Data and codes for a nonhydrostatic alternative scheme (NAS) in dynamical core of atmosphere based on machine learning. In this new version, the randomly sampled training data samples testing data samples from nonhydrostatic simulations in WRF baraclinic wave test are provided. They are processed into a new data structure, which can be directly utilized in training and testing. Follow the instructions in README.txt and download the training and testing data, and the associated codes. Here we provide 3 parts of data and codes: 1, Training and testing data from WRF; 2, Training and testing codes for two machine learning emulators: machine learning and neural network 3, WRF application
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks"...
A new approach based on a synergetic combination of statistical/machine learning and deterministic m...
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of mac...
Data associated with an upcoming paper on the use of neural networks (NN) to emulate a shortwave rad...
Data and code used in a paper submitted to JAMES titled : Implementation of a machine-learned gas op...
Data and code used in a paper submitted to JAMES titled : Implementation of a machine-learned gas op...
Data for "On the choice of training data for machine learning of geostrophic mesoscale turbulence". ...
We assess the value of machine learning as an accelerator for the parameterization schemes of operat...
This includes the code used in the paper "WRF-ML v1.0: A Bridge between WRF v4.3 and Machine Learnin...
FIXED Data and Python code for training and evaluating machine learning models for predicting thunde...
A new approach based on a synergetic combination of statistical/machine learning and deterministic m...
This code demonstrate the construction of surrogate models for the three designated studies in the f...
This repository contains the code and data for the machine learning analysis of "Seasonal Surface Ed...
Data and codes for a deep convolutional residual neural network moist physics parameterization (ResC...
In numerical weather prediction (NWP) models, physical parameterization schemes are the most computa...
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks"...
A new approach based on a synergetic combination of statistical/machine learning and deterministic m...
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of mac...
Data associated with an upcoming paper on the use of neural networks (NN) to emulate a shortwave rad...
Data and code used in a paper submitted to JAMES titled : Implementation of a machine-learned gas op...
Data and code used in a paper submitted to JAMES titled : Implementation of a machine-learned gas op...
Data for "On the choice of training data for machine learning of geostrophic mesoscale turbulence". ...
We assess the value of machine learning as an accelerator for the parameterization schemes of operat...
This includes the code used in the paper "WRF-ML v1.0: A Bridge between WRF v4.3 and Machine Learnin...
FIXED Data and Python code for training and evaluating machine learning models for predicting thunde...
A new approach based on a synergetic combination of statistical/machine learning and deterministic m...
This code demonstrate the construction of surrogate models for the three designated studies in the f...
This repository contains the code and data for the machine learning analysis of "Seasonal Surface Ed...
Data and codes for a deep convolutional residual neural network moist physics parameterization (ResC...
In numerical weather prediction (NWP) models, physical parameterization schemes are the most computa...
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks"...
A new approach based on a synergetic combination of statistical/machine learning and deterministic m...
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of mac...