研讨会｜机器学习和人工智能在天气和气候模式方面的新机遇（视频和PPT）

文末可获取所有演讲视频及PPT

本次研讨会是由IS-ENES3和ESiWACE2联合举办，旨在将来自学术界和产业界的气候科学家和专家聚集在一起，分享知识和经验，并在天气和气候建模的机器学习、人工智能和大数据技术领域发现新的机遇。

研讨会主要围绕以下三个主题：

• Views from Domain Science
• ML/AI Software Technologies
• High performance, Infrastructure and Big data Challenges

Session 1: Views from Domain Science

• New approaches based on ML for a range of climate prediction problems, Emily Shuckburgh (U. Cambridge)
• Philosophy and Targeted Applications of ML/AI Techniques for Climate Risk Analytics at Jupiter, Luke Madaus & Steve Sain (Jupiter Intel.)
• The optimization dichotomy: Why is it so hard to improve climate models with machine learning, Stephan Rasp (ClimateAI)
• Improving convection parameterizations with a library of large-eddy simulations, Zhaoyi Shen (Caltech)
• Stochastic Super-Resolution for Convective Regimes using Gaussian Random Fields, Rachel Prudden (Met Office Inf. Lab)
• Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, Kirsten Mayer (CSU)
• Using transfer learning and backscattering analysis to build stable, generalizable data-driven subgrid-scale models: A 2D turbulence test case, Pedram Hassanzadeh (U. Rice)

Session 2: ML/AI Software technologies

• Stochastic machine learning for atmospheric fields with generative adversarial networks, Jussi Leinonen (MeteoSwiss)
• Causal discovery in time series with unobserved confounders, Andreas Gerhardus (DLR Jena)
• Estimating stochastic closures using sparsity-promoting ensemble Kalman inversion, Jinlong Wu (Caltech)
• Deep Learning on the sphere for weather/climate applications, Gionata Ghiggi and Michaël Defferrard (EPFL)
• Deep learning-based remote sensing for infrastructure damage assessment, Thomas Chen (AMSE)
• Leveraging physics information in neural networks for fluid flow problems, Akshay Subramaniam (NVIDIA)
• Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models, Jonathan Weyn (U. of Washington)

Session 3: High performance, Infrastructure and Big data challenges

• Scaling Up Deep Learning Workloads - A Data-Centric View, Tal Ben-Nun (ETHZ)
• Radar QPE and Machine Learning, Micheal Simpson (NOAA)
• Using ML at the Edge to Improve Data Gathering, Pete Warden (Google)
• An Overview of ML and AI on Arm Based HPC Systems for Weather and Climate Applications, Phil Ridley (Arm)
• Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows, Jan Ackmann (U. Oxford)
• You do you. How next-gen data platforms can stop weather and climate scientists from being software engineers and other perversions, Theo McCaie (MO Informatics Lab)
• 3D bias correction with deep learning in the Integrated Forecasting System, Thorsten Kurth (NVIDIA)

为了了解一下大家对机器学习和人工智能在气象领域的应用的关注度，此次通过在后台关键词方式获取资源。可在后台回复红字关键词获取：ISENES

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