Assimilation of drifter data in the East Mediterranean Sea

Participants : Julien Brajard, Isabelle Herlin, Leila Issa [Lebanese American University, Lebanon] , Laurent Mortier [LOCEAN] , Daniel Hayes [Oceanography Centre, Cyprus] , Milad Fakhri [CNRS, Lebanon] , Pierre-Marie Poulain [Oceanography Institute of Trieste, Italy] .

Surface velocity fields of the ocean in the Eastern Levantine Mediterranean are estimated by blending altimetry and surface drifters data. The method is based on a variational assimilation approach for which the velocity is corrected by matching real drifters positions with those predicted by a simple advection model, while taking into account the wind effect. The velocity correction is done in a time-continuous fashion by assimilating at once a whole trajectory of drifters with a temporal sliding window. Except for the wind component, a divergence-free regularization term was added to constrain the velocity field. Results show that, with few drifters, our method improves the estimated velocity in two typical situations: an eddy between the Lebanese coast and Cyprus, and velocities along the Lebanese coast. A description of these results is published in the Ocean Modelling journal.

State estimation for noise pollution

Participants : Raphaël Ventura, Vivien Mallet, Valérie Issarny [Mimove] , Pierre-Guillaume Raverdy [SED] , Fadwa Rebhi [Mimove] , Cong Kinh Nguyen [Mimove] .

70 million observations of ambient noise have been collected with the mobile application Ambiciti (previously, SoundCity). An important work was carried out on the calibration of the measurements. Over 100 mobile phones were calibrated against a sound level meter, at various noise intensities and frequencies, in order to test their response and devise a calibration strategy.

A data assimilation procedure has been put in place in order to select and assimilate the most reliable observations. Simulated noise maps have been improved with the observations, by computing the so-called best linear unbiased estimator (BLUE) with error covariance models suitable for noise pollution. The assimilation of mobile observation introduces new errors, like location errors, compared to the assimilation of the more common observations from fixed monitoring stations.