CIRA Guide to Custom Loss Functions for Neural Networks in Environmental Sciences - Version 1. - Imme Ebert-Uphoff; Ryan Lagerquist; Kyle Hilburn; Yoonjin Lee; Katherine Haynes; Jason Stock; Christina Kumler; Jebb Q. Stewart (CoRR, 2021)

Can we integrate spatial verification methods into neural-network loss functions for atmospheric science? - Ryan Lagerquist; Imme Ebert-Uphoff (CoRR, 2022)

Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications. - Tom Beucler; Arthur Grundner; Sara Shamekh; Peter Ukkonen; Matthew Chantry; Ryan Lagerquist (CoRR, 2024)

Center-fixing of tropical cyclones using uncertainty-aware deep learning applied to high-temporal-resolution geostationary satellite imagery. - Ryan Lagerquist; Galina Chirokova; Robert DeMaria; Mark DeMaria; Imme Ebert-Uphoff (CoRR, 2024)