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UID:submissions.pasc-conference.org_PASC24_sess129_msa189@linklings.com
SUMMARY:Fortran's Role and Evolution in Earth System Prediction: Integrati
 ng Machine Learning with Traditional Modeling Techniques
DESCRIPTION:Minisymposium\n\nMilan Curcic (University of Miami)\n\nNumeric
 al weather, ocean, and climate (together, Earth system) prediction has bee
 n a humanity’s essential activity toward minimizing the loss of human live
 s and damage to infrastructure. It has also heavily relied on Fortran sinc
 e its inception in the late 1950s. Fast forward to 2024, virtually all wea
 ther, ocean, and climate models that are used for critical decision making
  by governments and businesses are implemented in Fortran, most typically 
 a mix of legacy and modern dialects. Historically, these models and their 
 developer communities have gone through several paradigm shifts, for examp
 le the introduction of vector supercomputers, then distributed-memory clus
 ters, and finally, specialized hardware accelerators such as GPUs. At this
  moment, the looming paradigm shift, and likely the largest one to date, i
 s the adoption of machine learning to emulate numerical models in part or 
 in their entirety. Having spent a number of years deep in the Earth system
  model development world, Fortran advocacy and education, and collaborativ
 e research across academia, government, and industry sectors, I will share
  my perspectives on the key challenges that the Earth system enterprise fa
 ces in the context of software implementation, hardware architectures, and
  emerging machine learning techniques.\n\nDomain: Climate, Weather, and Ea
 rth Sciences, Computational Methods and Applied Mathematics\n\nSession Cha
 ir: Damian Rouson (Lawrence Berkeley National Laboratory, Sourcery Institu
 te)
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