True Sea Surface Temperature Research Articles

Validating Landsat Analysis Ready Data for Nearshore Sea Surface Temperature Monitoring in the Northeast Pacific

In the face of global ocean warming, monitoring essential climate variables from space is necessary for understanding regional trends in ocean dynamics and their subsequent impacts on ecosystem health. Analysis Ready Data (ARD), being preprocessed satellite-derived products such as Sea Surface Temperature (SST), allow for easy synoptic analysis of temperature conditions given the consideration of regional biases within a dynamic range. This is especially true for SST retrieval in thermally complex coastal zones. In this study, we assessed the accuracy of 30 m resolution Landsat ARD Surface Temperature products to measure nearshore SST, derived from Landsat 8 TIRS, Landsat 7 ETM+, and Landsat 5 TM thermal bands over a 37-year period (1984–2021). We used in situ lighthouse and buoy matchup data provided by Fisheries and Oceans Canada (DFO). Excellent agreement (R2 of 0.94) was found between Landsat and spring/summer in situ SST at the farshore buoy site (>10 km from the coast), with a Landsat mean bias (root mean square error) of 0.12 °C (0.95 °C) and a general pattern of SST underestimation by Landsat 5 of −0.28 °C (0.96 °C) and overestimation by Landsat 8 of 0.65 °C (0.98 °C). Spring/summer nearshore matchups revealed the best Landsat mean bias (root mean square error) of −0.57 °C (1.75 °C) at 90–180 m from the coast for ocean temperatures between 5 °C and 25 °C. Overall, the nearshore image sampling distance recommended in this manuscript seeks to capture true SST as close as possible to the coastal margin—and the critical habitats of interest—while minimizing the impacts of pixel mixing and adjacent land emissivity on satellite-derived SST.

Space-time multi-level modeling for zooplankton abundance employing double data fusion and calibration

An important objective for marine biologists is to forecast the distribution and abundance of planktivorous marine predators. To do so, it is critically important to understand the spatiotemporal dynamics of their prey. Here, the prey we study are zooplankton and we build a novel space-time hierarchical fusion model to describe the distribution and abundance of zooplankton species in Cape Cod Bay (CCB), MA, USA. The data were collected irregularly in space and time at sites within the first half of the year over a 17 year period, using two different sampling methods. We focus on sea surface zooplankton abundance and incorporate sea surface temperature as a primary driver, also collected with two different sampling methods. So, with two sources for each, we observe true abundance or true sea surface temperature with measurement error. To account for such error, we apply calibrations to align the data sources and use the fusion model to develop a prediction of daily spatial zooplankton abundance surfaces throughout CCB. To infer average abundance on a given day within a given year in CCB, we present a marginalization of the zooplankton abundance surface. We extend the inference to consider abundance averaged to a bi-weekly or annual scale as well as to make an annual comparison of abundance.

Evaluation and selection of SST regression algorithms for JPSS VIIRS

AbstractTwo global level 2 sea surface temperature (SST) products are generated at NOAA from the Suomi National Polar‐Orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data records (L1) with two independent processing systems, the Joint Polar Satellite System (JPSS) Interface Data Processing Segment (IDPS) and the NOAA heritage Advanced Clear‐Sky Processor for Oceans (ACSPO). The two systems use different SST retrieval and cloud masking algorithms. Validation against in situ and L4 analyses has shown suboptimal performance of the IDPS product. In this context, existing operational and proposed SST algorithms have been evaluated for their potential implementation in IDPS. This paper documents the evaluation methodology and results. The performance of SST retrievals is characterized with bias and standard deviation with respect to in situ SSTs and sensitivity to true SST. Given three retrieval metrics, all being variable in space and with observational conditions, an additional integral metric is needed to evaluate the overall performance of SST algorithms. Therefore, we introduce the Quality Retrieval Domain (QRD) as a part of the global ocean, where the retrieval characteristics meet predefined specifications. Based on the QRDs analyses for all tested algorithms over a representative range of specifications for accuracy, precision, and sensitivity, we have selected the algorithms developed at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI‐SAF) for implementation in IDPS and ACSPO. Testing the OSI‐SAF algorithms with ACSPO and IDPS products shows the improved consistency between VIIRS SST and Reynolds L4 daily analysis. Further improvement of the IDPS SST product requires adjustment of the VIIRS cloud and ice masks.

What is responsible for the strong observed asymmetry in teleconnections between El Niño and La Niña?

AbstractA large asymmetric component (El Niño + La Niña) of El Niño–Southern Oscillation (ENSO)‐related teleconnections over North America is found during 1984–2009 that is comparable in strength to the commonly studied symmetric component (El Niño − La Niña). Climate reforecasts spanning this period are diagnosed in order to understand the processes responsible for the observed asymmetry. It is confirmed that an asymmetric component is indeed a fundamental property of atmospheric responses to recent ENSO forcing. Each and every composite of a 16‐member reforecast ensemble has appreciable asymmetry in tropical Pacific rainfall, upper tropospheric Pacific‐North American circulation patterns, and contiguous U.S. surface temperatures. There is considerable sampling variability in the magnitude of this asymmetric component among individual reforecast composites. We argue therefore that the true sea surface temperature boundary‐forced signal of ENSO teleconnections is likely composed of a symmetric component having greater magnitude than its asymmetric component, though the latter is an important property of how ENSO affects North American climate.

SST spatial anisotropic covariances from METOP-AVHRR data

The Advanced Very High Resolution Radiometer (AVHRR) instrument on-board the METOP satellite is designed to provide very accurate measurements of Sea Surface Temperature (SST). In this work, using one year of METOP-AVHRR data and a geostatistical approach, we characterize the spatial anisotropy and non-stationarity of the SST variability using oriented ellipsoids. The method is also able to separate the true SST variability from the artificial error introduced by the METOP-AVHRR sensor. These spatial parameters are then used for producing variability atlases (available on-line) over the whole ocean.

Objective Determination of Feature Resolution in Two Sea Surface Temperature Analyses

Abstract Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

Development and evaluation of SST algorithms for GOES-R ABI using MSG SEVIRI as a proxy

Cross-evaluation of sea surface temperature (SST) algorithms was undertaken using split-window channels of Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (SEVIRI) as a proxy for the Geostationary Operational Environmental Satellites-R (GOES-R) Advanced Baseline Imager (ABI). The goal of the study was to select the algorithm which provides the highest and the most uniform SST accuracy within the area observed by the geostationary sensor. The previously established algorithms, such as Non-Linear Regression (NLR) and Optimal Estimation (OE) were implemented along with two new algorithms, Incremental Regression (IncR) and Corrected Non-Linear Regression (CNLR), developed within preparations for the GOES-R ABI mission. OE, IncR and CNLR adopt the first guesses for SST and brightness temperatures (BT) and retrieve deviations of SST from the first guess (increments). OE retrieves SST increments with inversion of the radiative transfer model, whereas CNLR and IncR use regression equations. The difference between CNLR and IncR is that CNLR uses NLR coefficients, whereas IncR implies optimization of coefficients specifically for incremental formulation. Accuracy and precision of SST retrievals were evaluated by comparison with drifting buoys. The major observations from this study are as follows: 1) all algorithms adopting first guesses for SST and BTs are capable of improving SST accuracy and precision over NLR; and 2) IncR delivers the highest overall SST precision and the most uniform distributions of regional SST accuracy and precision. This paper also addresses implementation and validation issues such as bias correction in simulated BTs; preserving sensitivity of incremental SST retrievals to true SST variations; and selection of criteria for optimization and validation of incremental algorithms.

A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: A new retrieval scheme

We present a new coefficient-based retrieval scheme for estimation of sea surface temperature (SST) from the Along Track Scanning Radiometer (ATSR) instruments. The new coefficients are banded by total column water vapour (TCWV), obtained from numerical weather prediction analyses. TCWV banding reduces simulated regional retrieval biases to <0.1K compared to biases ~0.2K for global coefficients. Further, detailed treatment of the instrumental viewing geometry reduces simulated view-angle related biases from ~0.1K down to <0.005K for dual-view retrievals using channels at 11 and 12μm. A novel analysis of trade-offs related to the assumed noise level when defining coefficients is undertaken, and we conclude that adding a small nominal level of noise (0.01K) is optimal for our purposes.When applied to ATSR observations, some inter-algorithm biases appear as TCWV-related differences in SSTs estimated from different channel combinations. The final step in coefficient determination is to adjust the offset coefficient in each TCWV band to match results from a reference algorithm. This reference uses the dual-view observations of 3.7 and 11μm. The adjustment is independent of in situ measurements, preserving independence of the retrievals. The choice of reference is partly motivated by uncertainty in the calibration of the 12μm of Advanced ATSR.Lastly, we model the sensitivities of the new retrievals to changes to TCWV and changes in true SST, confirming that dual-view SSTs are most appropriate for climatological applications.

Assimilation of sea surface temperature into CECOM by flux correction

Sea surface temperature (SST) from a near real-time data set produced from satellites data has been assimilated into a coupled ice–ocean forecasting model (Canadian East Coast Ocean Model) using an efficient data assimilation method. The method is based on an optimal interpolation scheme by which SST is melded into the model through the adjustment of surface heat flux. The magnitude and space–time variation of the adjustment depend on the depth of heat diffusion into the water column in response to changes in surface flux, the correlation time scale of the data, and model and data errors. The diffusion depth is scaled by the eddy diffusivity for temperature. The ratio of the model and data errors is treated as an adjustable parameter. To evaluate the quality of the assimilation, the results from the model with and without assimilation are compared to independent ship data from the Atlantic Zone Monitoring Program and the World Ocean Circulation Experiment. It is shown that the assimilation has a significant impact on the modeled SST, reducing the root mean square difference (RMSD) between the model SST and the ship SST by 0.63°C or 37%. The RMSD of the assimilated SST is smaller than that of the satellite SST by 0.23°C. This suggests that model simulations or predictions with data assimilation can provide the best estimate of the true SST. A sensitivity study is performed to examine the change of the model RMSD with the adjustable parameter in the assimilation equation. The results show that there is an optimal value of the parameter and the model SST is not very sensitive to the parameter.

Sea surface temperature from a geostationary satellite by optimal estimation

Optimal estimation (OE) is applied as a technique for retrieving sea surface temperature (SST) from thermal imagery obtained by the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on Meteosat 9. OE requires simulation of observations as part of the retrieval process, and this is done here using numerical weather prediction fields and a fast radiative transfer model. Bias correction of the simulated brightness temperatures (BTs) is found to be a necessary step before retrieval, and is achieved by filtered averaging of simulations minus observations over a time period of 20 days and spatial scale of 2.5° in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5° in latitude and longitude. Results for the OE SST are compared to results using a traditional non-linear retrieval algorithm (“NLSST”), both validated against a set of 30108 night-time matches with drifting buoy observations. For the OE SST the mean difference with respect to drifter SSTs is − 0.01 K and the standard deviation is 0.47 K, compared to − 0.38 K and 0.70 K respectively for the NLSST algorithm. Perhaps more importantly, systematic biases in NLSST with respect to geographical location, atmospheric water vapour and satellite zenith angle are greatly reduced for the OE SST. However, the OE SST is calculated to have a lower sensitivity of retrieved SST to true SST variations than the NLSST. This feature would be a disadvantage for observing SST fronts and diurnal variability, and raises questions as to how best to exploit OE techniques at SEVIRI's full spatial resolution.

Examination of the Merged Sea Surface Temperature Using Wavelet Analysis

In the previous study, merged sea surface temperature (SST) dataset called “New Generation SST” has been produced from several infrared and microwave satellite SSTs through an objective mapping. Here we examine the merged SST by comparison with moored buoy SST at 1 m depth, which is treated as true sea surface temperature. Comparison between wavelet spectra of merged and buoy SSTs shows that the former have larger amplitudes than those of the latter, which is partly explained as an aliasing effect due to TRMM Microwave Imager (TMI) aboard Tropical Rainfall Measuring Mission (TRMM) sampling on merged products. Coherency between wavelet-decomposed merged and buoy SSTs has high values in autumn and low ones in winter to spring. In winter, phase differences between them are positive, meaning that wavelet components of merged SST lag those of buoy SST. Reasons for delay and low coherency are: (1) seasonal components of merged SSTs are strongly affected by a lack of infrared SSTs due to clouds in winter, and (2) small-scale oceanic features, undetectable by coarse-resolution microwave SSTs, are blurred by the merging process. Improvements of merging methodology are discussed with regard to present study results.

A correlation based cloud-detection algorithm combined with an examination of some split-window assumptions

Abstract Twenty-six daytime NOAA-11 AVHRR images covering the Danish waters are analysed together with 99 cloud-free bulk temperature measurements. The images are cloud screened with a cloud-detection algorithm, which in excess of the usual threshold and variation tests consists of: a min_max-routine which among suspicious pixels points out the cloud contaminated pixels, and a separation of cloud pixels from cold water pixels by means of the correlation coefficient between the channel 4 brightness temperature and the channel 4-channel 5 brightness temperature. In the cloud screened images, the channel 4-channel 5 brightness temperatures do not increase with the satellite zenith angle and do not seem to carry any significant information about the true sea surface temperature (SST). Local meteorological and oceanographic effects are believed to be more important than the channel 4-channel 5 brightness temperatures.

An algorithm for sea surface temperature estimation from ERS-1 ATSR using moisture dependent coefficients: a simulation study

A new algorithm for estimating sea surface temperature (SST) from ERS-1 ATSR data using moisture dependent coefficients has been proposed. The clear-column water vapour (WV) information required for selecting the appropriate coefficients has also been derived from ERS-1 ATSR data. The method has been tested with the simulated ATSR data by comparing the estimated SST with the true SST used for simulating the data. The comparison of SST estimation errors by present and standard methods, under different noise conditions, has indicated improvements by the present method over the standard method. The small errors anticipated in SST estimation due to the errors in WV estimation causing erroneous selection of coefficients have also been taken care of by making use of overlapping ranges of WV for moisture dependent SST estimation coefficients. The method could be used for operational SST from ATSR data.

Simultaneous observations of sea surface temperature in the western equatorial Pacific Ocean by bulk, radiative and satellite methods

The increasing reliance on satellite observations for mapping and following changes in sea surface temperature (SST) has renewed interest in the measurement of the skin temperature of the ocean surface. In this experiment, skin surface temperature was measured with a narrow‐band radiometer over a 13‐day period in the western equatorial Pacific Ocean and compared with bulk sea temperatures and satellite‐derived estimates. A cool skin was found to be a quasi‐permanent feature in this region; its temperature averaged 0.3°C less than the bulk sea temperature. Conventional models for the cool skin were found to give a reasonable fit down to wind speeds of 1 m s−1. Satellite measurements showed some deviations from the true SST values, a result not unexpected, considering the substantial corrections required as a result of the high atmospheric water vapor content in these latitudes.

A Single-Channel, Double-Viewing Angle Method for Sea Surface Temperature Determination from Coincident METEOSAT and TIROS-N Radiometric Measurements

Abstract An experimental simulation of a single-channel, double-angle viewing technique for the determination of sea surface temperature from satellite is presented. This method relies upon the fact that the same area can be viewed simultaneously at two different angles (different air masses) by the geostationary satellite METEOSAT and by the polar orbiting satellite TIROS-N. Extrapolating the two air mass observations to zero air mass is shown to give a value of the temperature in good agreement with the true sea surface temperature. A discussion concerning the viewing angles is presented.