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UID:submissions.pasc-conference.org_PASC24_sess144_msa244@linklings.com
SUMMARY:Machine-Learning Emulation of the Radiative Transfer Module in a S
 urface Continental Model ORCHIDEE
DESCRIPTION:Minisymposium\n\nXiaoni Wang (CNRS, Le Laboratoire des Science
 s du Climat et de l'Environnement); Andrew Shao (HPE); Mandresy Rasolonjat
 ovo (UVSQ, Le Laboratoire des Sciences du Climat et de l'Environnement); F
 abienne Maignan (CEA, Le Laboratoire des Sciences du Climat et de l'Enviro
 nnement); and Philippe Peylin (CNRS, Le Laboratoire des Sciences du Climat
  et de l'Environnement)\n\nThe ORCHIDEE land surface model is one of the I
 PSL's Earth System Model components. The radiative transfer portion of the
  land model calculates reflected, absorbed and transmitted light at multip
 le canopy levels. This calculation is crucial to the climate system, but i
 s also the most time-consuming in ORCHIDEE. To solve this problem, we show
  the results of our random forest-based emulator to represent the calculat
 ion in a fast and accurate way. This emulator closely mimics the original 
 numerics-based model with relative errors of < 10% and correlations > 0.9,
  while taking ~50% less computational time. The second challenge describes
  the process of integrating this emulator in an online-way within the cont
 ext of ORCHIDEE. We describe the process of integrating using the SmartSim
  open-source, machine-learning tool into the Fortran-based model and will 
 show initial results and performance benchmarks that demonstrate the futur
 e viability of hybrid HPC/AI climate modelling.\n\nDomain: Chemistry and M
 aterials, Climate, Weather, and Earth Sciences, Engineering, Computational
  Methods and Applied Mathematics\n\nSession Chair: Riccardo Balin (Argonne
  National Laboratory)
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