About The Project

The project presents MeteoSuMisura, a platform capable of automating the entire process of running high-resolution weather models for a specific geographic area. In particular, it integrates the Global Forecast System (GFS) and the Weather Research and Forecasting Model (WRF), automating all operational phases: from downloading global data, to their pre-processing and conversion into compatible formats, up to running local high-resolution simulations.

The platform offers an interactive web visualization system that makes consulting forecasts intuitive. The web interface is built using modern technologies such as Leaflet for interactive mapping, Bootstrap for responsive design, and jQuery for dynamic UI interactions. For the base map, OpenStreetMap tiles are used via the Stadia Maps tile server, ensuring a detailed and up-to-date cartographic background. These tools allow users to explore weather data, navigate the forecast timeline, and interact with high-resolution meteorological maps in a user-friendly environment.

Finally, the platform has been used for running and visualizing weather simulations for the Gulf of Salerno.

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Prerequisites

To run the project correctly, you need to have the following components installed:

• Docker: for isolated and portable execution of the simulation environment and auxiliary services.

• Python 3.8+: used for automation scripts and workflow management.

• Node.js: used to run the web platform.

Repository Structure

• download/
  Scripts and files to download and configure the WRF model on your local machine.

• wrf-docker/
  Configuration and files to create a ready-to-use Docker container with all the necessary tools to run WRF.

• website/
  Interactive web front-end of the platform, developed with Node.js and JavaScript. Includes all components for dynamic visualization of weather forecasts generated by the WRF model, as well as backend APIs and static resources.

• docker-env/
  Complete configuration to set up and launch the platform in a Docker environment, including startup scripts, entrypoint, and configuration files.

• script/
  Collection of Python and shell scripts to automate the workflow: processing, execution, and management of data generated by WRF.

• public/
  Static resources and files for the interactive visualization of weather data produced by the model via the web front-end.

• examples/
  Example configuration files (namelist.input, namelist.wps, config.json) useful for quickly customizing and launching the platform.

Quick Start: Running Your Own MeteoSuMisura Instance

Follow these steps to launch the MeteoSuMisura platform from scratch using Docker:

1. Clone the repository

   git clone https://github.com/your-user/WRF4Salerno.git
   cd WRF4Salerno

2. Build the Docker image

   Go to the docker-env folder and build the Docker image:

   cd docker-env
   docker build -t meteosumisura .

3. Configure the input files

   Prepare the necessary configuration files:

   • namelist.input and namelist.wps (for WRF and WPS)
   • config.json (for platform configuration)

   You can find examples of these files in the examples folder.
   For more details on configuring the namelist files, see the official WRF documentation.

4. Start the Docker container

   Start the container, mounting the necessary input files (adapt the paths to your local files):

   docker run -d --name <container-name> \
     -p 80:3000 \
     -v $(pwd)/docker-env/WRF/run/namelist.input:/tmp/files/namelist.input \
     -v $(pwd)/docker-env/WPS/namelist.wps:/tmp/files/namelist.wps \
     -v $(pwd)/docker-env/config.json:/tmp/files/config.json \
     meteosumisura

5. Access the web interface

   Once the container is running, open your browser at http://localhost to view the platform.

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