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

Semester: Spring 2024


Presentation description

Weather forecasts often decrease in precision over complex terrain due to predictions generalizing for large areas. A convolutional neural network trained to predict orographic precipitation gradients to increase resolution of precipitation forecasting over complex terrain was evaluated for this project. Evaluation of the spatial correlation between facets and their OPG values in the Western US was performed to be applied as a loss function in the training of the model to improve its accuracy.

Presenter Name: Annabelle Warner

Presentation Type: Poster
Presentation Format: In Person
Presentation #B29
College: Mines & Earth Sciences
School / Department: Atmospheric Sciences
Research Mentor: Courtenay Strong
Date | Time: Tuesday, Apr 9th | 10:45 AM