Search

Cloud detection is a source of significant errors in retrieval of sea surface temperature (SST).We apply a Bayesian cloud detection scheme to 37 years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data, which is an important source of multi-decadal global SST information. The Bayesian scheme calculates a probability of clear-sky for each image pixel, conditional on the satellite observations and prior probability. We compare the cloud detection performance to the operational Clouds from AVHRR Extended algorithm (CLAVR-x), as a measure of improvement from reduced cloud-related errors. To do this we use sea surface temperature differences between satellite retrievals and in situ observations from drifting buoys and the Global Tropical Moored Buoy Array (GTMBA). The Bayesian scheme reduces the absolute difference between the mean and median SST biases and reduces the standard deviation of the SST differences by ~10% for both daytime and nighttime retrievals. These reductions are indicative of removing cloud contaminated outliers in the distribution, as these fall only on one side of the distribution forming a cold tail. At a probability threshold of 0.9 typically used to determine a binary cloud mask for SST retrieval, the Bayesian mask also reduces the robust standard deviation by ~5-10% during the day, in comparison with the operational cloud mask. This shows an improvement in the central distribution of SST differences for daytime retrievals. [ABSTRACT FROM AUTHOR]

NOAA's new operational analysis combines polar-orbiting and geostationary data to provide daily global fields of sea surface temperature on a 0.05° (~5 km) grid for a range of applications in climate, ecosystems, weather, and mesoscale oceanography. [ABSTRACT FROM AUTHOR]

Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth's surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown. [ABSTRACT FROM AUTHOR]

The Sentinel-3 mission is part of the Copernicus programme space segment and has the objective of making global operational observations of ocean, land and atmospheric parameters with its four on-board sensors. Two Sentinel-3 satellites are currently on orbit, providing near-daily global coverage. Sentinel-3A was launched on 16 February 2016 and Sentinel-3B on 25 April 2018. For the early part of its operation, Sentinel-3B flew in tandem with Sentinel-3A, flying 30 s ahead of its twin mission. This provided a unique opportunity to compare the instruments on the two satellites, and to test the per pixel uncertainty values in a metrologically-robust manner. In this work, we consider the tandem-phase data from the infrared channels of one of the on-board instruments: the Sea and Land Surface Temperature Radiometer, SLSTR. A direct comparison was made of both the Level 1 radiance values and the Level 2 sea surface temperature values derived from those radiances. At Level 1, the distribution of differences between the sensor values were compared to the declared uncertainties for data gridded on to a regular latitude-longitude grid with propagated pixel uncertainties. The results showed good overall radiometric agreement between the two sensors, with mean differences of ∼0.06 K, although there was a scene-temperature dependent difference for the oblique view that was consistent with what was expected from a stray light effect observed pre-flight. We propose a means to correct for this effect based on the tandem data. Level 1 uncertainties were found to be representative of the variance of the data, expect in those channels affected by the stray light effect. The sea surface temperature results show a very small difference between the sensors that could be in part due to the fact that the Sentinel-3A retrieval coefficients were also applied to the Sentinel-3B retrieval because the Sentinel-3B coefficients are not currently available. This will lead to small errors between the S3A and S3B retrievals. The comparison also suggests that the retrieval uncertainties may need updating for two of the retrieval processes that there are extra components of uncertainty related the quality level and the probability of cloud that should be included. Finally, a study of the quality flags assigned to sea surface temperature pixel values provided valuable insight into the origin of those quality levels and highlighted possible uncertainties in the defined quality level. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing. [ABSTRACT FROM AUTHOR]

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics. Measurement(s) temperature of water Technology Type(s) satellite imaging Factor Type(s) time • geographic location Sample Characteristic - Environment sea Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9939638 [ABSTRACT FROM AUTHOR]

The Chinese Ocean Color and Temperature Scanner (COCTS) on board the Haiyang-1B (HY-1B) satellite has two thermal infrared channels (9 and 10) centred near 11 μm and 12 μm, respectively, which are intended for sea surface temperature (SST) observations. To improve the accuracy of COCTS SSTs, inter-calibration of COCTS thermal infrared radiance is carried out. The Infrared Atmospheric Sounding Interferometer (IASI) on board MetOp-A satellite is used as inter-calibration reference owing to its hyperspectral nature and high-quality measurements. The inter-calibration of HY-1B COCTS thermal infrared radiances with IASI is undertaken for data from the Period 2009–2011 located in the northwest Pacific. Collocations of COCTS radiance with IASI are identified within a temporal window of 30 min, a spatial window of 0.12° and an atmospheric path tolerance of 3%. Matched IASI spectra are convolved with the COCTS spectral response functions, while COCTS pixels within the footprint of each IASI pixel are spatially averaged, thus creating matched IASI-COCTS radiance pairs that should agree well in the absence of satellite biases. The radiances of COCTS 11 and 12 μm channel are lower than IASI with relatively large biases, and a strong dependence of difference on radiance in the case of 11 μm channel. We used linear robust regression for four different detectors of COCTS separately to obtain the inter-calibration coefficients to correct the COCTS radiance. After correction, the mean values of COCTS 11 and 12 μm channel minus IASI radiance are −0.02 mW m−2 cm sr−1 and −0.01 mW m−2 cm sr−1, respectively, with corresponding standard deviations of 0.51 mW m−2 cm sr−1 and 0.57 mW m−2 cm sr−1. Striped noise is present in COCTS original radiance imagery associated with inconsistency among the four detectors, and inter-calibration is shown to reduce, although not eliminate, the striping. The calibration accuracy of COCTS is improved after inter-calibration, which is potentially useful for improving COCTS SST accuracy in the future. [ABSTRACT FROM AUTHOR]

Abstract: There are a growing number of level 4 (L4; gap-free gridded) sea surface temperature (SST) products generated by blending SST data from various sources which are available for use in a wide variety of operational and scientific applications. In most cases, each product has been developed for a specific user community with specific requirements guiding the design of the product. Consequently differences between products are implicit. In addition, anomalous atmospheric conditions, satellite operations and production anomalies may occur which can introduce additional differences. This paper describes a new web-based system called the L4 SST Quality Monitor (L4-SQUAM) developed to monitor the quality of L4 SST products. L4-SQUAM intercompares thirteen L4 products with 1-day latency in an operational environment serving the needs of both L4 SST product users and producers. Relative differences between products are computed and visualized using maps, histograms, time series plots and Hovmöller diagrams, for all combinations of products. In addition, products are compared to quality controlled in situ SST data (available from the in situ SST Quality Monitor, iQUAM, companion system) in a consistent manner. A full history of products statistics is retained in L4-SQUAM for time series analysis. L4-SQUAM complements the two other Group for High Resolution SST (GHRSST) tools, the GHRSST Multi Product Ensemble (GMPE) and the High Resolution Diagnostic Data Set (HRDDS) systems, documented in part 1 of this paper and elsewhere, respectively. Our results reveal significant differences between SST products in coastal and open ocean areas. Differences of >2°C are often observed at high latitudes partly due to different treatment of the sea-ice transition zone. Thus when an ice flag is available, the intercomparisons are performed in two ways: including and excluding ice-flagged grid points. Such differences are significant and call for a community effort to understand their root cause and ensure consistency between SST products. Future work focuses on including the remaining daily L4 SST products, accommodating for newer L4 SSTs which resolve the diurnal variability and evaluating retrospectively regenerated L4 SSTs to support satellite data reprocessing efforts aimed at generating improved SST Climate Data Records. [Copyright &y& Elsevier]