dataset whilstminimizing the impact of the deficiencies requires:
• A common retrieval framework, with known errorcharacteristics (seasonally and geographicallyvarying)
• Modeling of surface effects (accurate fluxes)
• Assimilation methods to take account ofcharacteristics of input data (e.g. non-gaussian)
• This may require a predictive model withappropriate geophysical constraints & forcing
A l b t d bl t k!