Description:

Numerical predictions and projections of weather and climate can allow public health professionals the ability to prepare for changes in weather-related illnesses such as those caused by extreme heat, changes in air quality, and the likelihood of environmental conditions which promote the transmission of vector borne diseases. Continual improvements in high performance computing resources and advances in conventional weather, climate, ocean, and hydrological models as well as newer data-driven AI modeling approaches allow projections to be more accurate over longer periods of time and with enhanced detail, helping to better target limited public health resources to areas of greatest need. This session seeks submissions that depend upon or further improve the relationship between HPC resources, conventional and AI data driven models, and assessments of public health impacts and resource needs. Example submissions might include, but are not limited to, utilizing GPUs and large HPC storage resources to develop new AI data driven forecasts, scaling up ensembles for clearer probabilistic guidance on future regional or local health impacts, or developing new models or analysis tools that require HPC to quickly and efficiently process datasets of weather, climate, and/or public health information.