Improving the Scalability of a Hurricane Forecast System in Mixed-Parallel Environments

The Hurricane Weather Research and Forecasting (HWRF) model is one of the premier models in NOAA´s operational suite of severe weather forecasting systems. An axiom in numerical weather prediction suggests that modeling the environment at high resolution optimizes forecast accuracy. However, due to operational time constraints, only the region immediately surrounding a single hurricane can be modeled in high resolution. Currently, this is achieved by embedding a relatively small high resolution, storm-following pair of grids within a larger and coarser grid. In a previous work, we extended HWRF to support multiple such independent storm-following pair of grids. The result was improved forecast accuracy by virtue of modeling storm-to-storm interactions in high resolution. However, some shortcomings in the underlying WRF framework cause these independent pairs of grids to be simulated sequentially. This limits the model´s scalability and makes it impossible to harness this novel capability within the operational time constraints. In this paper, we address this issue by modifying the underlying WRF framework to simulate these independent pairs of storm-following grids in parallel. This is the first approach to be successfully implemented in the history of the WRF framework.