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With the accumulation of meteorological big data, data-driven models for short-term precipitation forecasting have shown increasing promise.
Hamiltonian systems and transformation in Hilbert space
Bernard O. Koopman · 1931
Earlier work this paper cites.
Microphysics of clouds and precipitation
H. R. Pruppacher and J. D. Klett · 1996
Earlier work this paper cites.
Spectral Properties of dynamical systems, model reduction and decompositions
Igor Mezić · 2005
Earlier work this paper cites.
Spectral analysis of nonlinear flows
Clarence W. Rowley, Igor Mezić, Shervin Bagheri, Philipp Schlatter, and Dan S. Henningson · 2009
Earlier work this paper cites.
The manifold tangent classifier
Salah Rifai, Yann N. Dauphin, Pascal Vincent, Yoshua Bengio, and Xavier Muller · 2011
Earlier work this paper cites.
An introduction to dynamic meteorology
James R. Holton and Gregory J. Hakim · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Analysis of fluid flows via spectral properties of the Koopman operator
Igor Mezić · 2013
Earlier work this paper cites.
On dynamic mode decomposition: Theory and applications
Jonathan H. Tu, Clarence W. Rowley, Dirk M. Luchtenburg, Steven L. Brunton, and J. Nathan Kutz · 2014
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Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz · 2016
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Deep learning for precipitation nowcasting: A Benchmark and a new model
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Learning Koopman invariant subspaces for dynamic mode decomposition
Naoya Takeishi, Yoshinobu Kawahara, and Takehisa Yairi · 2017
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Assessing hourly precipitation forecast skill with the fractions skill score
Bin Zhao and Bo Zhang · 2018
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Numerical Gaussian processes for time-dependent and nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2018
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Applied Koopman theory for partial differential equations and data-driven modeling of spatio-temporal systems
J. Nathan Kutz, Joshua L. Proctor, and Steven L. Brunton · 2018
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Machine learning for precipitation nowcasting from radar images
Shreya Agrawal, Luke Barrington, Carla Bromberg, John Burge, Cenk Gazen, and Jason Hickey · 2019
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Data-driven discovery of partial differential equations
Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz · 2017
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Machine learning of linear differential equations using Gaussian processes
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Jens Berg and Kaj Nyström · 2019
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A characteristic dynamic mode decomposition
Jörn Sesterhenn and Amir Shahirpour · 2019
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