The CASCADE computation and predictions team is developing scientific tools, workflow patterns, and scalable algorithms that can process massive model output on modern HPC systems.

The computation and predictions team is tightly integrating the detection system with the attribution framework so that statistics from the detection analyses automatically yield the probability distribution functions required to produce quantitative attribution and projection statements for extreme events. In a related effort, we are integrating event detection and analysis with the ILIAD ((InitiaLized-ensemble Identify, Analyze, Develop) framework to ensure that probabilities of event detection do not depend on model configuration, thereby mitigating the resolution dependence of hurricane detection.

The CASCADE research portfolio requires extensive computational and statistical infrastructure. Much of the SFA research requires implementation of novel statistical methods. Likewise, the formal application of UQ methods for extremes requires the implementation of a surrogate model and new developments in emulator methodology. Further, all of the SFA’s analyses require sophisticated, robust, and parallelizable data analysis tools to operate on the enormous datasets that we use (O (100{1000) TB). Therefore, the SFA focuses on three main research and development foci to support the broader goals of the project:  methodological development for systematic event causation at ne spatial scales, development of a statistical framework for the holistic uncertainty characterization work, and development of a multilevel model emulator for extremes.

High performance computing to detect and predict changes in weather extremes.