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DTSTAMP:20190719T085744Z
LOCATION:HG EO Nord
DTSTART;TZID=Europe/Stockholm:20190613T195000
DTEND;TZID=Europe/Stockholm:20190613T215000
UID:submissions.pasc-conference.org_PASC19_sess179_post126@linklings.com
SUMMARY:CLW03 - DATALAKES - Data Platform and Stochastic Bayesian Forecast
 ing for Swiss Lakes Using Supercomputers
DESCRIPTION:Poster\n\n\nCLW03 - DATALAKES - Data Platform and Stochastic B
 ayesian Forecasting for Swiss Lakes Using Supercomputers\n\nSafin, Bouffar
 d, Georgatos, Bouillet, Perez Cruz...\n\nDATALAKES is a multi-disciplinary
  and multi-institutional project involving Eawag, ETH Zurich, Swiss Data S
 cience Center and EPF Lausanne. Lakes are often misrepresented uniformly i
 n blue, hiding rich spatio-temporal dynamics. As Chateau d’Eau of Eu
 rope, Switzerland requires a scientifically grounded lake management. A ke
 y prerequisite are 3D numerical simulations, capable of delivering forecas
 t with uncertainties. Yet, required in situ observations, remote sensing a
 nd computational resources are under-utilized. DATALAKES aims to develop a
 n end-to-end data platform for lake dynamics, including: acquisition, qual
 ity control, curation, storage, visualization, numerical modeling and futu
 re forecast. Machine learning will be employed to provide a data-driven mo
 del of input processes (skin-to-bulk). Bayesian uncertainty quantification
  will use modern statistical sampling algorithms with multi-level variance
  reduction, while harvesting parallel compute clusters to mitigate vast co
 mputational costs. DATALAKES envisions a platform for monitoring lakes dyn
 amics, including reliable weekly forecasts. We also hope to incite new col
 laborations between scientists, stakeholders, and citizens, and aim to fur
 ther improve scientifically grounded management of water resources in Swit
 zerland.
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