Half a century of satellite remote sensing of sea-surface temperature

Sea-surface temperature (SST) was one of the first ocean variables to be studied from earth observation satellites. Pioneering images from infrared scanning radiometers revealed the complexity of the surface temperature fields, but these were derived from radiance measurements at orbital heights and included the effects of the intervening atmosphere. Corrections for the effects of the atmosphere to make quantitative estimates of the SST became possible when radiometers with multiple infrared channels were deployed in 1979. At the same time, imaging microwave radiometers with SST capabilities were also flown. Since then, SST has been derived from infrared and microwave radiometers on polar orbiting satellites and from infrared radiometers on geostationary spacecraft. As the performances of satellite radiometers and SST retrieval algorithms improved, accurate, global, high resolution, frequently sampled SST fields became fundamental to many research and operational activities. Here we provide an overview of the physics of the derivation of SST and the history of the development of satellite instruments over half a century. As demonstrated accuracies increased, they stimulated scientific research into the oceans, the coupled ocean-atmosphere system and the climate. We provide brief overviews of the development of some applications, including the feasibility of generating Climate Data Records. We summarize the important role of the Group for High Resolution SST (GHRSST) in providing a forum for scientists and operational practitioners to discuss problems and results, and to help coordinate activities world-wide, including alignment of data formatting and protocols and research. The challenges of burgeoning data volumes, data distribution and analysis have benefited from simultaneous progress in computing power, high capacity storage, and communications over the Internet, so we summarize the development and current capabilities of data archives. We conclude with an outlook of developments anticipated in the next decade or so

Optimal estimation of sea surface temperature from split-window observations

Optimal estimation (OE) improves sea surface temperature (SST) estimated from satellite infrared imagery in the “split-window”, in comparison to SST retrieved using the usual multi-channel (MCSST) or non-linear (NLSST) estimators. This is demonstrated using three months of observations of the Advanced Very High Resolution Radiometer (AVHRR) on the first Meteorological Operational satellite (Metop-A), matched in time and space to drifter SSTs collected on the global telecommunications system. There are 32,175 matches. The prior for the OE is forecast atmospheric fields from the Météo-France global numerical weather prediction system (ARPEGE), the forward model is RTTOV8.7, and a reduced state vector comprising SST and total column water vapour (TCWV) is used. Operational NLSST coefficients give mean and standard deviation (SD) of the difference between satellite and drifter SSTs of 0.00 and 0.72 K. The “best possible” NLSST and MCSST coefficients, empirically regressed on the data themselves, give zero mean difference and SDs of 0.66 K and 0.73 K respectively. Significant contributions to the global SD arise from regional systematic errors (biases) of several tenths of kelvin in the NLSST. With no bias corrections to either prior fields or forward model, the SSTs retrieved by OE minus drifter SSTs have mean and SD of − 0.16 and 0.49 K respectively. The reduction in SD below the “best possible” regression results shows that OE deals with structural limitations of the NLSST and MCSST algorithms. Using simple empirical bias corrections to improve the OE, retrieved minus drifter SSTs are obtained with mean and SD of − 0.06 and 0.44 K respectively. Regional biases are greatly reduced, such that the absolute bias is less than 0.1 K in 61% of 10°-latitude by 30°-longitude cells. OE also allows a statistic of the agreement between modelled and measured brightness temperatures to be calculated. We show that this measure is more efficient than the current system of confidence levels at identifying reliable retrievals, and that the best 75% of satellite SSTs by this measure have negligible bias and retrieval error of order 0.25 K

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

Restitution de la température de la mer à partir des données du satellite NOAA/AVHRR

ABSTRACT This paper reviews first the basic problems raised by the Sea Surface Temperature (SST) restitution from A VHRR data, and the solutions adopted at CMS. In particular some results concerning the split window lagorithm errors at large satellite zenith angle will be discussed. Its lain topic concerns however the techniques Used in the operational small scale SST restitution at CMS. The objective of this activity is to provide the French Navy, the océanographie campaigns and some fishing activities with real time or climatological concerning the sea surface temperatures over the European seas. Six zones (2 000 * 2 000 km) have been defined inside the CMS acquisition circle : North Sea, Biscay, Canary Islands, Western Mediterranean, Eastern Mediterranean, Norwegian Sea. After preprocessing on the main computer, each zone (corresponding after sampling and mapping onto a stereopolar grid to a 1 024 * 1 024 pixel image) is analysed, every day, on the image processing system. Various studies or products are based on this processing suite, ranging from daily, weekly (etc.) charts to the making of an SST Atlas over Europe.RÉSUMÉ Ce texte passe d'abord en revue les problèmes de base posés par la restitution de la Température de Surface de la Mer (TSM) à partir des données de l'Advanced Very High Resolution Radiometer (AVHRR) embarqué sur les satellites polaires de la National Oceanic and Atmospheric Administration (NOAA), et les solutions adaptées au CMS. On présentera en particulier la dernière version de l'algorithme de calcul de température de surface tenant compte de l'angle d'incidence de la visée satellitaire. Il aborde ensuite les aspects techniques de la restitution opérationnelle des structures thermiques de la surface des mers européennes à l'échelle fine. Six zones (2 000 * 2 000 km) ont été définies à l'intérieur du cercle d'acquisition du CMS : mer du Nord, golfe de Gascogne, Canaries, Méditerranée occidentale, Méditerranée orientale, mer de Norvège. Après prétraitement sur calculateur principal, chaque zone (correspondant après échantillonage et mise en projection stéréopolaire à une image 1 024 * 1 024) est analysée quotidiennement sur un système interactif de traitement d'image. Divers études et produits sont basés sur cette chaîne opérationnelle, depuis les cartes quotidiennes, hebdomadaires, etc., jusqu'à réalisation d'un Atlas de TSM sur l'Europe.Antoine Jean-Yves, Derrien M, Gaillard O, Le Borgne P, Le Goas C, Marsouin A. Restitution de la température de la mer à partir des données du satellite NOAA/AVHRR. In: Norois, n°155, Juillet-Septembre 1992. pp. 297-304

Observations of the Ushant front displacements with MSG/SEVIRI derived sea surface temperature data

International audienceHourly Sea Surface Temperature (SST) fields derived from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) onboard Meteosat Second Generation (MSG) are frequently used in studies of the diurnal cycle of the ocean. In this article, we focus on high frequency SST variability induced by tidal currents in the Iroise Sea, west of Brittany (France). This region is known for its strong tidal currents that are responsible in summer for the generation of an intense thermal front, the Ushant front. We use hourly MSG/SEVIRI derived SST to compute the displacements of this front. In the northern part of the front, at 48.75°N, we show that the longitudinal displacements of the front on subdiurnal time scales can be explained by the Lagrangian advection induced by surface currents.We also present maps of surface currents computed from hourly SEVIRI derived SST data using the Maximum Cross Correlation (MCC) method. Comparison of SEVIRI derived velocities with velocities obtained with high frequency (HF) radar measurements and a hindcast numerical simulation (Mercator Ocean) gives encouraging results in the northern part of the Ushant front, near the Ushant Island. Within that region, the mean bias of the SEVIRI velocities was below 0.12 m·s− 1, with the standard deviation ranging from 0.26 m·s− 1 during moderate tides to 0.49 m·s− 1 during spring tides. Further offshore, where the surface thermal structures are weaker and the SST more homogeneous, currents derived using the MCC method were overestimated by 0.3 m·s− 1 and showed larger error standard deviations

Surface fluxes in the North Atlantic current during CATCH/FASTEX

CATCH (Couplage avec 1' Atmosphere en Conditions Hivernales) was the oceanic component of FASTEX (Fronts and Atlantic Storm-Track EXperiment). It took place in January and February 1997, in the Newfoundland Basin near 47 degrees N, 40 degrees W, a region characterized by the presence of the warm North Atlantic Current and cold surrounding waters. CATCH was devoted to the study of the parametrization of surface turbulent fluxes in strong winds and changing directions, the surface-flux variability related to the passage of atmospheric fronts and the influence on fluxes of the strong sea surface temperature gradients associated with the North Atlantic Current. This paper presents first results of ship data analysis. A large range of wind and stratification conditions were experienced: 5% of measured winds were higher than 20 m s(-1); 30% of unstable stratification (air-sea temperature differences lower than −5 degC) and 30% of very dry conditions lair-sea moisture differences lower than -2.5 g kg(-1) were sampled. Surface turbulent heat and momentum fluxes were obtained using the inertial-dissipative method from which a bulk algorithm was derived. A significant increase of latent-heat and momentum-transfer coefficients with increasing wind is obtained, This parametrization is compared to others published using the CATCH dataset. For high winds and unstable stratifications, differences between schemes reach 200 W m(-2) for latent-heat flux values of 600 W m(-2). Radiative and turbulent ship-measured fluxes are compared with modelled fluxes from the European Centre for Medium-Range Forecasts (ECMWF) along the ship's trajectory: each component of the net heat budget is higher in the ECMWF model, consequently the heat loss of the ocean is 35% higher in the model. Finally, the effect of sea surface temperature fronts on surface turbulent fluxes is analysed by evaluating the contribution of the various terms in the Aux variations. showing a significant impact of the surface temperature change in all unperturbed cases

IASI‐derived sea surface temperature dataset for climate studies

International audienceSea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST dataset suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST dataset derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck’s Law and simple atmospheric corrections. We assess the dataset using the ERA5 reanalysis and the Eumetsat‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a standard deviation of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed dataset shows a stable bias and standard deviation, which is an advantage for climate studies. The inter‐annual variability and trends were compared with other SST datasets: ERA5, Hadley Centre's SST (HadISST) and NOAA’s Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST dataset is able to capture the patterns of inter‐annual variability well, showing the same areas of high inter‐annual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI dataset reproduces the same trend patterns found in the other datasets (i.e.: cooling trend in the North Atlantic, warming trend over the Mediterranean)