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DTSTAMP:20210916T132528Z
LOCATION:Jean Calvin
DTSTART;TZID=Europe/Stockholm:20210708T110000
DTEND;TZID=Europe/Stockholm:20210708T130000
UID:submissions.pasc-conference.org_PASC21_sess114@linklings.com
SUMMARY:Enriching Earth and Climate Science Simulations Using AI/ML
DESCRIPTION:Minisymposium\n\nIn this mini-symposium, we examine the increa
 sing role that AI/ML methods are playing in the Earth and Climate Sciences
 , with speakers relating how these new tools can be used judiciously. From
  the atmospheric sciences side, we will see how ML can help understand the
  variability of the stratospheric polar vortex and thus enhance seasonal f
 orecasts. And in terms of solid earth science, we discuss the usefulness o
 f ML in gaining insight into earthquake dynamics. We further cover how to 
 provision ML services for Earth System sciences as these tools gain more a
 doption within the scientific communities, and need to be made available i
 n a more systematic way. Finally, we peer into the future of both ML and E
 arth Science, with thoughts on how sparsity in both of these areas will li
 kely grow over time, and the expected interplay of this sparsity with curr
 ent and alternative computer architectures.\n\nToward Enhanced Seasonal Fo
 recasts Using Machine learning: Understanding the Polar Vortex Variability
 \n\nde Fondeville\n\nIn both hemispheres, the atmosphere above polar regio
 ns is characterized by a vortex of winds, whose intensity fluctuates seaso
 nally and peaks during winter. In average once every two years, a sudden w
 arming of the stratosphere disturbs the wind field causing displacements a
 nd deformations of the vo...\n\n---------------------\nOn Sparsity in AI/M
 L and Earth Science Applications, and its Architectural Implications\n\nSp
 eiser\n\nThe topic of sparsity relates to many aspects of the interplay be
 tween Earth Science, AI/ML, and computing. Many types of data are intrinsi
 cally sparse, and in situations when data are naturally dense there are ma
 ny mechanisms which generate sparsity in data collection and aggregation. 
 We discuss so...\n\n---------------------\nReinforcement Learning Optimize
 r for Earthquakes Simulations using Fiber Bundle Models\n\nMonterrubio-Vel
 asco, Modesto, Carrasco Jimenez, de la Puente\n\nRupture of any heterogene
 ous material is a complex physical process difficult to model deterministi
 c due to the number of unmeasurable parameters involved and the poorly con
 strained physical conditions. The lack of long seismic series, due to our 
 short instrumental recording time, makes it difficult...\n\n--------------
 -------\nML Service Provisioning for Earth System Science\n\nLudwig\n\nThe
  German Climate Computing Centre provides services for its four shareholde
 rs and the German national climate research and earth system science commu
 nity. In the past, services were centered around high performance computin
 g, climate model support, and data management. With the advent of machine 
 l...\n\n\nDomain: CS and Math, Emerging Applications, Climate and Weather,
  Solid Earth Dynamics
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