Advancing U.S. Operational Weather Prediction Capabilities in the Next Decade with Exascale HPC, Machine Learning and Big Data Technologies

Mark Govett

  22 May, Noon, in 2155


A revolution in computing, modeling, software and big data is needed to advance U.S. weather prediction capabilities in the next decade. An estimated 1000 to 10000 times more computing is needed to advance prediction models to cloud-resolving, 1-3KM resolution global scales. However, existing models are not capable of exploiting future HPC systems with tens to hundreds of millions of processors. Models will need to be rewritten to use more efficient algorithms, incorporate parallelism at all levels, minimize inter-processor communications, and improve I/O efficiency. In addition, Artificial Intelligence (AI) can be used to replace computeheavy calculations with fast, light-weight algorithms. AI and the rapidly growing field of Machine Learning (ML) has the potential to disrupt the way weather prediction and assimilation models are developed in the future.

In addition, the current prediction system is being overwhelmed with too much data. New strategies are needed to more effectively handle the ingest, processing, computation, and distribution of data within the prediction system. Emerging technologies such as 5G networks, cloud computing, ML, and edge computing can be used to support the processing, distribution, and dissemination of data, information, and insights to diverse end users.

This presentation will offer a critical and visionary assessment of key technologies and developments needed to advance U.S. operational weather prediction in the next decade. I will describe challenges in our prediction system today and highlight exploratory developments at GSD and other modeling centers to overcome these challenges.