Assessing the effect of tandem phase Sentinel-3 OLCI Sensor uncertainty on the estimation of potential ocean chlorophyll-a trends

The Sentinel-3 tandem project represents the first time that two ocean colour satellites have been flown in the same orbit with minimal temporal separation (~30 s), thus allowing them to have virtually identical views of the ocean. This offers an opportunity for understanding how differences in individual sensor uncertainty can affect conclusions drawn from the data. Here, we specifically focus on trend estimation. Observational chlorophyll-a uncertainty is assessed from the Sentinel-3A Ocean and Land Colour Imager (OLCI-A) and Sentinel-3B OLCI (OLCI-B) sensors using a bootstrapping approach. Realistic trends are then imposed on a synthetic chlorophyll-a time series to understand how sensor uncertainty could affect potential long-term trends in Sentinel-3 OLCI data. We find that OLCI-A and OLCI-B both show very similar trends, with the OLCI-B trend estimates tending to have a slightly wider distribution, although not statistically different from the OLCI-A distribution. The spatial pattern of trend estimates is also assessed, showing that the probability distributions of trend estimates in OLCI-A and OLCI-B are most similar in open ocean regions, and least similar in coastal regions and at high northern latitudes. This analysis shows that the two sensors should provide consistent trends between the two satellites, provided future ageing is well quantified and mitigated. The Sentinel-3 programme offers a strong baseline for estimating long-term chlorophyll-a trends by offering a series of satellites (starting with Sentinel-3A and Sentinel-3B) that use the same sensor design, reducing potential issues with cross-calibration between sensors. This analysis contributes an important understanding of the reliability of the two current Sentinel-3 OLCI sensors for future studies of climate change driven chlorophyll-a trends

Benefits and lessons learned from the Sentinel-3 tandem phase

During its commissioning phase, the Copernicus Sentinel-3B satellite has been placed in a tandem formation with Sentinel-3A for a period of 6 months. This configuration allowed a direct comparison of measurements obtained by the two satellites. The purpose of this paper was to present the range of analyses that can be performed from this dataset, highlighting methodology aspects and the main outcomes for each instrument. We examined, in turn, the benefit of the tandem in understanding instrument operational modes differences, in assessing inter-satellite differences, and in validating measurement uncertainties. The results highlighted the very good consistency of the Sentinel-3A and B instruments, ensuring the complete inter-operability of the constellation. Tandem comparisons also pave the way for further improvements through harmonization of the sensors (OLCI), correction of internal stray-light sources (SLSTR), or high-frequency processing of SRAL SARM data. This paper provided a comprehensive overview of the main results obtained, as well as insights into some of the results. Finally, we drew the main lessons learned from the Sentinel-3 tandem phase and provided recommendations for future missions

The Relative Humidity in an Isentropic Advection–Condensation Model: Limited Poleward Influence and Properties of Subtropical Minima

An idealized model of advection and condensation of water vapor is considered as a representation of processes influencing the humidity distribution along isentropic surfaces in the free troposphere. Results are presented for how the mean relative humidity distribution varies in response to changes in the distribution of saturation specific humidity and in the amplitude of a tropical moisture source. Changes in the tropical moisture source are found to have little effect on the relative humidity poleward of the subtropical minima, suggesting a lack of poleward influence despite much greater water vapor concentrations at lower latitudes. The subtropical minima in relative humidity are found to be located just equatorward of the inflection points of the saturation specific humidity profile along the isentropic surface. The degree of mean subsaturation is found to vary with the magnitude of the meridional gradient of saturation specific humidity when other parameters are held fixed. The atmospheric relevance of these results is investigated by comparison with the positions of the relative humidity minima in reanalysis data and by examining poleward influence of relative humidity in simulations with an idealized general circulation model. It is suggested that the limited poleward influence of relative humidity may constrain the propagation of errors in simulated humidity fields

The SURPRISE demonstrator: a super-resolved compressive instrument in the visible and medium infrared for Earth Observation

While Earth Observation (EO) data has become ever more vital to understanding the planet and addressing societal challenges, applications are still limited by revisit time and spatial resolution. Though low Earth orbit missions can achieve resolutions better than 100 m, their revisit time typically stands at several days, limiting capacity to monitor dynamic events. Geostationary (GEO) missions instead typically provide data on an hour-basis but with spatial resolution limited to 1 km, which is insufficient to understand local phenomena. In this paper, we present the SURPRISE project - recently funded in the frame of the H2020 programme – that gathers the expertise from eight partners across Europe to implement a demonstrator of a super-spectral EO payload - working in the visible (VIS) - Near Infrared (NIR) and in the Medium InfraRed (MIR) and conceived to operate from GEO platform -with enhanced performance in terms of at-ground spatial resolution, and featuring innovative on-board data processing and encryption functionalities. This goal will be achieved by using Compressive Sensing (CS) technology implemented via Spatial Light Modulators (SLM). SLM-based CS technology will be used to devise a super-resolution configuration that will be exploited to increase the at-ground spatial resolution of the payload, without increasing the number of detector’s sensing elements at the image plane. The CS approach will offer further advantages for handling large amounts of data, as is the case of superspectral payloads with wide spectral and spatial coverage. It will enable fast on-board processing of acquired data for information extraction, as well as native data encryption on top of native compression. SURPRISE develops two disruptive technologies: Compressive Sensing (CS) and Spatial Light Modulator (SLM). CS optimises data acquisition (e.g. reduced storage and transmission bandwidth requirements) and enables novel onboard processing and encryption functionalities. SLM here implements the CS paradigm and achieves a super-resolution architecture. SLM technology, at the core of the CS architecture, is addressed by: reworking and testing off-the-shelf parts in relevant environment; developing roadmap for a European SLM, micromirror array-type, with electronics suitable for space qualification. By introducing for the first time the concept of a payload with medium spatial resolution (few hundreds of meters) and near continuous revisit (hourly), SURPRISE can lead to a EO major breakthrough and complement existing operational services. CS will address the challenge of large data collection, whilst onboard processing will improve timeliness, shortening time needed to extract information from images and possibly generate alarms. Impact is relevant to industrial competitiveness, with potential for market penetration of the demonstrator and its components

Relations entre cirrus et humidité dans la haute troposphère à partir du sondage infrarouge et de sa synergie avec d'autres observations. Application à l'impact du trafic aérien sur le climat.

L'augmentation considérable du trafic aérien depuis quelques décennies a motivé des recherches sur l'impact des traînées de condensation sur le climat. Lorsque celles-ci persistent, elles induisent une couverture nuageuse additionnelle en cirrus dont l'impact sur le climat est difficile à quantifier. La condition physique de la persistance des trainées de condensation est la sursaturation en glace (humidité relative supérieure à la saturation), phénomène assez fréquent dans la haute troposphère et condition nécessaire pour la formation des cirrus. Dans ce contexte, les sondeurs infrarouges ont l'avantage de fournir, de manière globale et continue, des mesures conjointes de propriétés nuageuses et de profils atmosphériques de température et de vapeur d'eau. Nous utilisons dans cette étude les bases de données climatiques TIROS-N Observation Vertical Sounder (TOVS) puis Atmospheric InfraRed Sounder (AIRS). Ces deux instruments n'ont pas une résolution verticale suffisante pour détecter la présence de fines couches de sursaturation en glace. Des synergies avec d'autres types observations permettent de le mettre en évidence et de le corriger. Nous démontrons notamment à l'aide du lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) que l'humidité relative de la haute troposphère dans les cirrus, déterminée par AIRS, montre un biais négatif en comparaison de mesures in situ. Ce biais est lié à la résolution verticale de AIRS et il est d'autant plus prononcé que les nuages sont géométriquement fins. Il est difficile de détecter la sursaturation car cette dernière est plus à même d'apparaître sur de fines épaisseurs. Une méthode de correction est alors proposée pour mieux estimer les fréquences d'occurrence de la sursaturation en glace à l'échelle globale. La synergie entre AIRS et des mesures aéroportées de la campagne MOZAIC (Measurement of OZone and water vapour by AIrbus in-service airCraft) nous permet de construire une probabilité d'occurrence de la sursaturation en fonction de l'humidité relative déterminée par AIRS, quelque soit la valeur de cette humidité relative. Cette probabilité permet de construire des climatologies de la sursaturation en glace. Enfin, son couplage avec la détermination conjointe des propriétés nuageuses permet d'établir des climatologies des situations atmosphériques favorables à la persistance et à l'impact sur le climat des traînées de condensation

Evaluation of upper tropospheric humidity forecasts from ECMWF using AIRS and CALIPSO data

An evaluation of the upper tropospheric humidity from the European Centre of Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) is presented. We first make an analysis of the spinup behaviour of ice supersaturation in weather forecasts. It shows that a spinup period of at least 12 h is necessary before using forecast humidity data from the upper troposphere. We compare the forecasted upper tropospheric humidity with coincident relative humidity fields retrieved from the Atmospheric InfraRed Sounder (AIRS) and with cloud vertical profiles from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The analysis is made over one year, from October 2006 to September 2007, and we discuss how relative humidity and cloud features appear both in the IFS and in the observations. The comparison with AIRS is made difficult because of the vertical resolution of the sounder and the impossibility to retrieve humidity for high cloudiness. Clear sky relative humidities show a rather good correlation whereas cloudy situations reflect more the effect of a dry bias for AIRS increasing with the relative humidity. The comparison with CALIPSO shows that the IFS predicts high relative humidity where CALIPSO finds high clouds, which supports the good quality of the ECMWF upper tropospheric cloud forecast. In a last part, we investigate the presence of ice supersaturation within low vertical resolution pressure layers by comparing the IFS outputs for highresolution and low-resolution humidity profiles and by simulating the interpolation of humidity over radiosonde data. A new correction method is proposed and tested with these data

Sentinel-2 L1C Radiometric Validation Using Deep Convective Clouds Observations

In the frame of the ESA Scientific Exploitation of Operational Missions project, ACRI-ST is responsible for the development and the intercomparison of new algorithms to validate the Sentinel 2 L1C product radiometry, beyond the baseline algorithms used operationally in the frame of the S2 Mission Performance Centre. In this context ACRI-ST is in charge of the definition and implementation of a validation approach based on the exploitation of deep convective cloud (DCC) observations. Due to their physical properties, DCCs appear from the remote sensing point of view to have bright tops and white behavior; they can be used as invariant targets to monitor the radiometric response degradation of reflective solar bands. The observation of such targets allows an interband radiometry validation in the VIS-NIR domain (MSI bands between 443 and 865 nm) from a reference band considered as correctly calibrated. We first present the DCC data selection criteria appropriate for the radiometric validation of the Sentinel-2 MSI instrument. The validation methodology is then thoroughly described and justified. It is based on the simulation of DCC top-of-atmosphere reflectance using radiative transfer modeling and its comparison to the actual MSI measurements to assess systematic interband biases. Final results and uncertainties are computed through the statistical analysis of a large collection of individual observations with a view to provide consolidated interband radiometric gains for MSI. These show the very good radiometric performance of MSI with interband gains much lower than 2%

Upper tropospheric humidity and cirrus geometrical and optical thickness: Relationships inferred from 1 year of collocated AIRS and CALIPSO data

International audienceProfiles of relative humidity with respect to ice (RHice) determined from spaceborne passive remote sensing suffer a lack of vertical and spatial resolutions. RHice distributions show dry biases compared to in situ observations because geometrically thin moist layers are integrated within coarser vertical resolutions, a direct effect being the underestimation of ice supersaturation (RHice > 100%) occurrence. Collocated data from the Atmospheric Infrared Sounder (AIRS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) provide the opportunity to investigate relationships between RHice and geometrical thickness and optical depth of high clouds near the tropopause. “Apparent” optical depths are derived for single-layer high clouds from CALIPSO. By comparing these “apparent” optical depths to cloud infrared emissivities derived from AIRS the multiple scattering contribution is estimated and the optical depths are corrected. Mean RHice increases with cloud geometrical thickness but remains low compared to 100% except for very vertically extended clouds. Optically thicker clouds show on average larger geometrical thickness and larger relative humidity than optically thinner clouds. However, for a comparable geometrical thickness, optically thinner clouds are on average slightly more humid. This study concludes that cloud geometrical thickness has a greater influence than cloud optical depth on RHice integrated within a coarse vertical resolution. Limitations of AIRS humidity observations regarding the detection of ice supersaturation are discussed

Deep Convective Clouds for Sentinel-3 OLCI Cross-Calibration Monitoring

Few weeks after its launch in April 2018, Sentinel-3B of the European Space Agency has been put in a tandem phase with its twin Sentinel-3A already in orbit. Both platforms were on the same track with the same geometrical conditions to gather acquisitions over the same targets only thirty seconds apart. This tandem phase lasted from early June to mid October 2018 to provide a unique opportunity for each S-3 sensors to increase knowledge of payload differences, reduce uncertainties when comparing data and to homogenise differences by defining appropriate adjustments. The inter-unit consistency is critical for the mission. The outcome of the tandem phase analysis provides a strong reference for assessing other cross-calibration methodologies, one of those being based on the use of Deep Convective Clouds (DCCs). Whereas a physical model of DCC reflectance must be provided to compare Ocean and Land Colour Instrument (OLCI) measurements with an absolute reference, DCC observations are rather used for their whiteness, brightness, and large spatial extent, for interband monitoring. In this presentation, we present and validate a DCC-based radiometric validation methodology adapted to OLCI with a specific emphasis on its ability to accurately monitor the cross-calibration of the independent sensors. We base the analysis on a careful analysis of the OLCI DCC reflectance measurements with a sensitivity assessment of the data selection employed (use of SLSTR synergetic brightness temperature or reflectance in absorption bands, analysis and handling of saturated pixels) as well as a cautious analysis of the FOV-dependency of the results. Performance is assessed by comparisons with the cross-calibration reference of the tandem analysis, in and out of the tandem phase acquisition period. The methodology covers the complete OLCI spectrum (to the exception of absorption bands) with precision less than about 1%