A data-driven framework for the stochastic reconstruction of small-scale features with application to climate data sets

Article history: Available online 2 June 2021

[1]  Ronald M. Errico,et al.  Spectral analysis of forecast error investigated with an observing system simulation experiment , 2015 .

[2]  Jian Yu,et al.  Flowfield Reconstruction Method Using Artificial Neural Network , 2019, AIAA Journal.

[3]  P. Gent,et al.  Isopycnal mixing in ocean circulation models , 1990 .

[4]  Andrew J. Majda,et al.  Stochastic Superparameterization and Multiscale Filtering of Turbulent Tracers , 2017, Multiscale Model. Simul..

[5]  Andrew J Majda,et al.  Efficient stochastic superparameterization for geophysical turbulence , 2013, Proceedings of the National Academy of Sciences.

[6]  Nan Chen,et al.  Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification , 2018, Entropy.

[7]  K. Taira,et al.  Super-resolution reconstruction of turbulent flows with machine learning , 2018, Journal of Fluid Mechanics.

[8]  Antonio Napolitano,et al.  Cyclostationarity: Half a century of research , 2006, Signal Process..

[9]  José M. Gutiérrez,et al.  VALUE: A framework to validate downscaling approaches for climate change studies , 2015 .

[10]  G. Boer.  Predictability as a function of scale , 2003 .

[11]  S. Pryor,et al.  Downscaling temperature and precipitation: a comparison of regression‐based methods and artificial neural networks , 2001 .

[12]  Lawrence Sirovich,et al.  Karhunen–Loève procedure for gappy data , 1995 .

[13]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[14]  D. A. Sachindra,et al.  Machine learning for downscaling: the use of parallel multiple populations in genetic programming , 2019, Stochastic Environmental Research and Risk Assessment.

[15]  Nan Chen,et al.  Filtering Nonlinear Turbulent Dynamical Systems through Conditional Gaussian Statistics , 2016 .

[16]  Andrew Glaws,et al.  Adversarial super-resolution of climatological wind and solar data , 2020, Proceedings of the National Academy of Sciences.

[17]  Andrew J. Majda,et al.  Stochastic superparameterization in a one-dimensional model for wave turbulence , 2014 .

[18]  Pabitra Mitra,et al.  Statistical downscaling of precipitation using long short-term memory recurrent neural networks , 2018, Theoretical and Applied Climatology.

[19]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[20]  M. Spall,et al.  Specification of eddy transfer coefficients in coarse resolution ocean circulation models , 1997 .

[21]  T. Wigley,et al.  Statistical downscaling of general circulation model output: A comparison of methods , 1998 .

[22]  Steven L. Brunton,et al.  Shallow Learning for Fluid Flow Reconstruction with Limited Sensors and Limited Data , 2019, ArXiv.

[23]  José Manuel Gutiérrez,et al.  Configuration and intercomparison of deep learning neural models for statistical downscaling , 2019, Geoscientific Model Development.

[24]  F. Waleffe.  The nature of triad interactions in homogeneous turbulence , 1992 .

[25]  Andrew J. Majda,et al.  Stochastic superparameterization in a quasigeostrophic model of the Antarctic Circumpolar Current , 2015 .

[26]  Sangram Ganguly,et al.  DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution , 2017, KDD.

[27]  Eun-Sung Chung,et al.  A hybrid model for statistical downscaling of daily rainfall , 2016 .

[28]  Ian T. Jolliffe,et al.  Empirical orthogonal functions and related techniques in atmospheric science: A review , 2007 .

[29]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[30]  Balaji Rajagopalan,et al.  Statistical downscaling using K‐nearest neighbors , 2005 .

[31]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[32]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[33]  George Barbastathis,et al.  Imaging through glass diffusers using densely connected convolutional networks , 2017, Optica.

[34]  M. Vohland,et al.  Downscaling land surface temperatures at regional scales with random forest regression , 2016 .

[35]  Christian Lessig,et al.  A Local Spectral Exterior Calculus for the Sphere and Application to the Shallow Water Equations , 2020, ArXiv.

[36]  Tim Salimans,et al.  Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.

[37]  T. Ouarda,et al.  Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator , 2012, Climatic Change.

[38]  Thomas Vandal,et al.  Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation , 2017, Theoretical and Applied Climatology.

[39]  É. Mémin,et al.  Geophysical flows under location uncertainty, Part I Random transport and general models , 2016, 1611.02572.

[40]  S. Sorooshian,et al.  Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network , 2019, Water.

[41]  B. Chapron,et al.  Geophysical flows under location uncertainty, Part III SQG and frontal dynamics under strong turbulence conditions , 2016, 1611.03047.

[42]  Benoit Hingray,et al.  An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment , 2016 .

[43]  F. Takens.  Detecting strange attractors in turbulence , 1981 .

[44]  Bertrand Chapron,et al.  Geophysical flows under location uncertainty, Part II Quasi-geostrophy and efficient ensemble spreading , 2016, 1611.03041.

[45]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[47]  Michele Milano,et al.  Neural network modeling for near wall turbulent flow , 2002 .