FORTRAN evaluation engine for Comparing Methods of Hurricane Forecast Uncertainty with Neural Networks
Evaluate pre-trained artificial neural networks to estimate consensus hurricane intensity and track errors, as well as the associated uncertainties of the network predictions.
This code was compiled and tested using: *GNU Fortran (MinGW-W64 x86_64-ucrt-posix-seh, built by Brecht Sanders) 12.2.0
This work is a collaborative effort between Dr. Elizabeth A. Barnes and Dr. Randal J. Barnes and Dr. Mark DeMaria.
[1] Barnes, Elizabeth A., Randal J. Barnes and Nicolas Gordillo, 2021: Adding Uncertainty to Neural Network Regression Tasks in the Geosciences, arXiv 2109.07250.
[2] Barnes, Elizabeth A., Randal J. Barnes and Mark DeMaria, 2022: Sinh-arcsinh-normal distributions to add uncertainty to neural network regression tasks: applications to tropical cyclone intensity forecasts, preprint available at https://doi.org/10.31223/X51649.
This project is licensed under an MIT license.
MIT © Elizabeth A. Barnes