Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data
Abstract
Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05° from satellite observations, to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05–0.1°, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04K in global area coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.
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Total SST uncertainty and uncertainty components were estimated context-specifically.
“context-specific estimation of total SST uncertainty and uncertainty components16,17”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [17]
The Introduction (p. 4) says this paper models sampling uncertainty, one component of the uncertainty budget, while a companion paper addresses context-specific uncertainties from other error sources. Section 4.2 (p. 16) describes estimating sampling uncertainty from clear-sky coverage and SST variability. This source therefore supports context-specific estimation of a component, but not the attributed estimation of total SST uncertainty and its other components. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe uncertainty from sub-sampling is parameterised in terms of the fraction of the cell observed and the variability in SST in the observed part of the cell.
“The uncertainty is parameterised effectively in terms of the fraction of the cell observed and the variability in SST in the observed part of the cell17.”
From Satellite-based time-series of sea-surface temperature since 1981 for climate applications (2019) · cites this paper as [17]
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References 21 · Cited by 40
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- Authors
- Bulgin, C. E., Embury, O., & Merchant, C. J.
- Affiliations
- University of Reading (GB)
- Corresponding author
- Bulgin, C.E.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 177
- Pages
- 287-294
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2292782929
- Cited by
- 40
- References
- 21
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 2 · 2023: 2 · 2022: 2 · 2021: 8 · 2020: 4 · 2019: 6 · 2018: 2 · 2017: 6 · 2016: 1
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Remote Sensing of Environment (publishedVersion)
- CentAUR (University of Reading) (acceptedVersion)
- Funders
- European Space Agency Sea Surface Temperature Climate Change Initiative (ESA ESRIN REF 3-13904/13/I-NB)
- Topics
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Engineering › Aerospace Engineering › Calibration and Measurement Techniques
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Keywords
- sea surface temperature, sampling uncertainty, cloud cover, clear-sky fraction, sea surface temperature variability, AATSR, instrumental noise
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