IAGOS (www.iagos.org) is a European Research Infrastructure using commercial aircraft (Airbus A340, A330, and soon A350) for automatic and routine measurements of atmospheric composition including reactive gases (ozone, carbon monoxide, nitrogen oxides, volatile organic compounds), greenhouse gases (water vapour, carbon dioxide, methane), aerosols and cloud particles along with essential thermodynamic parameters. The main objective of IAGOS is to provide the most complete set of high-quality essential climate variables (ECV) covering several decades for the long-term monitoring of climate and air quality. The observations are stored in the IAGOS data centre along with added-value products to facilitate the scientific interpretation of the data. IAGOS began as two European projects, MOZAIC and CARIBIC, in the early 1990s. These projects demonstrated that commercial aircraft are ideal platforms for routine atmospheric measurements. IAGOS then evolved as a European Research Infrastructure offering a mature and sustainable organization for the benefits of the scientific community and for the operational services in charge of air quality and climate change issues such as the Copernicus Atmosphere Monitoring Services (CAMS) and the Copernicus Climate Change Service (C3S). IAGOS is also a contributing network of the World Meteorological Organization (WMO). IAGOS provides measurements of numerous chemical compounds which are recorded simultaneously in the critical region of the upper troposphere – lower stratosphere (UTLS) and geographical regions such as Africa and the mid-Pacific which are poorly sampled by other means. The data are used by hundreds of groups worldwide performing data analysis for climatology and trend studies, model evaluation, satellite validation and the study of detailed chemical and physical processes around the tropopause. IAGOS data also play an important role in the re-assessment of the climate impact of aviation. Most important in the context of weather-related research, IAGOS and its predecessor programmes provide long-term observations of water vapour and relative humidity with respect to ice in the UTLS as well as throughout the tropospheric column during climb-out and descending phases around airports, now for more than 25 years. The high quality and very good resolution of IAGOS observations of relative humidity over ice are used to better understand the role of water vapour and of ice-supersaturated air masses in the tropopause region and to improve their representation in numerical weather and climate forecasting models. Furthermore, CAMS is using the water vapour vertical profiles in near real time for the continuous validation of the CAMS atmospheric models.

The paper gives an overview of the development of satellite oceanography over the past five years focusing on the most relevant issues for operational oceanography. Satellites provide key essential variables to constrain ocean models and/or serve downstream applications. New and improved satellite data sets have been developed and have directly improved the quality of operational products. The status of the satellite constellation for the last five years was, however, not optimal. Review of future missions shows clear progress and new research and development missions with a potentially large impact for operational oceanography should be demonstrated. Improvement of data assimilation techniques and developing synergetic use of high resolution satellite observations are important future priorities.

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST.In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

The sensitivity of passive microwave observations to the sea surface temperature (SST) is carefully analyzed, with the objective of designing an optimized satellite instrument, MICROwave Wind And Temperature (MICROWAT), dedicated to an “all‐weather” estimation of the SST at high spatial resolution (15 km). Our study stresses the importance of low‐frequency observations around 6 GHz for accurate SST retrieval. Compared to the 11 GHz channel, the 6 GHz channel provides more sensitivity to the low SSTs and offers lower instrument noise, thanks to possibly broader channel bandwidths. However, it requires much larger antenna size for a given spatial resolution. Two instrument concepts have been suggested, one using a classic real aperture antenna and the other using synthetic interferometric antennas. This first analysis shows that 2‐D interferometric systems would be very complex and would not satisfy the user requirements in terms of SST accuracy. A 1‐D interferometric system could be proposed, but its development requires additional investigation. A dedicated conical scanner onboard a microsatellite with a 6 m antenna and channels at 6.9 and 18.7 GHz (both with V and H polarizations) can provide an SST accuracy of 0.3 K with a 15 km spatial resolution, with today's technology.

In sea surface temperature (SST) retrieval from infra-red imagery using coefficient-based algorithms, a technique called atmospheric correction smoothing is sometimes used to reduce noise in the retrieved SST. It is shown that the sensitivity of retrieved SST to change in true SST is not significantly modified by atmospheric correction smoothing, correcting an earlier statement to the contrary.

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

One objective of the sea surface temperature (SST) component of the Global Monitoring for the Environment and Security (GMES) MERSEA project was to define the combined use of all SST products in an optimal interpolation scheme. Most SST products are now available in a common format but show regional differences between datasets that must be removed before use. We have defined a simple method using the SST derived from the Advanced Along Track Scanning Radiometer (AATSR) onboard the European Space Agency (ESA) ENVISAT platform as a common reference for all SST products. This method is based on an analysis of the differences between a given dataset and the AATSR SST over the 10 preceding days and over a 5° regular grid. These space–time characteristics have been defined empirically in a preliminary experiment. The efficiency of the method has been evaluated on a daily basis for a two year period (2007–2008) based on two SST products from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT): the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) derived SST over the Atlantic and the Advanced Very High Resolution Radiometer (AVHRR) SST derived at fine scale over the North-East Atlantic. They have been compared to the AATSR and buoy SSTs and to the Naval Oceanographic Office (NAVOCEANO) AVHRR global SST product. The SEVIRI SST bias, which was reduced from −0.40K to −0.02K for the 0–20N latitude band by this approach provides a good example of the performance of the method. Reductions in the bias are also demonstrated for the North Atlantic, where cloudiness related problems were expected.

High resolution surface wind fields covering the global ocean, estimated from remotely sensed wind data and ECMWF wind analyses, have been available since 2005 with a spatial resolution of 0.25° in longitude and latitude, and a temporal resolution of 6h. Their quality is investigated through various comparisons with surface wind vectors from 190 buoys moored in various oceanic basins, from research vessels and from QuikSCAT scatterometer data taken during 2005–2006. The NCEP/NCAR and NCDC blended wind products are also considered. The comparisons performed during January-December 2005 show that speeds and directions compare well to in-situ observations, including from moored buoys and ships, as well as to the remotely sensed data. The root-mean-squared differences of the wind speed and direction for the new blended wind data are lower than 2m/s and 30°, respectively. These values are similar to those estimated in the comparisons of hourly buoy measurements and QuikSCAT near real time retrievals. At global scale, it is found that the new products compare well with the wind speed and wind vector components observed by QuikSCAT. No significant dependencies on the QuikSCAT wind speed or on the oceanic region considered are evident.

Criteria are proposed for evaluating sea surface temperature (SST) retrieved from satellite infra‐red imagery: bias should be small on regional scales; sensitivity to atmospheric humidity should be small; and sensitivity of retrieved SST to surface temperature should be close to 1 K K−1. Their application is illustrated for non‐linear sea surface temperature (NLSST) estimates. 233929 observations from the Advanced Very High Resolution Radiometer (AVHRR) on Metop‐A are matched with in situ data and numerical weather prediction (NWP) fields. NLSST coefficients derived from these matches have regional biases from −0.5 to +0.3 K. Using radiative transfer modelling we find that a 10% increase in humidity alone can change the retrieved NLSST by between −0.5 K and +0.1 K. A 1 K increase in SST changes NLSST by <0.5 K in extreme cases. The validity of estimates of sensitivity by radiative transfer modelling is confirmed empirically.

We characterize near‐surface ocean diurnal warm‐layer events, using satellite observations and fields from numerical weather forecasting. The study covers April to September, 2006, over the area 11°W to 17°E and 35°N to 57°N, with 0.1° cells. We use hourly satellite SSTs from which peak amplitudes of diurnal cycles in SST (dSSTs) can be estimated with error ∼0.3 K. The diurnal excursions of SST observed are spatially and temporally coherent. The largest dSSTs exceed 6 K, affect 0.01% of the surface, and are seen in the Mediterranean, North and Irish Seas. There is an anti‐correlation between the magnitude and the horizontal length scale of dSST events. Events wherein dSST exceeds 4 K have length scales of ≤40 km. From the frequency distribution of different measures of wind‐speed minima, we infer that extreme dSST maxima arise where conditions of low wind speed are sustained from early morning to mid afternoon.

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 degrees in latitude and longitude. Throughout this study, BT observations are clear-sky averages over cells of size 0.5 degrees 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. (C) 2008 Elsevier Inc. All rights reserved.

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 Meteo-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 degrees-latitude by 30 degrees-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. (C) 2007 Elsevier Inc. All rights reserved.

A regional Sea Surface Temperature product covering the Mediterranean Sea and the western Atlantic Ocean at 1/16° spatial resolution has been developed and is operationally produced and distributed in nearreal time in the framework of the MFSTEP (Mediterranean Forecasting System Toward an Environmental Prediction) project. The MFS processing chain is described here. It includes several steps, from the cloud detection and removal, to the images compositing and merging of CNR-ISAC and CMS datasets. Night time maps of SST for each day are interpolated on the model domain through statistical methods and then assimilated in the MFS General Circulation Model. The data assimilation techniques and their impact on the modelling tasks are also briefly discussed.

Surface fluxes of momentum, freshwater, and energy across the air-sea interface determine oceanic circulation and its variability at all timescales. The goal of this paper is to estimate and examine some ocean surface flux variables using satellite measurements. The remotely sensed data come from the European Remote Sensing (ERS) satellite scatterometer on ERS-2, NASA scatterometer (NSCAT), and several Defense Meteorological Satellite Program (DMSP) radiometers [Special Sensor Microwave Imager (SSM/I)] on board the satellites F10-F14. The sea surface temperature comes from daily analysis calculated from Advanced Very High Resolution Radiometer (AVHRR) measurements. This study focuses on the 9-month period (October 1996-June 1997) of the NSCAT mission. To ensure high quality of the merged surface parameter fields, comparisons between different satellite estimates for the same variable have been performed, and bias corrections have been applied so that they are compatible with each other. The satellite flux fields are compared to in situ observations from buoys and ships globally and in different regions of the ocean. It is found that the root-mean-square (rms) difference with weekly averaged wind speeds is less than 2.5 m s(-1) and the correlation coefficient is higher than 0.8. For weekly latent heat flux, the rms difference between satellite and buoys does not exceed 30 W m(-2). The comparisons with weekly ship latent heat flux estimates gives an rms difference approaching 40 W m(-2). Comparisons are also made between satellite fields and atmospheric analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and reanalyses from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR). The wind speeds and latent heat fluxes from these atmospheric analyses compare reasonably well with the satellite estimates. The main discrepancies are found in regions and seasons of large air-sea temperature difference and high wind speed, such as the Gulf Stream during the winter season.

The setting up of an operational Mediterranean Forecasting System (MFS) will require both in-situ and space data. Remote sensing data, which are envisioned for the MFS, are satellite altimetry, sea surface temperature (SST) and ocean color. SST is also a fundamental ocean parameter that can be measured from space. It influences the ocean and atmosphere circulation. Heat fluxes also strongly depend on SST. SST measurements are, thus, crucial for understanding and predicting the ocean/atmosphere system. At small scales, SST measurements can provide useful information on mesoscale ocean flow field and the position of the main current systems and the regions of upwellings. The system is based on existing facilities and serves as a prototype for a future operational system. Specific objectives are (1) to develop algorithms and test them to process near real time (NRT) altimeter data; (2) to develop a NRT sea surface temperature product MFS model/assimilation scheme is a powerful means to integrate these remote sensing with in-situ data, forcing data and dynamic constraints.

Une nouvelle climatologie décadaire de la température de surface de la mer à échelle fine a été réalisée pour améliorer la détection des nuages sur la mer dans l'imagerie satellitaire. Elle a été construite à partir des données AVHRR de nuit provenant des satellites NOAA sur la période 1985-1995. Cette climatologie comporte des champs de température moyenne, de température minimale et de température maximale à environ 9 kilomètres derésolution. Des champs du nombre de cas utilisés pour les calculs statistiques, de présence de glace et d'écart type ont également été calculés pour déterminer la qualité obtenue en chaque point de grille. Une méthode d'interpolation optimale a été mise en oeuvre pour pallier le manque de données dans les zones de nébulosité persistante. La haute résolution permet de mettre en évidence des structures fines ou des phénomènes locaux, tels les upwellings côtiers. Le réalisme de cette nouvelle climatologie a été évalué par comparaison aux climatologies existantes et à des observations in situ.

This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First, a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated direction are given. At least 75% of the most probable wind directions are consistent with ECMWF winds (at ±20°). Then the remaining ambiguities are solved by an adapted PRESCAT method, which uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the accuracy of the method. Its good performance is mainly due to the use of a spatial context and to the probabilistic approach for estimating the direction. Comparisons with other methods are also presented. The good performance of the neural method suggests that self-consistent wind retrieval is possible.

Two main modes of climatic variability occur in the tropical Atlantic Ocean at inter-annual time-scales: the equatorial mode, similar to the El Nino phenomenon in the Pacific Ocean, and the meridional mode, or dipole-like mode, with no Pacific counterpart. The Atlantic equatorial mode is characterized by the occurrence of alternating warm and cold episodes at the equator, on the eastern side of the basin. These events are associated with abnormal variations in the zonal equatorial slope of the thermocline. The meridional mode is characterized by an inter-hemispheric gradient in the sea-surface temperature (SST). The mean position of the Inter-tropical Convergence Zone (ITCZ) separates positive and negative SST signals. It was recently shown with observational indices that there is significant correlation between these two climatic modes of variability. This study goes one step further, by using a multi-year numerical simulation, where an oceanic general circulation model is forced by the 1979-1993 ECMWF reanalysis. Model computed indices representing the two main modes of variability compare well with observations. The two inter-annual modes of variability are shown to have the same physics as the annual variability does, which is related to the latitudinal displacement of the ITCZ. Furthermore, it is suggested that the ocean dynamics (as opposed to thermodynamic processes) is the principal cause of climate variability in the region. Copyright (C) 2000 Royal Meteorological Society.