The WRF-Rainfall-Skill-Evaluation tool helps you assess how well the Weather Research and Forecasting (WRF) model predicts rainfall. This evaluation focuses on the Super Cyclone Kyarr that occurred in 2019. The software uses GPM and TRMM datasets to provide day-wise and threshold-wise metrics, visualizations, and automated report generation.
To get started with the WRF-Rainfall-Skill-Evaluation tool, please follow these simple steps.
- Operating System: Windows, macOS, or Linux
- RAM: Minimum 4GB recommended
- Storage: At least 500 MB free space for installation
- Python: Version 3.6 or above
- Comprehensive evaluation of the WRF model's rainfall predictions
- Automated report generation in PDF format
- Interactive visualizations of data
- Easy-to-use interface for quick assessments
- Supports multiple datasets for broader analysis
To download the application, please visit the Releases page. This page lists all available versions of the WRF-Rainfall-Skill-Evaluation tool.
Download Now from Releases Page
-
Visit the Releases Page: Click on the link above to go to the Releases page.
-
Select the Latest Version: Locate the latest version of the software. Look for the version number in bold.
-
Download the File: Click on the appropriate download link for your operating system. The file will be saved to your computer.
-
Install the Application:
- For Windows: Double-click the downloaded
.exefile and follow the prompts to install. - For macOS: Open the
.dmgfile, drag the application to your Applications folder. - For Linux: Use the terminal to navigate to the downloaded file and run the installation commands provided.
- For Windows: Double-click the downloaded
-
Run the Application: After installation, open the application from your desktop or applications folder.
- Load Your Data: Choose the dataset you want to analyze (GPM or TRMM).
- Set Parameters: Adjust any settings like date range and thresholds as needed.
- Run Evaluation: Click the 'Evaluate' button to start the assessment.
- View Results: Review the metrics and visualizations generated by the software.
- Export Report: Save your findings as a PDF for easy sharing.
The application will generate various metrics to evaluate the WRF model's performance. Here are a few common terms you’ll encounter:
- Correlation Coefficient: Indicates how closely predicted rainfall matches observed rainfall.
- Mean Absolute Error (MAE): Measures the average error between predicted and observed values.
- Root Mean Square Error (RMSE): Highlights the difference between predicted and actual values, giving more weight to larger errors.
Each metric has its importance, offering insights into the model's predictive ability.
The tool overviews visual data presentations, making it easy to understand performance metrics. You’ll find:
- Time Series Plots: Visualize rainfall predictions over time.
- Scatter Plots: Compare observed vs predicted rainfall.
- Histograms: Show the distribution of prediction errors.
Yes, the application is designed for users without technical backgrounds, making it user-friendly.
You can analyze data from the GPM and TRMM datasets for best results.
Yes, there’s a support section within the application. You can also reach out via GitHub issues for help.
- Climate AI
- Cyclone analysis
- Data assimilation
- GPM and TRMM datasets
- Meteorology insights
- Python-based solutions
- Rainfall forecasting improvements
- WRF model evaluations
We appreciate user feedback. If you encounter any issues or have suggestions, please open an issue on the GitHub repository. Your input helps improve the software.
This project is licensed under the MIT License. You can read more about it in the LICENSE file within this repository.
We thank the teams that developed the GPM and TRMM datasets and all contributors who helped make this project a reality.
Download Now from Releases Page
Enjoy evaluating rainfall predictions with WRF-Rainfall-Skill-Evaluation!