FORTRAN evaluation engine for Comparing Methods of Hurricane Forecast Uncertainty with Neural Networks

October 24, 2022 · View on GitHub

Evaluate pre-trained artificial neural networks to estimate consensus hurricane intensity and track errors, as well as the associated uncertainties of the network predictions.

FORTRAN Code

This code was compiled and tested using: *GNU Fortran (MinGW-W64 x86_64-ucrt-posix-seh, built by Brecht Sanders) 12.2.0

General Notes

Credits

This work is a collaborative effort between Dr. Elizabeth A. Barnes and Dr. Randal J. Barnes and Dr. Mark DeMaria.

References

[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.

License

This project is licensed under an MIT license.