Entry Date:
January 25, 2017

Hazards SEES: Advanced Lagrangian Methods for Prediction, Mitigation and Response to Environmental Flow Hazards

Principal Investigator Thomas Peacock

Co-investigator Pierre Lermusiaux

Project Start Date September 2015

Project End Date
 August 2019

Environmental flow disasters occur when hazardous material is released and dispersed into the environment by the natural processes of air and water. Recent catastrophic examples include: the spread of oil during the Deep Water Horizon disaster, the passage of the ash cloud from the Eyjafjallajokull volcano through commercial air space, and the trail of radioactive waste from the Fukushima reactor disaster. These types of hazards are common and many have profound impacts on society. When hazardous material is released, accurate predictions of where the material is likely to go can greatly improve emergency response and significantly reduce negative consequences. Preparedness and effective response can save many lives, untold environmental damage and enormous financial cost. However, predicting where materials go in complex environmental flows remains a formidable scientific challenge.

This project intends to transform science's environmental flow predictive capabilities by exploiting and advancing recent fundamental breakthroughs in four-dimensional (3D+time) Lagrangian methods. This research will integrate theoretical, computational, and observational approaches to develop and utilize cutting-edge Lagrangian methods with data driven modeling for the purpose of uncovering, quantifying and predicting key transport processes and structures during regional flow-based hazards in the ocean and atmosphere. This project will (i) exploit and advance mathematical methods for four-dimensional (3D+time) Lagrangian Coherent Structures (LCS) in order to elucidate unsteady Lagrangian flow transport; (ii) test and develop LCS methods on historical data sets; (iii) produce efficient, accurate, distributed and web-based software to support LCS analysis and visualization; (iv) integrate LCS methodology into numerical models and non-Gaussian data assimilation; (v) perform field testing and a proof-of-concept, coupled ocean-atmosphere field experiment; and (vi) respond to a hazard of opportunity during the tenure of the project.