Title: Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Speaker: Paulo Orenstein
To improve the accuracy of long-term forecasts, the Bureau of Reclamation and the National Oceanic and Atmospheric Administration launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge, in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. Here we present and evaluate our machine learning approach to the Rodeo. Our system is an ensemble of two regression models, and exceeds that of the top Rodeo competitor as well as the government baselines for each target variable and forecast horizon.
Paulo Orenstein is a PhD Candidate in the Department of Statistics at Stanford University. He holds a Master’s in Mathematics and a Bachelor of Science in Economics, both form PUC-Rio, in Brazil. His research focuses on the interplay between statistics, probability, and computation, particularly as they apply to high-dimensional Bayesian models and Monte Carlo methods.