Mooers, Griffin, Pritchard, Michael, Beucler, Tom, Ott, Jordan, Yacalis, Galen, Baldi, Pierre, and Gentine, Pierre. Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions. Retrieved from https://par.nsf.gov/biblio/10299897. Journal of Advances in Modeling Earth Systems 13.5 Web. doi:10.1029/2020MS002385.

Mooers, Griffin, Pritchard, Michael, Beucler, Tom, Ott, Jordan, Yacalis, Galen, Baldi, Pierre, & Gentine, Pierre. Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions. Journal of Advances in Modeling Earth Systems, 13 (5). Retrieved from https://par.nsf.gov/biblio/10299897. https://doi.org/10.1029/2020MS002385

Mooers, Griffin, Pritchard, Michael, Beucler, Tom, Ott, Jordan, Yacalis, Galen, Baldi, Pierre, and Gentine, Pierre. "Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions". Journal of Advances in Modeling Earth Systems 13 (5). Country unknown/Code not available. https://doi.org/10.1029/2020MS002385. https://par.nsf.gov/biblio/10299897.

@article{osti_10299897,
place = {Country unknown/Code not available}, title = {Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real‐Geography Boundary Conditions}, url = {https://par.nsf.gov/biblio/10299897}, DOI = {10.1029/2020MS002385}, abstractNote = {}, journal = {Journal of Advances in Modeling Earth Systems}, volume = {13}, number = {5}, author = {Mooers, Griffin and Pritchard, Michael and Beucler, Tom and Ott, Jordan and Yacalis, Galen and Baldi, Pierre and Gentine, Pierre}, editor = {null} }