Sst_cci Chris Merchant The University of Edinburgh.
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Transcript of Sst_cci Chris Merchant The University of Edinburgh.
sst_cci
Chris MerchantThe University of Edinburgh
1. User requirements analysis
User requirements survey
• Methods• literature review
• lessons learned review
• web-based discussions / interviews
• questionnaire
• Analysis of 108 completed questionnaire respondents
Approach to analysis of user requirements: e.g., spatial resolution
Threshold Breakthrough Objective
User Requirements Summary
• SST records longer than 30 years (breakthrough)• Phase 1 will cover 1991 – 2010
• L4 SSTs available within 1 week, 99% reliable
• Homogeneous record always available, upgrades• Will carry into system specification for operations
• Proper uncertainties and simple quality information• Pixel/cell flags
• NetCDF available by ftp, CF compliant• Yes + GHRSST compatibility
• Simple documentation … that describes all steps in product development (!)
• Certainly algorithm and uncertainty information readily obtainable
Climate Operations Model
User Requirements Summary
• SST records longer than 30 years (breakthrough)• Phase 1 will cover 1991 – 2010
• L4 SSTs available within 1 week, 99% reliable
• Homogeneous record always available, upgrades• Will carry into system specification for operations
• Proper uncertainties and simple quality information
• NetCDF available by ftp, CF compliant• Yes + GHRSST compatibility
• Simple documentation … that describes all steps in product development (!)
• Certainly algorithm and uncertainty information readily obtainable
User Requirements for SST• Skin SST retrievals and buoy-depth SST estimates
• As planned
• GCOS (2006) supports blending skin and “bulk”/in situ
• 3 hourly analyses at 10 km resolution or better• Daily at 0.05 deg
• Fundamental research for sub-daily analyses proposed as option
• Bias: 0.01 K over 100 km scales• SST CCI target is to demonstrate 0.1 K over 1000 km scales
• GCOS (2006) states 0.25 K with no indication of applicable scale
• Stability 0.01 K, per decade, seasonally, diurnally• Our aim is 0.05 K
• GCOS (2006) presents only 0.1 K per decade
• Mix of L4 (analyses), L3 (regridded) and L2 (native)
Require-ment
GCOS(2006) CMUG(2010) URD L3/L4breakthru’
SST CCI plan
Accuracy 0.25 K 0.1 K / 10 km0.2 K / 1 km
0.02 K / 100 km 0.1 K/1000 km
Precision None 0.05 K in monthly
0.05 K / 100 km Varies, quantify it
Stability 0.1 K / decade 0.05 K / per decade
0.02 K / per decade; 0.05 K seasonally, diurnally
0.05 K / per decade, seasonally, diurnally
Spatial resolution
1 km 1 km (re-anal), 10 km
0.1 deg 0.05 deg
Temporal resolution
3 hourly “observing cycle”
3 h (re-anal.), daily, monthly
Day/night (UTC) Day/night on standardized local time (L3)
Uncertainty information
None SSAOBSSEOB
Total uncertainty
Total, systematic and uncorrelated
Type of SST
Blended Skin & ?? Skin & buoy-depth
Skin and buoy-depth
Period ~1980 - now 1991 - 2010
2. Product specification
Product Specification Process
• Prepared by someone with EO experience within the CRG, advised by Science Team
• Covering• file metadata
• discovery metadata
• document revision control
• file format
• file naming
• Input constraints: GHRSST, CMIP5, CF and Guidance• “Data and Metadata Requirements for CMIP5 Observational Datasets“
• GDS2.0 takes precedence over CMIP5 where in conflict
• Such conflicts will be debated within GHRSST
• GHRSST community for international review
3. Consistency between ECVs
Consistency of ECVs: two aspects
• Spatio-temporal consistency
• Compatibility with – CLOUDS at L1B/L2 levels from same sensors
– SEA ICE at L2/L3/L4
– COLOUR at L3/L4 – want to be able to co-analyse
– SEA-LEVEL?
• Estimation consistency
• Use compatible auxiliary info: aerosol, winds …
• Mutual benefit from joint retrieval (in principle)– CLOUDS (e.g., thin and/or subpixel allowing SST)
– AEROSOL (correlations in geophysics and errors)
3. Uncertainties in products
Starting point
• Uncertainty estimation is part of retrieval
• (Some) users need to know about variability of uncertainty – need an uncertainty for every SST
• Components of uncertainty have different correlation properties. Propagation of uncertainty from L2 to L3 and L4 needs to address each component appropriately.
Uncertainty Characterisation
• Six components to uncertainty
• Random (precision / uncorrelated)• E.g., Radiometric noise: ~Gaussian NEDT, uncorrelated
• Estimate by propagation through retrieval
• Pseudo-random (precision / corr. sub-synoptic)• Algorithmic inadequacy
• Correlated on synoptic space-time scales
• Can simulate
• Systematic (accuracy / correlated)• Forward model bias, calibration bias…
• Prior error
Merchant C J, Horrocks L A, Eyre J
and O'Carroll A G (2006), Retrievals of SST from infra-red
imagery: origin and form of systematic
errors, Quart. J. Royal Met. Soc., 132, 1205-
1223.
Uncertainty Characterisation
• Contaminant (precision, accuracy)• Non-Gaussian, asymmetric, sporadic
• E.g., Failure to detect cloud; retrieval error from aerosol
• Various space-time scales
• Sampling• Random: scattered gaps because of cloud
• Systematic: clear-sky effect?, biased false cloud detection
• Stability• Time variation of any systematic effect
• Approach: model / quantify each element
• Aim: reconcile modelled and observed uncertainty
Uncertainty estimation in Round Robin
• SST uncertainty estimation is the reasoned attribution of uncertainty information to an estimate of SST
• Algorithms for SST to include SST uncertainty
• SST uncertainty estimates will be assessed for • BIAS
• INDEPENDENCE
• GENERALITY
• IMPROVABILITY
• DIFFICULTY
5. Needs for ECMWF data