Peak Load and Grid Resilience: Developed a unique set of North American winter weather regimes to predoict electricity load spikes across central US power markets.
Selecting the Best Models: Evaluated subseasonal models for different stratosphere-troposphere coupling metrics important to deliver produce reliable temperature forecasts for critical energy demand.
Preparing for Extreme Rainfall: Co-developed advanced methods of identifying subseasonal extreme precipitation events across the US and their precursors, with use for identifying potential flood risks and operational delays.
Agricultural Risk for Flash Droughts: Employed statistical and machine learning techniques for early flash drought detection.
El Niño Impacts on Weather Extremes: Identified and modeled new atmospheric pathways linking different types of El Niño to North Pacific and North America extreme weather patterns.
Arctic Climate Extremes: Evaluated interactions between Arctic amplification, polar vortex disruptions, and regional weather patterns for winter extremes in the present and future climate.
Hydroclimate Extremes: Linked long-lasting North Pacific atmospheric circulations to hydroclimate extremes across western North America, with significant impacts on water availability in the region.
Setting Data Standards in Climate Prediction. Established key steps for the pre-processing of weather and climate data for machine learning and AI applications, supporing prediction models are built on clean, accurate, and defensible science.
Infrastructure Climate Risk: Applied deep learning and statistical downscaling of climate models to predict future pavement degradation and structural stress across Oklahoma.
AI-Driven Prediction Tool: Built custom medium-to-long range cold wave prediction tool with random forests, directly applied to energy trading needs.
