Seminario (recordatorio): The bluefin tuna project: Linking environmental variability and early life dynamics for the sustainability of tuna species in the Mediterranean
Start: 18/10/2018 12:00 - End: 18/10/2018 13:00Place: Sala de Seminarios, IMEDEA
The bluefin tuna project: Linking environmental variability and early life dynamics for the sustainability of tuna species in the Mediterranean
Speaker: Diego Alvarez Berastegui, SOCIB
Summary: The Bluefin Tuna project is a joint research initiative involving institutions with expertise in ecology of tuna species and environmental variability (SOCIB, IEO, IMEDEA, NOAA). The main objectives of the Project are twofold, first, to understand the inter-annual variability of Bluefin Tuna spawning and larval habitats, and second, the application of operational oceanography to the conservation and management of tuna species in the Balearic Sea. The main activities have focused on the study of the relations between mesoscale oceanography in the Balearic Sea and the early life stages of bluefin tuna. From this knowledge we develop predictive models of spawning and larval habitats using the different SOCIB observing systems. This information is then applied to improve direct assessment of the westerns stock of bluefin tuna in the framework of ICCAT, developing various indices providing information about relevant parameters such the spawning stock biomass or the inter-annual larval survival potential.
Abstract: Interpolating spatio-temporal dynamics given noisy observations with missing data is a key issue in environmental sciences. Among others, sea surface geophysical parameters are important drivers of oceanic and atmospheric circulation and the possibility of reconstructing high resolution fields is a challenge influencing several applications such as tropical rainfalls forecasting and short term climate change understanding.
Data assimilation based techniques are still the state-of-the-art approaches in the reconstruction of spatio-temporal geophysical fields. These methods heavily rely on an explicitly given dynamical model to compute several forward simulations. While model-driven representations are widely used to characterize the hidden dynamics present in our data the increasing availability of remote sensing observations and simulation datasets motivated the development of accurate and efficient data-driven models. Analog methods for instance are one of the first data-driven techniques to benefit from this deluge of observations and several techniques were developed to plug such data driven analog forecasting operators in a data assimilation scheme.
Initially introduced to solve classification and segmentation tasks, neural networks have shown very promising results in regression and identification problems. However, these powerful tools are barely used in the context of geophysical modelisation where deep non-linear characterization can be crucial to capture dependencies in spatio-temporal fields.
In our work, we investigate deep learning models to infer data-driven data assimilation dynamical priors using data. As a case-study, we consider satellite-derived Sea Surface Temperature time series off South Africa, which involves intense and complex upper ocean dynamics.
16/10/2018 Seminar: The bluefin tuna project: Linking environmental variability and early life dynamics for the sustainability of tuna species in the Mediterranean
08/10/2018 Seminar: The Baltic Sea as time machine for the global coastal ocean
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