arctic sst retrieval in the cci project owen embury chris merchant university of reading
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Arctic SST retrieval in the CCI project
Owen EmburyChris Merchant
University of Reading
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SST CCI Phase 1
• Combine ATSR accuracy with AVHRR coverage• Optimal Estimation (OE) retrieval– Cross referenced to ARC SST
• Diurnal variability adjustment– Report SSTs at standard depth and time of day
• Uncertainty estimation• Product specification– NetCDF 4 with classic data model– GDS2.0 compliant
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SST CCI Phase 1• Long-term (Aug 1991 – Dec 2010)– ATSR
• OE retrieval• Bayesian cloud screening• L3U (0.05°)
– AVHRR GAC• OE retrieval• CLAVR-X cloud screening• Ice detection• L2P
– SSTskin at time of observation– SST0.2m at 10:30 local time
• Adjusted to nearest am/pm
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Bayesian Cloud Detection
• Use RTTOV to simulate expected observations from ECMWF NWP
• Calculate P(obs | clear) from obs-sim differences• Get P(obs | cloud) from empirical lookup table• Use Bayes to get P(clear | obs, nwp)• P(obs | clear) is dominant factor– ECMWF NWP– RTTOV forward model– Prior error assumptions
• Prior SST error is location dependent (0.5 to 1.8 K)
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Bayesian Cloud Detection• Can consider the current system as Bayesian “clear-sky”
detection• Problem detecting conditions which look like clear-sky in
infrared– Seaice– Fog
• Potential improvements– Add fog / seaice as extra classes for detection
• Needs software refactoring– Use visible channels
• Not done yet due to ARC software heritage (ARC needed method applicable to ATSR1)
• Daytime only– Review prior SST error assumption in Arctic areas
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OE SST retrieval
• MAP formulation with prior SST error of 5 K– Reduces influence of prior on retrieval
• QC check on χ2 to remove bad retrievals– Calculation of χ2 similar to P(obs | clear) in
Bayesian cloud detection
• QC check to remove SSTs < 271.35 K– Not applied in pre-release data
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Comparison with other datasets
• Compare 5 day composite images– Pathfinder v5.2– ARC v1.1.1– AMSR-E v7– OSTIA– CCI
• AVHRR L2P• ATSR L3U
• Show with OSISAF sea ice concentration– 15% and 85% contour lines
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2008Norwegian and Greenland Seas
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2010Norwegian and Greenland Seas
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2008Ice melt in Beaufort Sea and outflow from Mackenzie river
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2010Ice melt in Beaufort Sea and outflow from Mackenzie river
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Summary
• AVHRR– CCI generally has better coverage than Pathfinder– Pathfinder less likely to reject extreme warm SSTs in
Mackenzie outflow• ATSR– CCI and ARC consistent in images shown
• CCI-ARC differences exist but << 1 K
• Large temperature anomalies can be problematic for Phase 1 CCI– Both SST retrieval and Bayesian cloud screening used
ECMWF-interim SST (OSTIA) as prior
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SST CCI Phase 1• Demonstration (two 3 month periods)
– AATSR L2P (1km)• OE retrieval• Bayesian cloud screening
– Metop-A AVHRR L3C (0.05°)• Meteo France retrieval• Meteo France cloud screening
– SEVIRI L3C (0.05°)• Meteo France retrieval• Meteo France cloud screening
– AMSR-E L2P (0.25°)• RSS retrieval
– TMI L2P (0.25°)• RSS retrieval
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Known bugs in pre-release data
• Two biggest issues caused by “minor” bugs• No data after midnight– Unsigned int bug in pre-processing code– Last orbit in day cut off at mid-night– Regular gaps in ATSR data
• Missing data flagged as quality_level 5– QC based on cloud mask and uncertainty information– OE retrieval can produce SST < 271.15 K– SST-CCI product can not store SST values < 271.15 K