Benefits of a Closely-Spaced Satellite Constellation of Atmospheric Polarimetric Radio Occultation Measurements. - F. Joseph Turk; Ramon Padullés; Chi O. Ao; Manuel de la Torre Juárez; Kuo-Nung Wang; Garth W. Franklin; Stephen T. Lowe; Svetla M. Hristova-Veleva; Eric J. Fetzer; Estel Cardellach; Yi-Hung Kuo; J. David Neelin (Remote. Sens., 2019)

Climate-Invariant Machine Learning. - Tom Beucler; Michael S. Pritchard; Janni Yuval; Ankitesh Gupta; Liran Peng; Stephan Rasp 001; Fiaz Ahmed; Paul A. O'Gorman; J. David Neelin; Nicholas J. Lutsko; Pierre Gentine (CoRR, 2021)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)