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Publications (215)
Twelve climate models and observations are used to attribute the global mean surface temperature (GMST) changes from 1900 to 2014 to external climate forcings. The external forcings are decomposed into the effects of the well-mixed greenhouse gas concentration variation, the effects of anthropogenic aerosol concentration changes, and the effects of...
The oceans have a very important role in climate regulation due to their massive heat storage capacity. Thus, for the past decades, oceans have been observed by satellites to better understand their dynamics. Satellites retrieve several data with various spatial resolutions. For instance, sea surface height (SSH) is a low-resolution data field wher...
The oceans have a very important role in climate regulation due to its massive heat storage capacity. Thus, for the past decades oceans have been observed by satellites in order to better understand its dynamics. Satellites retrieve several data with various spatial resolution. For instance Sea Surface Height (SSH) is a low-resolution data field wh...
Observing the vertical dynamic of phytoplankton in the water column is essential to understand the evolution of the ocean primary productivity under climate change and the efficiency of the CO2 biological pump. This is usually made through in-situ measurements. In this paper, we propose a machine learning methodology to infer the vertical distribut...
We revisited the partitioning of the Mediterranean Sea into bioregions by processing satellite Sea Surface Temperature (SST) and Chlorophyll‐a concentration (Chla) from ocean color observations combined with Argo mixed‐layer depth for a period ranging from 2003 up to 2020. This regionalization was performed using an innovative classification based...
The western tropical Atlantic Ocean is a very energetic and highly variable region. It is one of the main contributors to the inter-hemispheric mass and heat transports. This study aim is to give a new picture of the space and time variability of this region using statistical tools applied to five different satellite measurements (Sea Surface Tempe...
We present a partitioning of the Mediterranean Sea into regions having well defined characteristics with respect to sea-surface temperature and surface chlorophyll-a concentration observed by satellite and by the Argo mixed-layer depth. This regionalization was performed using an innovative classification based on neural networks, the so-called 2S-...
The capacity to monitor suspended sediment concentrations (SSC) in the ocean, from surface to bottom, using data acquired by the future MTG/FCI satellite sensor has been quantified by Observing System Simulation Experiments (OSSE). The "true" ocean state for these experiments is based on a 15 months numerical simulation of hydrodynamic and sediment...
Climate simulations require very complex numerical models.
Unfortunately, they typically present biases due to parameterizations,
choices of numerical schemes, and the complexity of many physical processes.
Beyond improving the models themselves, a way to improve the performance of
the modeled climate is to consider multi-model combinations. In the...
We processed daily ocean-color satellite observations to construct a monthly
climatology of phytoplankton pigment concentrations in the Senegalo–Mauritanian region. Our proposed new method primarily consists of associating, in well-identified clusters, similar pixels in terms of
ocean-color parameters and in situ pigment concentrations taken from a...
In a warming world context, Sea Surface Temperature (SST) off central-south Peru, northern Chile and further offshore increases at a slower rate than the global average since several decades, i.e. cools, relative to the global average. This tendency is synchronous with an Interdecadal Pacific Oscillation (IPO) negative trend since ∼1980, which has...
Total column water vapor is an important factor for the weather and climate. This study apply deep learning based multiple regression to map the TCWV with elements that can improve spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the fifth generation ECMWF atmospheric reanalysis of the global climate. We us...
In this work, we study spatial and temporal atmospherics parameters evolution retrieved by neuro-variationnal method from SeaWiFS observations measured off the west African coast. The SeaWiFS sensor measures the radiance above the top of atmosphere (TOA) solar irradiance. SeaWiFS use standard algorithm to invert the signal in order to retrieve weak...
Total column water vapor is an important factor for the weather and climate. This study apply deep learning based multiple regression to map the TCWV with elements that can improve spatiotemporal prediction. In this study, we predict the TCWV with the use of ERA5 that is the fifth generation ECMWF atmospheric reanalysis of the global climate. We us...
Abstract. Climate simulations require very complex numerical models. Unfortunately, they typically present biases due to parameterizations, choices of numerical schemes, and the complexity of many physical processes. Beyond improving the models themselves, a way to improve the performance of the modeled climate is to consider multi-model averages....
We present a new method to identify phytoplankton functional types (PFTs) in the Mediterranean Sea from ocean color data (GlobColour data in the present study) and AVHRR sea surface temperature. The principle of the method is constituted by two very fine clustering algorithms, one mapping the relationship between the satellite data and the pigments...
We processed daily ocean-color satellite observations to construct a monthly climatology of phytoplankton pigment concentrations in the Senegalo-Mauritanian region. Thanks to the difficulty of the problem, we proposed a new method. It primarily consists in associating, in well-identified clusters, similar pixels in terms of ocean-color parameters a...
This study presents a method for estimating secondary phytoplankton pigments from satellite ocean colour observations. We first compiled a large training data set composed of 12000 samples; each sample is composed of ten in‐situ phytoplankton HPLC measured pigment concentrations, GlobColour products of Chlorophyll‐a concentration (Chla) and remote...
The present study deals with the merging of ocean colour satellite data with hydrodynamic simulations of waves and currents, in order to retrieve Suspended Particulate Inorganic Matter (SPIM) concentrations in the water column. The merging method involves time series analysis and combines 1D-vertical spatial clustering and temporal pattern learning...
Massive coral skeleton offers the bestsuited material for reconstruction of tropical climate during the last century. Indeed, growth rate of some species is fast enough to provide high resolution (monthly) sampling. The formation of a big colony may cover continuously several decades, even, several centuries. Chronology is made easy by annual gro...
It is commonly admitted that chemical measurements of coral skeleton provide the best climate archives of tropical zone. However, studies earlier published failed to quantitatively reconstruct climate conditions of the last centuries. All the biases introduced by using an empirical SST calibration based on the classical isotopic thermometer have be...
Résumé : Dans cette étude, l'assimilation variationnelle a été implémentée avec le modèle hydrologique HBV en utilisant la plateforme YAO de l'Université Pierre et Marie Curie (France). Le principe de l'assimilation variationnelle consiste à considérer les variables d'état du modèle comme des variables de contrôle et à les optimiser en minimisant u...
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SO...
Hydro-sedimentary models have been widely used for deriving suspended particulate matter concentrations from the coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used computationally cheap, well-established methodology combining self-organizing maps (SOM) with hidden...
The SECHIBA module of the ORCHIDEE land surface model describes the exchanges
of water and energy between the surface and the atmosphere. In the present
paper, the adjoint semi-generator software called YAO was used as a framework
to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The
objective was to deliver the adjoint model of...
Aerosol optical thickness (AOT) was provided by SeaWiFS over oceans from October 1997 to December 2010. Weekly, monthly, and annually maps might help scientifics to better understand climate change and its impacts. Making average of several images to get these maps is not suitable on West African coast. A particularity of this area is that it is co...
The Sicily Channel surface circulation is investigated by analyzing the outputs of a high-resolution ocean model MED12 forced during 46 years by the ARPERA atmospheric fields. Applying a neural network classifier, we show that the surface circulation in the Sicily Channel can be decomposed into 8 modes characterizing the major patterns of that circ...
The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software denoted YAO was used as a framework to implement a 4D-VAR assimilation method. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obt...
In the present study we propose the fusion of satellite ocean colour data with hydrodynamical models simulations, to retrieve the suspended particulate inorganic matter concentration (SPIM) in the water column.
We present a novel approach named ITCOMP SOM that uses iterative self-organizing maps (SOM) to progressively reconstruct missing data in a highly correlated multidimensional dataset. This method was applied for the completion of a complex oceanographic data-set containing glider data from the EYE of the Levantine experiment of the EGO project. ITCO...
We have investigated the phytoplankton dynamics of the Senegalo-Mauritanian upwelling region, which is a very productive region, by processing a 13 year set of SeaWiFS satellite ocean-color measurements using a PHYSAT-like method. We clustered the spectra of the ocean-color normalized reflectance (reflectance normalized by a reflectance dependent o...
To simulate the behavior of the ocean several numerical models have been developed such as NEMO [1]. The dynamical component of NEMO is based on a variant of Navier-Stockes equations with some hypothesis related to large-scale ocean circulation. This paper proposes a variational approach for controlling the initial conditions of the dynamical compo...
We present a statistical method, denoted PROFHMM, to infer the evolution of the vertical profiles of oceanic biogeophysical variables from sea-surface data. This method makes use of discrete Hidden Markov Models whose states are defined through Self-Organizing Topological Maps. The Self-Organizing Topological Maps are used to provide the states of...
A neural network model is proposed for reconstructing ocean salinity profiles from sea surface parameters only. The method is applied to the tropical Atlantic. Prior data mining on a complete dataset shows that latitude and sea surface salinity are the most relevant surface parameters in the prediction of salinity profiles. A classification using a...
Nous proposons une méthode de sélection de variables en classification basée sur les cartes topologiques auto-organisées SOM. Elle utilise la méthode de subspace clustering 2S-SOM dans un processus hiérarchique à deux niveaux. Le premier niveau fournit un système de poids évaluant les contributions relatives des variables et des blocs aux groupes d...
Spectral scaling properties have already been evidenced on oceanic numerical simulations and have been subject to several interpretations. They can be used to evaluate classical turbulence theories that predict scaling with specific exponents and to evaluate the quality of GCM outputs from a statistical and multiscale point of view. However, a more...
In this study data assimilation based on variational assimilation was implemented with the HBV
hydrological model using the YAO platform of University Pierre and Marie Curie (France). The principle of
the variational assimilation is to consider the model state variables as control variables and optimise them by
minimizing a cost function measuring...
Variational data assimilation of FLUXNET soil surface temperature is applied to the energy and water budgets
modules of the ORCHIDEE land surface model. This part of the model, called SECHIBA, describes the exchanges
of water and energy between the surface and the atmosphere. The adjoint semi-generator software YAO is used as
a framework to impleme...
This work aims at assessing the capability of passive remote-sensed measurements such as aerosol optical depth (AOD) to monitor the surface dust concentration during the dry season in the Sahel region (West Africa). We processed continuous measurements of AODs and surface concentrations for the period (2006–2010) in Banizoumbou (Niger) and Cinzana...
A land surface model (LSM) is a numerical model describing the exchange of water and energy between the land surface and the atmosphere. Land surface physics includes an extensive collection of complex processes. The balance between model complexity and resolution, subject to computational limitations, represents a fundamental query in the developm...
We present a method able to fill large data gaps in satellite chlorophyll (CHL) images, based on the principle of Self-Organizing Maps (SOM) classification methods. The method makes use of complementary oceanic remote sensing observations: sea surface temperature (SST) and sea surface height (SSH). It relies on the assumption that a state of the oc...
We present a sensitivity analysis performed on the one dimension version
of the land surface model SECHIBA. This model was developed at IPSL
institute in France. To perform this sensitivity analysis, the adjoint
of the SECHIBA model was developed using a software named YAO, developed
at LOCEAN/IPSL laboratory in France. YAO facilitates the implemen...
Radiometers on board satellite measure the solar radiation reflected by
both ocean and atmosphere at several wavelengths. One difficulty is that
the signal is strongly polluted by the contribution of the atmosphere.
An important step in the processing of ocean colour images is the
so-called "atmospheric correction" that consists in removing the
con...
The YAO software (http://www.locean-ipsl.upmc.fr/~yao/) was used for
variational data assimilation. This software represents the numerical
model through a modular graph formalism. Each modulus represents a
function and its jacobian. Connecting all the modules constitutes the
graph. Running the graph forward represents the direct model; running it
b...
The "Sahelian belt" is known as a region where mineral dust content is
among the highest in the world. In the framework of the AMMA
international Program, a transect of 3 ground based stations, the
"Sahelian Dust Transect", has been deployed in order to obtain
quantitative information on the mineral dust content over the Sahel. The
three stations :...
Variational data assimilation consists in estimating key control parameters of a numerical model in order to minimize the misfit between the model values and the actual observations. The YAO framework is a code generator based on a modular graph decomposition of the model; it is dedicated for helping data assimilation experiment achievement. YAO is...
A method of retrieving PM10 particles concentrations at the ground level from AOT (Aerosol Optical Thickness) measurements is presented. It uses data obtained among five years during 2003 to 2007 summers in the Lille region (northern France). As PM10 concentration strongly depends on meteorological variables, we clustered the meteorologi- cal situa...
One of the difficulties in analyzing the ocean signal provided by satellite ocean color sensors is that it is strongly polluted by atmospheric contributions, which should be removed by an atmospheric correction process.We propose a new methodology, based on spectral optimization in the near-infrared, to simultaneously estimate the contributions gen...
On présente ici une méthode globale d'inversion des données de la tomographie acoustique en eau peu profonde. Le but est de retrouver le profil vertical de la célérité du sonàson`sonà partir de la mesure de la pression acoustique dans une antenne verticale d'hydrophones. Notre méthode est basée sur une approche variationnelle de l'inversion et un p...
Variational data assimilation consists in estimating control parameters of a numerical model in order to minimize the misfit between the forecast values and the actual observations. The YAO framework is a code generator that facilitates, especially for the adjoint model, the writing and the generation of a variational data assimilation program for...
Nous présentons une nouvelle méthodologie qui permet la restitution de l'épaisseur optique des aérosols et concentration de la chlorophylle-a sur treize années d'observations SeaWiFS (1997-2009) au large des côtes de l'Afrique de l'Ouest. Une particularité de cette zone est qu'elle est fréquemment traversée par des poussières désertiques, mais engl...
We present a method for retrieving atmospheric particulate matter (PM10) from sun-sky photometer measurements (AOT). As PM10 is a “surface parameter” and AOT is an “integrated parameters”, we first determined whether a “functional relationship” linking these two quantities exists. Since these two parameters strongly depend on atmospheric structures...
Variational data assimilationSatellite and situ dataImprovement of the biogeochemical marine model PISCES
Phytoplankton patchiness has been investigated with multifractal analysis techniques. We analyzed oceanic chlorophyll maps, measured by the SeaWiFS orbiting sensor, which are considered to be good proxies for phytoplankton. Multifractal properties are observed, from the sub-mesoscale up to the mesoscale, and are found to be consistent with the Cors...
