Can Machine Learning Diagnose Extremes
Abstract
Project AbstractApproved for Public ReleaseDeep-Learning-Weather-Prediction (DLWP) has shown great promise as a method for improvi,ng subseasonal-to-seasonal (S2S) forecasts, as well as weather forecasts at shorter lead times. Two key aspects of DLWP forecast mod,els are their extreme computational efficiency and their abil- ity to learn physical parameterizations in a holistic sense as part o,f the model training. DLWP?s computational efficiency allows the generation of ensemble forecasts of unprecedented size, with thousa,nds of members. Such large ensembles, if well-calibrated, could be very useful in identifying extreme events. This is a proposal for, a short project to fund an incoming graduate student and the PI to assess a crucial property of such DLWP ensembles: their ability,to accurately capture extremes. In particular, we will test the ability of ML to diagnose extreme surface temperatures when trained,only on less extreme events.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 06, 2022
- Source ID
- N000142212807
Entities
People
- Dale R. Durran
Organizations
- Office of Naval Research
- United States Navy
- University of Washington