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fernanqv edited this page Feb 28, 2023 · 9 revisions

Introduction

WRF4G is a framework for executing and monitoring weather and climate experiments with the WRF Modeling System (see this presentation) for an introduction to WRF). It provides a flexible and easy way of designing complex experiments involving many simulations (multiple start/end dates, multi-ensemble simulations, long climate runs and so on). The monitor allows a precise control of the experiment's state, where broken simulations are automatically detected and relaunched at the next submission.

Given a list of computing resources that the user can access, WRF4G submits the experiment to them according to the experiment needs. Users can configure different (Distributed Computing Infrastructures (DCIs) such as HPC, Grid and Cloud resources. The output files are going to be stored depending on the resources used to run the simulations.

Installation

The current version of WRF4G (3.0.2) can be installed in any Linux machine using the wrf4g pip package. If you want to try new features, you can download the source code from the WRF4G github site. More information about the installation process can be found in Installation Instructions

If you are still using WRF4G 2.0, we encourage to upgrade to version 3.0. Documentation of WRF4G 2.0 can be accessed from the old WRF4G site.

WRF4G Documentation

Configuration files and command line interface:

How to create different kind of experiments with WRF4G:

Advanced documentation:

Citation

If you are writing a research paper please cite:

@article{fernandez-quiruelas_large-scale_2015,
        series = {Special {Section}: {A} {Note} on {New} {Trends} in {Data}-{Aware} {Scheduling} and {Resource} {Provisioning} in {Modern} {HPC} {Systems}},
        title = {Large-scale climate simulations harnessing clusters, grid and cloud infrastructures},
        volume = {51},
        issn = {0167-739X},
        doi = {10.1016/j.future.2015.04.009},
        journal = {Future Generation Computer Systems},
        author = {Fernández-Quiruelas, V. and Blanco, C. and Cofiño, A. S. and Fernández, J.},
        year = {2015},
        keywords = {Cloud, Grid, HPC, Hybrid DCIs, Regional climate model, WRF},
        pages = {36--44},
}
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