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