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DTSTAMP:20210916T132528Z
LOCATION:Jean-Jacques Rousseau
DTSTART;TZID=Europe/Stockholm:20210708T170000
DTEND;TZID=Europe/Stockholm:20210708T190000
UID:submissions.pasc-conference.org_PASC21_sess196@linklings.com
SUMMARY:High Performance Scientific Computing in Aquatic Research
DESCRIPTION:Minisymposium\n\nLakes form an integral component of ecosystem
 s and our communities, with a significant portion of the Swiss population 
 living in their close proximity. Better understanding of the internal lake
  processes and can be obtained through the development of more accurate co
 mputational models and the use of the newly available high frequency senso
 r data. Enabled by powerful computational resources, researchers can now t
 est and evaluate multitude of model paradigms, calibrate and infer quantit
 ies governing the physical and ecological dynamical processes and study th
 e underlying fine-scale mechanisms. These methodologies can be coupled wit
 h state-of-the-art data assimilation techniques allowing to perform statis
 tical inference of inaccessible quantities of interest and to perform accu
 rate forecasting for early warning systems, including the quantification o
 f the associated uncertainty. The goal of this minisymposium is to foster 
 exchange of recent developments and methodologies pertaining to high perfo
 rmance computing in aquatic research, with a large focus on lake phenomena
 . These discussions aim to help scientists to better understand complex pr
 ocesses that are relevant to improve the quality of lake models and predic
 tive frameworks.\n\nOpen Discussion on High Performance Scientific Computi
 ng in Aquatic Research\n\nSafin\n\nFollowing the formal presentations, we 
 invite an open discussion on the use of HPC in aquatic science. Given the 
 emergent nature of this field, an exchange to find the common interests an
 d potential synergies could provide a substantial benefit to the community
 .\n\n---------------------\nData Assimilation for a 3D Hydrodynamic Model 
 of Lake Geneva Combining Novel Particle Filtering and Machine Learning Alg
 orithms\n\nSafin, Bouffard, Sukys, Ozdemir, Runnalls\n\nAs part of the DAT
 ALAKES project, we develop a comprehensive data assimilation framework for
  a 3D hydrodynamic model of Lake Geneva. We use the SPUX package, which en
 ables Baeysian inference and particle Markov Chain Monte Carlo methods for
  uncertainty quantification. For data assimilation, the filt...\n\n-------
 --------------\nImproving Predictions of the Terrestrial Water Cycle by In
 tegrated Modelling, Data Assimilation and HPC\n\nHendricks-Franssen, Kelle
 r, Ghasemi, Goergen, Caviedes-Voullieme...\n\nThe modelling of the terrest
 rial water and energy cycles is important for weather prediction, climate 
 scenarios, water resources assessment and for agricultural applications. W
 e use the coupled terrestrial  systems model TSMP which simulates the wate
 r and energy cycles from the deep subsurface to th...\n\n-----------------
 ----\nTsunami Propagation in Lakes - an HPC Task?\n\nBacigaluppi, Bürgler,
  Strupler, Kremer, Vetsch\n\nNowadays, high performance computing (HPC) pl
 ays a leading role along with robust, stable and accurate numerical method
 s, to guarantee the effectiveness and suitability of software for any proc
 ess involving decision support in engineering practice and science. In par
 ticular, in the context of rivers...\n\n\nDomain: CS and Math, Emerging Ap
 plications, Climate and Weather
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