Julien Brajard

Inferring the seasonal evolution of phytoplankton groups in the Senegalo-Mauritanian upwelling region from satellite ocean-color spectral measurements

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 on chlorophyll-a concentration only) into 10 significant spectral classes using a Self-Organized Map (SOM) associated with a hierarchical ascendant classification (HAC). By analyzing a 13 year climatology of these classes, we have been able to outline a coherent scenario describing the Senegalo-Mauritanian upwelling region in terms of spatiotemporal variability of phytoplankton groups: during the onset of the upwelling (December–February), we mainly observed nanoeukaryote-type phytoplankton in the coastal area; in April–May, the period corresponding to the maximum chlorophyll-a concentration, the nanoeukaryote types were replaced by diatom types. This scenario is in agreement with microscope phytoplankton cell observations done during several past cruises.

Validation of model simulations with respect to in situ observations by the use of probabilistic estimations

The present work addresses the problem of validation of a synthetic dataset with respect to observations. It gives an index that determines locally how much a region of the synthetic dataset fits the observations. The method uses an estimation of the probability density function computed with the probabilistic self-organizing maps. Then, an index F was defined to quantify the areas of the synthetic datasets that correspond to the observations. The method was first applied to a ldquotoyrdquo example in 2 dimensions to see its potentiality and then applied to two real datasets of optics measurements of the surface ocean. The method allowed to characterize some simulations that have not been encountered during ship campaigns.

Atmospheric correction and oceanic constituents retrieval, with a neuro-variational method

Ocean color sensors on board satellite measure the solar radiation reflected by the ocean and the atmosphere. This information, denoted reflectance, is affected for 90% by air molecules and aerosols in the atmosphere and for only 10% by water molecules and phytoplankton cells in the ocean. Our method focuses on the chlorophyll-a concentration (chl-a) retrieval, which is commonly used as a proxy for phytoplankton concentration. Our algorithm, denoted NeuroVaria, computes relevant atmospheric (Angstrom coefficient, optical thickness, single-scattering albedo) and oceanic parameters (chl-a, oceanic particulate scattering) by minimizing the difference over the whole spectrum (visible + near infrared) between the observed reflectance and the reflectance computed from artificial neural networks that have been learned with a radiative transfer model. NeuroVaria has been applied to SeaWiFS (sea-viewing wide field-of-view sensor) imagery in the Mediterranean sea. A comparison with in-situ measurements of the water-leaving reflectance shows that NeuroVaria enables to better reconstruct this component at 443 nm than the standard SeaWiFS processing. This leads to an improvement of the retrieval of the chl-a for the oligotrophic sea. This result is generalized to the entire Mediterranean sea through weekly maps of chl-a.

Estimation of Round-off Errors in OpenMP Codes

It is crucial to control round-off error propagation in numerical simulations, because they can significantly affect computed results, especially in parallel codes like OpenMP ones. In this paper, we present a new version of the CADNA library that enables the numerical validation of OpenMP codes. With a reasonable cost in terms of execution time, it enables one to estimate which digits in computed results are affected by round-off errors and to detect numerical instabilities that may occur during the execution. The interest of this new OpenMP-enabled CADNA version is shown on various applications, along with performance results on multi-core and many-core (Intel Xeon Phi) architectures.

Spatial Distribution of Aerosol Optical Thickness Retrieved from SeaWiFS Images by a Neural Network Inversion over the West African Coast

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 constantly traversed by desert dust. The algorithm used by SeaWiFS inverts the reflectance measurements to retrieve the aerosol optical thickness at 865 nm. For the poorly absorbing aerosol optical thickness less than 0.35, the standard algorithm works very well. On the west African coast that is often crossed by desert aerosol plumes characterized by high optical thicknesses. In this paper we study the spatial and temporal variability of aerosols on the West African coast during the period from December 1997 to November 2009 by using neural network inversion. The neural network method we used is mixed method of neuro-variational inversion called SOM-NV. It is an evolution of NeuroVaria that is a combination of a variational inversion and multilayer perceptrons, multilayer perceptrons (MLPs). This work also enables validation of the optical thickness retrieved by SOM-NV with AOT in situ measurements collected at AErosol RObotic NETwork (AERONET) stations.

Analysis of the Senegalo-Mauritanian upwelling by processing satellite remote sensing observations with topological maps.

The Senegalo-Mauritanian upwelling is a very productive upwelling occurring along the West coast of Africa. Its seasonal and inter-annual variability south of 20°N was analyzed by processing ocean color data and sea surface temperature provided by satellite sensors. We used a classification methodology consisting in a neural network topological map and a hierarchical ascendant classification. Four classes can explain most of the variability of the upwelling. Its extent is maximum in February-March, minimum in August September. The variability is linked to that of the wind. The classes can be considered as statistical indices allowing us to investigate the variability of the upwelling.