Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05 degrees 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 degrees, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04 K in Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods

Evaluating operational AVHRR sea surface temperature data at the coastline using surfers

Sea surface temperature (SST) is an essential climate variable that can be measured routinely from Earth Observation (EO) with high temporal and spatial coverage. To evaluate its suitability for an application, it is critical to know the accuracy and precision (performance) of the EO SST data. This requires comparisons with co-located and concomitant in situ data. Owing to a relatively large network of in situ platforms there is a good understanding of the performance of EO SST data in the open ocean. However, at the coastline this performance is not well known, impeded by a lack of in situ data. Here, we used in situ SST measurements collected by a group of surfers over a three year period in the coastal waters of the UK and Ireland, to improve our understanding of the performance of EO SST data at the coastline. At two beaches near the city of Plymouth, UK, the in situ SST measurements collected by the surfers were compared with in situ SST collected from two autonomous buoys located ∼7 km and ∼33 km from the coastline, and showed good agreement, with discrepancies consistent with the spatial separation of the sites. The in situ SST measurements collected by the surfers around the coastline, and those collected offshore by the two autonomous buoys, were used to evaluate the performance of operational Advanced Very High Resolution Radiometer (AVHRR) EO SST data. Results indicate: (i) a significant reduction in the performance of AVHRR at retrieving SST at the coastline, with root mean square errors in the range of 1.0 to 2.0 °C depending on the temporal difference between match-ups, significantly higher than those at the two offshore stations (0.4 to 0.6 °C); (ii) a systematic negative bias in the AVHRR retrievals of approximately 1 °C at the coastline, not observed at the two offshore stations; and (iii) an increase in the root mean square error at the coastline when the temporal difference between match-ups exceeded three hours. Harnessing new solutions to improve in situ sampling coverage at the coastline, such as tagging surfers with sensors, can improve our understanding of the performance of EO SST data in coastal regions, helping inform users interested in EO SST products for coastal applications. Yet, validating EO SST products using in situ SST data at the coastline is challenged by difficulties reconciling the two measurements, which are provided at different spatial scales in a dynamic and complex environment

Sampling errors in satellite-derived infrared sea-surface temperatures. Part II: Sensitivity and parameterization

In the recent work of Liu and Minnett (2016), we estimated the sampling errors in Moderate Resolution Imaging Spectroradiometer (MODIS) Sea-Surface Temperatures (SSTs) due to clouds and other causes, and characterized the global error dependence on the variability of clouds and SST. Here we report sampling error sensitivity to the choice of reference field and the error variation when data from a different year are used. We also developed an empirical model to parameterize sampling errors. Our sensitivity tests show that the sampling error quantification method developed is robust and can reveal the consequences of missing infrared SST observations primarily due to clouds. Since the previously found pronounced negative sampling errors along the Tropical Instability Waves are largely dependent on the SST gradients, here these regional sampling errors are quantified using data from an El Niño year, confirming that the weakened meridional SST gradient due to El Niño can reduce the negative sampling errors. Furthermore, the climatology-derived sampling errors are found to be a primary component that can be utilized to estimate and parameterize the sampling errors, especially for the spatial sampling errors. For the temporal sampling errors, good estimates are obtained especially in the high latitudes and stratocumulus regions, by incorporating an empirical model proposed in this study and the previously found sampling error dependence. •Global MUR SST fields and HYCOM-NCODA SST reanalysis have marked differences.•The global sampling error generated from MUR and HYCOM SSTs are very similar.•El Niño events reduce the negative sampling errors in TIW region.•The SST seasonality can only explain <30% sampling errors in the monthly SST fields.•The sampling error parameterization based on previous results shows promise