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.