Peer Reviewed Publications
A First Data-Driven Global Storm Resolving Model Intercomparison and Analysis
G. Mooers, M. Pritchard, T. Beucler, P. Srivastava, H. Mangipudi, L. Peng, P. Gentine, S. Mandt. Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning. (Under Review). https://arxiv.org/pdf/2208.11843.pdf
Simple Neural Network Emulation of Sub-grid Scale Convective Parameterizations
G.S. Mooers, M. Pritchard, T. Beucler, J. Ott, G. Yacalis, P. Baldi, P. Gentine. 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, e2020MS002385. https://doi.org/10.1029/2020MS002385
Combined Dynamic and Thermodynamic Analysis with a Multichannel VAE
H. Mangipudi, G. Mooers, M. Pritchard, T. Beucler, & S. Mandt, Analyzing high-resolution clouds and convection using multi-channel VAEs (2021). In Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2112.01221
Variational Autoencoders to Represent the Details of High Resolution Vertical Velocity Fields
G.S. Mooers, J. Tuyls, S. Mandt, M. Pritchard, and T. Beucler. Generative Modeling for Atmospheric Convection. Association for Computing Machinery. Climate Informatics 2020, https://dl.acm.org/doi/10.1145/3429309.3429324
Changing Extreme Precipitation in the Northeast United States
A.T. DeGaetano, G. Mooers, and T. Favata. Temporal Changes in the Areal Coverage of Daily Extreme Precipitation in the Northeastern United States Using High-Resolution Gridded Data. J. Appl Meteor. Climatol.,59 551-565, https://doi.org/10.1175/JAMC-D-19-0210.1.
