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Evaluating the Prediction of Orographic Precipitation Gradients From a Convolutional Neural Network

Semester: Fall 2023

Presentation description

The capability of weather models to precisely predict wintertime precipitation is critical in the Rocky Mountains. A convolutional neural network trained on historical data seeks to predict orographic precipitation gradients to increase the resolution of precipitation forecasting over complex terrain. This research evaluated the network's prediction ability in the Northern Rockies through exploration of relationships between its output statistics & characteristics of facets, or mountain faces.

Presenter Name: Annabelle Warner

Presentation Type: Poster
Presentation Format: Virtual
Presentation #40
College: Mines & Earth Sciences
School / Department: Atmospheric Sciences
Research Mentor: Courtenay Strong