GitHub - GlobalTopSystems/ai4wrf: Weather HyperConvergent Infrastructure and Appliance with integrated Artificial Intelligence modules. "weather-ai" HCIA is available as WSL2 distribution.

Citation:

Weather-AI software (Version 2.0.2.5) by Global Top Systems (GTS):

"It is important to note that any usage or publication that incorporates or references this software, must include a proper citation to acknowledge the work of the author. This is not only a matter of respect and academic integrity, but also a requirement set by the author. Please ensure to adhere to this guideline when using this software.

Weather-AI software (Version 2.0.2.5) HyperConvergent Infrastructure Appliance (HCIApp) Modular and cross-platform tool for installing and configuring OpenSource Meteorological Software (Computer software).

Getting Started

Clone the repository

git clone https://github.com/GlobalTopSystems/weather-ai

System Requirements

64-bit system
- Darwin (MacOS)
- Various Linux Distros (Ubuntu, Mint, CentOS, etc.)
- WSL (CentOS 7,8 and 8,9 and 10 Streams , etc.) currently being tested
- RHEL Systems (AlmaLinux, Fedora, RedHat, CentOS, etc.)
<150 Gigabyte (GB) of free storage space

Libraries Installed (Latest libraries as of 04/04/2025)

Libraries are manually installed in sub-folders utilizing either Intel or GNU Compilers.

- Libraries installed with GNU compilers

    - zlib (1.3.1)
    - MPICH (4.2.2)
    - libpng (1.6.39)
    - JasPer (1.900.1)
    - HDF5 (1.14.4.3)
    - PHDF5 (1.14.4.3)
    - Parallel-NetCDF (1.13.0)
    - NetCDF-C (4.9.2)
    - NetCDF-Fortran (4.6.1)
    - Miniconda

- Libraries installed with Intel compilers

    - zlib (1.3.1)
    - libpng (1.6.39)
    - JasPer (1.900.1)
    - HDF5 (1.14.4.3)
    - PHDF5 (1.14.4.3)
    - Parallel-NetCDF (1.13.0)
    - NetCDF-C (4.9.2)
    - NetCDF-Fortran (4.6.1)
    - Miniconda
    - Intel-Basekit
    - Intel-HPCKIT
    - Intel-AIKIT

Software Packages

- WRF

   - WRF v4.6.0
   - WPS v4.6.0
   - WRF PLUS v4.6.0
   - WRFDA 4DVAR v4.6.0

- WRF-CHEM

   - WRF Chem w/KPP 4.5
   - WPS v4.6.0
   - WRFDA Chem 3DVAR

- WRF-Hydro Standalone

   - WRF-Hydro v5.2

- WRF-Hydro Coupled

   - WRF-Hydro v5.2
   - WRF v4.6.0
   - WPS v4.6.0

- WRF-CMAQ

   - WRF v4.6.0
   - CMAW v5.4
   - WPS v4.6.0

- WRF-SFIRE

   - WRF-SFIRE v2
   - WPS v4.2

Pre/Post Processing Packages Installed

- WRF

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0
   - ARWPost v3
   - WRF-Python (Conda installed)
   - OpenGrADS
   - GrADS
   - NCAR Command Langauge (Conda installed)
   - Climate Data Operators (Conda installed)

- WRF-CHEM

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0
   - ARWPost v3
   - WRF-Python (Conda installed)
   - OpenGrADS
   - GrADS
   - NCAR Command Langauge (Conda installed)
   - Climate Data Operators (Conda installed)
   - Prep-Chem-SRC v1.5 (GNU only)

- WRF CHEM Tools

   - Mozbc
   - Megan Bio Emiss
   - Megan Bio Data
   - Wes Coldens
   - ANTHRO EMIS
   - EDGAR HTAP
   - EPA ANTHO EMIS
   - UBC
   - Aircraft
   - FINN

- WRF-Hydro Standalone

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0

- WRF-Hydo Coupled

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0
   - ARWPost v3
   - WRF-Python (Conda installed)
   - OpenGrADS
   - GrADS
   - NCAR Command Langauge (Conda installed)
   - Climate Data Operators (Conda installed)
   - WRF-GIS-Preprocessor (Conda installed)

- WRF-SFIRE

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0
   - ARWPost v3
   - WRF-Python (Conda installed)
   - OpenGrADS
   - GrADS
   - NCAR Command Langauge (Conda installed)
   - Climate Data Operators (Conda installed)

- WRF-CMAQ

   - Development Testbed Center (DTC) Model Evaluation Tools (MET) v12.0.2
   - Development Testbed Center (DTC) Enhanced Model Evaluation Tools (METplus) v6.0.0
   - ARWPost v3
   - WRF-Python (Conda installed)
   - OpenGrADS
   - GrADS
   - NCAR Command Langauge (Conda installed)
   - Climate Data Operators (Conda installed)

MacOS Installation

Make sure to download and Homebrew before moving to installation.

cd $HOME

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

brew install git

git clone https://github.com/GlobalTopSystems/weather-ai.git

cd $HOME/WRF-MOSIT

chmod 775 *.sh

./weather-ai.sh 2>&1 | tee weather-ai.log

APT Installation

(Make sure to download folder into your Home Directory):

cd $HOME

sudo apt install git -y

git clone https://github.com/GlobalTopSystems/weather-ai.git

cd $HOME/weather-ai

chmod 775 *.sh

./weather-ai.sh 2>&1 | tee weather-ai.log

YUM/DNF Installation

(Make sure to download folder into your Home Directory):

cd $HOME

sudo (yum or dnf) install git -y

git clone https://github.com/GlobalTopSystems/weather-ai.git

cd $HOME/weather-ai

chmod 775 *.sh

./weather-ai.sh 2>&1 | tee weather-ai.log

Script will check for System Architecture Type and Storage Space requirements.

Once running the script users will be provided with options to select how the Weather-AI will compile and install the various packages.

First option, Which compiler users want to use Intel or GNU compilers.

Second option, Which graphic display package should be installed. GrADS or OpenGrADS

Third option, Auto Configuration. This allows users to have a one-click install

Fourth option, Secondary WPS geography file download choice Author of script recommends selecting "YES" if user is unsure.

Fifth option, Optional WPS geography file download choice. Author of script recommends selecting "YES" if user is unsure.

Last option, Pick which WRF software user wants to install

Run the Services

docker compose -f weather-ai.yaml up

docker compose -f weather-ai.yaml ps

Exports to run WRF and WPS programs

GNU Compilers

export LD_LIBRARY_PATH=$HOME/WRF/Libs/NETCDF/lib:$LD_LIBRARY_PATH

export LD_LIBRARY_PATH=$HOME/WRF/Libs/grib2/lib:$LD_LIBRARY_PATH

export PATH=$HOME/WRF/Libs/MPICH/bin:$PATH

export PATH=$HOME/WRF/Libs/grib2/lib:$PATH
Intel Compilers

source /opt/intel/oneapi/setvars.sh

export LD_LIBRARY_PATH=$HOME/WRF_Intel/Libs/NETCDF/lib:$LD_LIBRARY_PATH

export LD_LIBRARY_PATH=$HOME/WRF_Intel/Libs/grib2/lib:$LD_LIBRARY_PATH

export PATH=$HOME/WRF_Intel/Libs/grib2/lib:$PATH
Make sure to change the name of the WRF Folder to whichever version you are using, WRF_CHEM, WRFHYDRO, etc.

*** Tested on Ubuntu 22.04.4 LTS, Ubuntu 24.04.1 LTS, MacOS Ventura, MacOS Sonoma, Centos7, Rocky Linux 9, Windows Subsystem for Linux with CentOS 9 Stream

Built 64-bit system.
Tested with current available libraries on 04/04/2025, exceptions have been noted in the script documentation.

Estimated Run Time ~ 60 to 120 Minutes @ 10mbps download speed.

Intel compilers take slightly more time to install packages.

Special thanks to:

University of Zadar's Ivan T. - Youtube's meteoadriatic
GitHub user jamal919
University of Manchester's Doug L
University of Tunis El Manar's Hosni S.
GSL's Jordan S.
NCAR's Mary B., Christine W., & Carl D.
DTC's Julie P., Tara J., George M., & John H.
UCAR's Katelyn F., Jim B., Jordan P., Kevin M.,

Citation:

Hatheway, W., Snoun, H., ur Rehman, H. et al. WRF-MOSIT: a modular and cross-platform tool for configuring and installing the WRF model. Earth Sci Inform (2023). https://doi.org/10.1007/s12145-023-01136-y

References:

Appel KW, Gilliam RC, Davis N, Zubrow A, Howard SC (2011) Overview of the atmospheric model evaluation tool (AMET) v1.1 for evaluating meteorological and air quality models. Environ Model Softw 26:434–443. https://doi.org/10.1016/J.ENVSOFT.2010.09.007 Article Google Scholar

Brousse O, Martilli A, Foley M, Mills G, Bechtel B (2016) WUDAPT, an efficient land use producing data tool for mesoscale models? Integration of urban LCZ in WRF over Madrid. Urban Clim 17:116–134. https://doi.org/10.1016/J.UCLIM.2016.04.001 Article Google Scholar

Brown B, Jensen T, Gotway JH, Bullock R, Gilleland E, Fowler T, Newman K, Adriaansen D, Blank L, Burek T, Harrold M, Hertneky T, Kalb C, Kucera P, Nance L, Opatz J, Vigh J, Wolff J (2021) The model evaluation tools (MET): more than a decade of community-supported forecast verification. Bull Am Meteorol Soc 102:E782–E807. https://doi.org/10.1175/BAMS-D-19-0093.1 Article Google Scholar

Carslaw DC, Ropkins K (2012) Openair — an R package for air quality data analysis. Environ Model Softw 27–28. https://doi.org/10.1016/J.ENVSOFT.2011.09.008 Chang V (2017) Towards data analysis for weather cloud computing. Knowl-Based Syst 127:29–45. https://doi.org/10.1016/J.KNOSYS.2017.03.003 Article Google Scholar

Coen JL, Cameron M, Michalakes J, Patton EG, Riggan PJ, Yedinak KM (2013) WRF-Fire: coupled Weather–Wildland Fire modeling with the weather research and forecasting model. J Appl Meteorol Climatol 52:16–38. https://doi.org/10.1175/JAMC-D-12-023.1 Article Google Scholar

Fast JD, Gustafson WI, Easter RC, Zaveri RA, Barnard JC, Chapman EG, Grell GA, Peckham SE (2006) Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J Geophys Res Atmos 111. https://doi.org/10.1029/2005JD006721 Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B (2005) Fully coupled online chemistry within the WRF model. Atmos Environ 39:6957–6975. https://doi.org/10.1016/J.ATMOSENV.2005.04.027 Article Google Scholar

Hluchy L (2016) Software support for the execution of WRF (Weather Research and Forecasting) simulations on HPC infrastructures. https://doi.org/10.1109/eScience.2016.7870932 Hoste K, Timmerman J, Georges A, Weirdt S, D (2012) Easybuild: building software with ease. Proc – 2012 SC Companion High Perform. Comput Netw Storage Anal SCC 2012:572–582. https://doi.org/10.1109/SC.COMPANION.2012.81 Maharjan A, Shakya A (2022) Enhancement of WRF Model using CUDA. Interdiscip J Innov Nepal Acad 1:16–22. https://doi.org/10.3126/IDJINA.V1I1.51963 Article Google Scholar

McCaslin et al (2004) 14.4 A Graphical User Interface to Prepare the Standard Initialization for WRF (2004–84Annual_20waf16nw) [WWW Document]. https://ams.confex.com/ams/84Annual/techprogram/paper_69852.htm. Accessed 3.7.23 Meyer D, Riechert M (2019) Open source QGIS toolkit for the advanced research WRF modeling system. Environ Model Softw 112:166–178. https://doi.org/10.1016/J.ENVSOFT.2018.10.018 Article Google Scholar

Muñoz-Esparza D, Kosović B, Jiménez PA, Coen JL (2018) An accurate fire-spread algorithm in the weather research and forecasting model using the level-set method. J Adv Model Earth Syst 10:908–926. https://doi.org/10.1002/2017MS001108 Article Google Scholar

National Oceanic and Atmospheric Administration (NOAA) (2021) WRF User’s Guide. Retrieved from https://www2.mmm.ucar.edu/wrf/users/docs/user_guide_V4/user_guide_V4.3.pdf. Accessed 2021 Nikfal A (2023) PostWRF: interactive tools for the visualization of the WRF and ERA5 model outputs. Environ Model Softw 160:105591. https://doi.org/10.1016/J.ENVSOFT.2022.105591 Article Google Scholar

Sanyal J, Zhang S, Dyer J, Mercer A, Amburn P, Moorhead R (2010) Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Trans Vis Comput Graph 16:1421–1430. https://doi.org/10.1109/TVCG.2010.181 Article Google Scholar

Shi J, Wu Z, Lu G, Li Y (2013) Design and application of WRF computing platform based on B/S structure. Proc – 2013 Int Conf Mechatron Sci Electr Eng Comput MEC 2013:1804–1807. https://doi.org/10.1109/MEC.2013.6885345 Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR/TN. https://doi.org/10.5065/D68S4MVH Skamarock C, Klemp B, Dudhia J, Gill O, Liu Z, Berner J, Wang W, Powers G, Duda G, Barker D, Huang X (2021) A Description of the Advanced Research WRF Model Version 4.3. https://doi.org/10.5065/1DFH-6P97 Wang YQ (2014) MeteoInfo: GIS software for meteorological data visualization and analysis. Meteorol Appl 21:360–368. https://doi.org/10.1002/MET.1345

Name		Name	Last commit message	Last commit date
Latest commit History 36 Commits
LICENSE		LICENSE
README.md		README.md
script.sh		script.sh
weather-ai.sh		weather-ai.sh

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Citation:

Weather-AI software (Version 2.0.2.5) by Global Top Systems (GTS):

Getting Started

System Requirements

Libraries Installed (Latest libraries as of 04/04/2025)

- Libraries installed with GNU compilers

- Libraries installed with Intel compilers

Software Packages

- WRF

- WRF-CHEM

- WRF-Hydro Standalone

- WRF-Hydro Coupled

- WRF-CMAQ

- WRF-SFIRE

Pre/Post Processing Packages Installed

- WRF

- WRF-CHEM

- WRF CHEM Tools

- WRF-Hydro Standalone

- WRF-Hydo Coupled

- WRF-SFIRE

- WRF-CMAQ

MacOS Installation

APT Installation

YUM/DNF Installation

Run the Services

Exports to run WRF and WPS programs

*** Tested on Ubuntu 22.04.4 LTS, Ubuntu 24.04.1 LTS, MacOS Ventura, MacOS Sonoma, Centos7, Rocky Linux 9, Windows Subsystem for Linux with CentOS 9 Stream

Estimated Run Time ~ 60 to 120 Minutes @ 10mbps download speed.

Special thanks to:

Citation:

References:

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