Uncertainty estimation from first principles: The future of SSES? Gary Corlett (University of Leicester) Chris Merchant (University of Edinburgh)

Current approach to SSES Provide estimate of bias and standard deviation from comparison to drifting buoys – Assumes drifting buoys are ‘truth’ SSES schemes to based on SSES common principles – All sensors have unique schemes Advantages – Common reference for users Disadvantages – Many

Need a more sophisticated approach to specifying uncertainties Strong message in SST-CCI user survey was the need for credible uncertainty information Next few slides: – Why is a bias and SD w.r.t. buoys inadequate? – What uncertainty information is needed? – What sort of approach could be taken? – How should uncertainty estimates be validated?

The geophysical limit Retrieval drifters Argo Number Nadir-onlyDual-view Number Nadir-onlyDual-view Median (K) RSD (K) Median (K) RSD(K) Median (K) RSD (K) Median (K) RSD (K) Day time Night time AATSR Version 2.0 (operational data) Drifting buoy data from ICOADS Argo data from Met Office EN3 Match-ups limited to wind speeds > 6 ms -1 Robust statistics AATSR N3 (D3) uncertainty = 0.15 (0.27) K DB uncertainty = 0.2 K Argo uncertainty = K Geophysical uncertainty = 0.1 K (1-km; +/- 2 hours)

Impact of in situ uncertainty on SSES (Standard deviation) Current drifting buoys, ~0.2 K Argo 4 m 0.05 K uncertainty (GHRSST proposal for improved buoys) AMSRE, Pathfinder AVHRR M-F AVHRR True satellite uncertainty SSES ARC dual 3-chan OE 3-chan AVHRR OE 2-chan AVHRR Progress in retrieval is masked by SSES SD approach Limit from geophysical skin-depth variability

Uncertainty Characterisation Six components to SST uncertainty (at least!) Random (precision) – E.g., Radiometric noise: ~Gaussian NEDT, uncorrelated – Estimate by propagation through retrieval – Uncertainty goes as 1/√n when averaging pixels Pseudo-random (precision) – Algorithmic inadequacy / uncertainty – Can simulate magnitude to create estimate – Correlated on ~synoptic space-time scales – No uncertainty reduction when averaging “nearby” pixels Systematic (accuracy) – Forward model bias, calibration bias – No uncertainty reduction from averaging pixels

Uncertainty Characterisation Contaminant (precision, accuracy) – Non-Gaussian, asymmetric, sporadic – E.g., failure to detect cloud, effects of 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 uncertainty component Ideal – model / quantify each element – reconcile modelled and observed uncertainty

What is needed? The random, pseudo-random and systematic components are needed separately for appropriate creation of averaged products with realistic uncertainties. – In addition, some estimate of sampling uncertainty could be introduced during averaging The contaminant uncertainty can only be addressed empirically – it is a research question

Random uncertainty First, define a model for the NEdT for each channel Then propagate through algorithm

Pseudo-random (algorithmic) Tractable by simulation of standard deviation of algorithm performance Can then characterise by geography, TCWV – e.g. for AATSR coefficients

Validating uncertainties Careful use of terms (adopted within CCI): – Validation: The process of assessing, by independent means, the quality of the data products derived from the system outputs. – Discrepancy: difference between satellite estimate and validation measurement – Bias: mean value of discrepancy – to be interpreted in the light of systematic uncertainties in both satellite and validation – Chi-square: degree of agreement between estimated total uncertainties (in both satellite and validation) and the distribution of discrepancies

Summary Uncertainty estimation using in situ data hindered by accuracy of in situ data and geophysical variability – Cannot provide true pixel level uncertainites SST_CCI will use a new approach of estimating uncertainties from first principles – These uncertainties will then be validated Users will then get SST and SST_uncertainty – Not a bias and standard deviation

SST_CCI Round Robin (1) SST_CCI project is holding a round robin algorithm evaluation for – ATSR, AVHRR-GAC, Metop & SEVIRI – BTs, cloud mask, NWP profiles and current best SSTs; also PMW L2P, aerosol & sea-ice concentration and in situ (drifting buoys) Participation is open to all Likely to start in July 2011, for five months

SST_CCI Round Robin (2) To participate you can – Provide SSTs and their uncertainties for any sensor in round robin distribution, or – Provide SSTs and their uncertainties for other sensors Two distributions – Training data issued at start of round-robin Will have in situ data – Selection data issued 1 month from end Will not have in situ data

SST_CCI Round Robin (3) Full supporting documentation to cover RR data content, algorithm selection procedure, data submission process Results published in algorithm selection report, including evaluation to selection in situ data. All data them available for download. Final algorithm will be used in processing – ATSR/AVHRR record for 1991 to 2010 – ATSR/AVHRR/SEVIRI/PMW for 6 months 2011/2012