Machine Learning Predictions of a Multiresolution Climate Model Ensemble
暂无分享,去创建一个
[1] George C. Runger,et al. Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination , 2009, J. Mach. Learn. Res..
[2] Mrinal K. Sen,et al. Error Reduction and Convergence in Climate Prediction , 2008 .
[3] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[4] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[5] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[6] Earth's Global Energy Budget , 2009 .
[7] Richard A. Marcum,et al. Application of deep convolutional neural networks to automatic feature/object detection in high resolution remote sensing imagery , 2017 .
[8] Marie-Alice Foujols,et al. Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model , 2013, Climate Dynamics.
[9] Michael Goldstein,et al. Fast linked analyses for scenario‐based hierarchies , 2012 .
[10] Michael Goldstein,et al. Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations , 2009, Technometrics.
[11] Sally A. McFarlane,et al. Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate , 2012 .
[12] Hui Wan,et al. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models , 2014 .
[13] S. E. Haupt,et al. Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather , 2017 .
[14] Richard Neale,et al. Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5 , 2015 .
[15] Peter Z. G. Qian,et al. Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments , 2008, Technometrics.
[16] L. Ruby Leung,et al. Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions , 2017 .
[17] W. Collins,et al. Description of the NCAR Community Atmosphere Model (CAM 3.0) , 2004 .
[18] Leonard A. Smith,et al. Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.
[19] Derek Bingham,et al. Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators , 2012, Technometrics.
[20] Prabhat,et al. Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets , 2016, ArXiv.
[21] Andrew Gettelman,et al. The Art and Science of Climate Model Tuning , 2017 .
[22] Katherine J. Evans,et al. Exploring an Ensemble-Based Approach to Atmospheric Climate Modeling and Testing at Scale , 2017, ICCS.
[23] Donald D. Lucas,et al. The parametric sensitivity of CAM5's MJO , 2014 .
[24] R. A. Miller,et al. Sequential kriging optimization using multiple-fidelity evaluations , 2006 .
[25] Andrei P. Sokolov,et al. Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations , 2002, Science.
[26] Malte Prieß,et al. Surrogate-based optimization of climate model parameters using response correction , 2011, J. Comput. Sci..
[27] M. Webb,et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations , 2004, Nature.
[28] W. Collins,et al. The Community Earth System Model: A Framework for Collaborative Research , 2013 .
[29] Claire Monteleoni,et al. Tracking climate models , 2011, CIDU.
[30] James E. Campbell,et al. An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment , 1981 .
[31] Donald D. Lucas,et al. Failure analysis of parameter-induced simulation crashes in climate models , 2013 .
[32] A. O'Hagan,et al. Predicting the output from a complex computer code when fast approximations are available , 2000 .