Position paper on high performance computing needs in earth system prediction

The article of record as published may be found at http://dx.doi.org/10.7289/V5862DH3The United States experiences some of the most severe weather on Earth. Extreme weather or climate events - such as hurricanes, tornadoes, flooding, drought, and heat waves - can devastate communities and businesses, cause loss of life and property, and impact valuable infrastructure and natural resources. The number and severity of extreme weather and climate events in the U.S. has risen since 1980, and is projected to continue rising this century. Growing populations in vulnerable areas create increased risks. If current trends continue, damages from extreme weather and climate events could grow four-fold by 2050. Predictions and projections of weather and extreme events across time scales from weather to climate rely on sophisticated numerical models running on High Performance Computing (HPC) systems, which press the frontier of the Nation’s HPC capability. The Nation’s Earth system modeling community has a unique set of HPC requirements which differ from industry needs. Typically, HPC advances are measured using computational peak performance metrics that are ill-suited to Earth system modeling applications. We advocate for a shift in processor design to increase emphasis on memory bandwidth, so Earth system models run more efficiently and better serve the public need

Progress Toward Demonstrating Operational Capability of Massively Parallel Processors at the Forecast Systems Laboratory

Two mesoscale numerical weather prediction models, Eta and the Rapid Update Cycle (RUC), are currently being run operationally at the U.S. National Weather Service's National Meteorological Center to produce nationwide weather forecasts. Improvements in weather forecast accuracy depend on increasing model resolution, which is limited by available computing resources. Massively Parallel Processing (MPP) offers a cost-effective way of increasing computing resources. At the Forecast Systems Laboratory, we are developing parallel versions of both models that can be easily ported between different MPP systems and traditional sequential machines. To support this effort, we have developed the Nearest Neighbor Tool (NNT), a software library of high level routines that greatly reduces the effort required to parallelize finite difference approximation weather forecast models 1 . Here, we describe our experiences using NNT to parallelize the Eta and RUC models, discuss performance optimization ..

Exascale computing and data handling: challenges and opportunities for weather and climate prediction

The emergence of exascale computing and artificial intelligence offer tremendous potential to significantly advance earth system prediction capabilities. However, enormous challenges must be overcome to adapt models and prediction systems to use these new technologies effectively. A recent WMO report on exascale computing recommends “urgency in dedicating efforts and attention to disruptions associated with evolving computing technologies that will be increasingly difficult to overcome, threatening continued advancements in weather and climate prediction capabilities. Further, the explosive growth in data from observations, model and ensemble output, and post processing threatens to overwhelm the ability to deliver timely, accurate, and precise information needed for decision making. AI offers untapped opportunities to alter how models are developed, observations are processed, and predictions are analyzed and extracted for decision-making. Given the extraordinarily high cost of computing, growing complexity of prediction systems and increasingly unmanageable amount of data being produced and consumed, these challenges are rapidly becoming too large for any single institution or country to handle. This paper describes key technical, and budgetary challenges, identifies gaps and ways to address them, and makes a number of recommendations