Machine Learning for Stochastic Parameterization: Generative Adversarial
Networks in the Lorenz '96 Model

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

10 September 2019

Papers citing "Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model"

17 / 17 papers shown

Title
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models P. Hess Markus Drüke S. Petri Felix M. Strnad Niklas Boers 33 60 0 03 Jan 2025
CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation Chuanqi Chen Nan Chen Yinling Zhang Jin-Long Wu AI4CE 30 2 0 26 Oct 2024
Online model error correction with neural networks: application to the Integrated Forecasting System A. Farchi M. Chrust Marc Bocquet Massimo Bonavita 21 0 0 06 Mar 2024
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical Distribution Elen Vardanyan Sona Hunanyan T. Galstyan A. Minasyan A. Dalalyan 26 2 0 31 Jul 2023
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification Daniel J. Getter J. Bessac J. Rudi Yan Feng 11 0 0 30 Nov 2022
A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization Nan Chen Yinling Zhang CML 29 15 0 19 Aug 2022
Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model Redouane Lguensat Julie Deshayes Homer Durand Venkatramani Balaji 33 4 0 11 Aug 2022
On the modern deep learning approaches for precipitation downscaling B. Kumar Kaustubh Atey B. Singh R. Chattopadhyay N. Acharya Manmeet Singh R. Nanjundiah A. S. Rao MLAU 22 39 0 02 Jul 2022
The Need for Ethical, Responsible, and Trustworthy Artificial Intelligence for Environmental Sciences A. McGovern I. Ebert‐Uphoff D. Gagne A. Bostrom 19 64 0 15 Dec 2021
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization Lorenzo Pacchiardi Rilwan A. Adewoyin P. Dueben Ritabrata Dutta AI4TS 13 21 0 15 Dec 2021
Combining data assimilation and machine learning to estimate parameters of a convective-scale model Stefanie Legler T. Janjić 21 18 0 07 Sep 2021
Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations Georg Gottwald Sebastian Reich AI4CE 38 37 0 08 Aug 2021
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation Truong-Vinh Hoang S. Krumscheid H. Matthies Raúl Tempone 11 7 0 15 Jun 2021
Controlled abstention neural networks for identifying skillful predictions for classification problems E. Barnes R. Barnes 13 8 0 16 Apr 2021
Non-parametric estimation of Stochastic Differential Equations from stationary time-series Xi Chen I. Timofeyev 6 4 0 16 Jul 2020
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network J. Leinonen D. Nerini A. Berne 21 139 0 20 May 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015