Scott Capps

Background

About Scott Capps

Conducting and applying atmospheric research and data mining to ease our society's transition to a more sustainable future. Applying machine learning techniques and statistical models to transform short-term and medium-term numerical weather model forecasts into intelligent operational guidance and risk management products. Specialities include design and implementation of operational short-term and medium-term weather forecast guidance products for high-impact weather events and wildfires. Skills: Linux server and high-performance cluster administration Programming (Python, NCL, NCO, Fortran, MySQL) Statistics Machine Learning: Logistic regression, self-organizing maps, artificial neural networks (ANN), etc. Weather Research and Forecasting (WRF) Model Specialities/Interests: Model Output Statistics (MOS) design, programming and operational implementation Applications of machine learning methods (Logistic regression, SOM, ANN) to weather forecasting Statistical and dynamical-downscaling of atmospheric data to resolutions useful for guiding and informing the public and utility operations Meteorology and climatology Near-surface wind dynamics and boundary layer processes Wildfire-centric weather variable research and forecasting Wind energy resource assessment High-resolution climatological dataset construction and analysis Extreme weather event analysis and prediction