fDOM Weather App

This repository contains the fDOM Weather App, an online tool designed to provide fluorescence Dissolved Organic Matter (fDOM) forecasts based on meteorological and soil data on lakes. The application integrates weather forecast data from the Global Forecast System (GFS) with machine learning models to deliver 7-day fDOM predictions. The tool is updated every 3 hours.

Features

• Real-time Weather Data: Connects to the GFS to download meteorological and soil data for 31 ensemble members.
• Machine Learning Model: Utilizes a pre-trained Random Forest model to predict fDOM levels.
• 7-Day Forecasts: Provides forecasts for the upcoming week based on the latest data.
• Frequent Updates: The application updates its forecasts every 3 hours.

Case Studies

Sau Reservoir

One of the primary sources for drinking water for the city of Barcelona. This reservoir serves as a key site for testing and validating the fDOM Weather App. Its geographical location is 41.9721°N, 2.4030°E, providing a unique setting for monitoring fluorescence Dissolved Organic Matter under varying meteorological conditions.

Lough Feeagh

It's a freshwater lake located in County Mayo, Ireland. Lough Feeagh is an important site for studying fDOM dynamics in temperate regions. Its geographical location is 53.9203°N, -9.5738°W. This case study will be added soon, expanding the tool's applicability and testing under diverse environmental conditions.

Visualization

Explore the interactive visualization here.

How It Works

1. Data Retrieval: The application fetches weather and soil data from the GFS for 31 ensemble members by using Open Meteo.
2. Model Execution: The retrieved data is passed through a Random Forest model trained to predict fDOM values.
3. Forecast Generation: The model outputs fDOM predictions for the next 7 days.
4. Online Access: Users can access the forecasts through the online tool.

Code Overview

app_ensemble_fdom.py

This is the main script that powers the fDOM Weather App. Below is an overview of its key components:

• Data Handling:

  • Downloads meteorological and soil data for the 31 ensemble members from the GFS.
  • Preprocesses the data to ensure it is ready for input into the machine learning model.

• Model Prediction:

  • Loads the pre-trained Random Forest model.
  • Uses the model to predict fDOM values for each ensemble member.
  • Aggregates the results to provide a robust forecast.

• Forecast Output:

  • Generates and formats the forecast results for online visualization and user access.

• Error Handling:

  • Includes mechanisms to manage missing data and ensure smooth operation in case of API downtime.

Current Limitations

This is the first version of the fDOM Weather App. While it provides valuable insights, a full analysis of uncertainties in the predictions is still required to enhance its reliability and accuracy.

Funding Acknowledgment

This tool has received funding from intoDBP. IntoDBP is an EU-funded project that will develop, test, scale-up, validate, and benchmark innovative tools and strategies to protect catchments and minimize human exposure to disinfection by-products (DBP) under current and future climates, without compromising disinfection efficacy, and which could be applied at the global scale. The project will develop its cross-cutting solutions on 4 complementary case studies (CS) combining rural and dense urban areas, from 3 European countries where disinfection by-products are a scientific, technological, and political challenge.

Future Improvements

• Add more case studies.
• Improve weather data by using GEFS model from AWS
• Comprehensive uncertainty analysis to refine prediction confidence.
• Support for other environmental variables beyond fDOM.
• Enhanced visualization tools for a more user-friendly interface.

Getting Started

To use the app:

1. Clone this repository:

   git clone https://github.com/danielmerbet/fdom_weather_app.git

2. Navigate to the project directory:

   cd fdom_weather_app

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app_ensemble_fdom.py

Contributions

Contributions are welcome! If you have suggestions for improving the app or addressing current limitations, please submit an issue or a pull request.

License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

You are free to share and adapt the work under the following terms:

• Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• NonCommercial: You may not use the material for commercial purposes.

For more details, see the LICENSE file or visit the Creative Commons License page.

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