This contains all of the processed/merged output from the simulations for the paper "Deep learned process parameterizations provide better representations of turbulent heat fluxes in hydrologic models", submitted to WRR
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
This tarball contains the processed datasets used for the analysis of the process networks. It also ...
This is a data repository in support of the publication "State estimation of surface and deep flows ...
These are the data used in this publication. The raw data of input and output (data_in and data_out...
The dataset is the supplement to our publication in Nonlinear Processes in Geophysics (https://doi.o...
One of the main challenges in fluid mechanics and heat transfer is the need for detailed studies and...
Thesis (Ph.D.)--University of Washington, 2021An explosion of new data sources, expansion of computi...
Gaining and understanding flow dynamics have much importance in a wide range of disciplines, e.g., a...
This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models,...
Code used in the paper "Deep learning models for generation of precipitation maps based on NWP" Als...
This repository contains the training code for subgrid-scale viscosity and thermal diffusivity
This release contains the codes and related data to train models with differentiable parameter learn...
In recent years, deep learning has opened countless research opportunities across many different dis...
Model fidelity and accuracy in process representations have been the crux of scientific hydrological...
NetCDF datatset of presented results from the publication titled "On the application of an observati...
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
This tarball contains the processed datasets used for the analysis of the process networks. It also ...
This is a data repository in support of the publication "State estimation of surface and deep flows ...
These are the data used in this publication. The raw data of input and output (data_in and data_out...
The dataset is the supplement to our publication in Nonlinear Processes in Geophysics (https://doi.o...
One of the main challenges in fluid mechanics and heat transfer is the need for detailed studies and...
Thesis (Ph.D.)--University of Washington, 2021An explosion of new data sources, expansion of computi...
Gaining and understanding flow dynamics have much importance in a wide range of disciplines, e.g., a...
This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models,...
Code used in the paper "Deep learning models for generation of precipitation maps based on NWP" Als...
This repository contains the training code for subgrid-scale viscosity and thermal diffusivity
This release contains the codes and related data to train models with differentiable parameter learn...
In recent years, deep learning has opened countless research opportunities across many different dis...
Model fidelity and accuracy in process representations have been the crux of scientific hydrological...
NetCDF datatset of presented results from the publication titled "On the application of an observati...
This dataset is used for the paper “Deep learning for subgrid-scale turbulence modeling in large-edd...
This tarball contains the processed datasets used for the analysis of the process networks. It also ...
This is a data repository in support of the publication "State estimation of surface and deep flows ...
DOI
Add to ORKG