Research and Development within the Scientific Computing Branch of NOAA’s Global Systems Laboratory

The NOAA Global Systems Laboratory, Earth Prediction Advancement Division, Scientific Computing Branch team works on approaches to facilitate development of cloud-resolving, Earth system prediction systems suitable for the next generation of exascale high performance computing (HPC), including exploration of machine learning (ML) algorithms within our systems for improved performance and reduced computational cost. 

Our work is divided into two main categories: incremental - shorter term and innovative - longer term challenges. Work related to incremental changes focuses on existing NOAA algorithms and improvement of their performance on different architectures (e.g. adapting existing codes to run on GPUs). The more innovative aspects focus on development and evaluation of new algorithms and approaches to environmental modeling that simultaneously improve prediction accuracy, performance, and portability. For this purpose we have developed the GeoFLuid Object Workflow (GeoFLOW), a C++ framework with convective (and other)  dynamics, high order truncation, quantifiable dissipation, an option to use a variety of 2D and 3D grids, and excellent strong scaling and on-node properties. An evaluation of the use of ML-based emulators for different components of the earth system prediction models also forms an important part of our research. 

Finally, a large portion of our research and development activities involves building federated and unified workflows to facilitate both the effective use of distributed computing resources as well as easy configuration for nontrivial workflow applications in research and operations.

A comprehensive summary of these research and development activities will be presented.