ML Paper dump from Reading Group

dump

Zanna, Laure; Bolton, Thomas	Data-Driven Equation Discovery of Ocean Mesoscale Closures
Toms, Benjamin A.; Kashinath, Karthik; Prabhat; Yang, Da	Testing the Reliability of Interpretable Neural Networks in Geoscience Using the Madden-Julian Oscillation
Rasp, Stephan	Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0)
Mooers, Griffin; Tuyls, Jens; Mandt, Stephan; Pritchard, Michael; Beucler, Tom	Generative Modeling for Atmospheric Convection
Mooers, Griffin; Pritchard, Mike; 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
Grönquist, Peter; Yao, Chengyuan; Ben-Nun, Tal; Dryden, Nikoli; Dueben, Peter; Li, Shigang; Hoefler, Torsten	Deep Learning for Post-Processing Ensemble Weather Forecasts
Gao, Han; Sun, Luning; Wang, Jian-Xun	PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain
Denby, L.	Discovering the Importance of Mesoscale Cloud Organization Through Unsupervised Classification
Moosavi, Azam; Rao, Vishwas; Sandu, Adrian	Machine learning based algorithms for uncertainty quantification in numerical weather prediction models
Yuan, Tianle; Song, Hua; Wood, Robert; Mohrmann, Johannes; Meyer, Kerry; Oreopoulos, Lazaros; Platnick, Steven	Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology
Chantry, Matthew; Christensen, Hannah; Dueben, Peter; Palmer, Tim	Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
Geer, A. J.	Learning earth system models from observations: machine learning or data assimilation?
Watson-Parris, D.	Machine learning for weather and climate are worlds apart
Kashinath, K.; Mustafa, M.; Albert, A.; Wu, J-L.; Jiang, C.; Esmaeilzadeh, S.; Azizzadenesheli, K.; Wang, R.; Chattopadhyay, A.; Singh, A.; Manepalli, A.; Chirila, D.; Yu, R.; Walters, R.; White, B.; Xiao, H.; Tchelepi, H. A.; Marcus, P.; Anandkumar, A.; Hassanzadeh, P.; Prabhat, null	Physics-informed machine learning: case studies for weather and climate modelling
Gettelman, A.; Gagne, D. J.; Chen, C.-C.; Christensen, M. W.; Lebo, Z. J.; Morrison, H.; Gantos, G.	Machine Learning the Warm Rain Process
Gottwald, Georg A.; Reich, Sebastian	Supervised learning from noisy observations: Combining machine-learning techniques with data assimilation
Schultz, M. G.; Betancourt, C.; Gong, B.; Kleinert, F.; Langguth, M.; Leufen, L. H.; Mozaffari, A.; Stadtler, S.	Can deep learning beat numerical weather prediction?
Arul, Monica; Kareem, Ahsan	Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
Beucler, Tom; Pritchard, Michael; Rasp, Stephan; Ott, Jordan; Baldi, Pierre; Gentine, Pierre	Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Rasp, Stephan; Thuerey, Nils	Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench
Geiss, Andrew; Hardin, Joseph C.	Invertible CNN-Based Super Resolution with Downsampling Awareness