Accurate long-term forecasts of temperature and precipitation are crucial to help people prepare and adapt to these extreme weather events. Currently, purely physics-based models dominate short-term weather forecasting. But these models have a limited forecast horizon. The availability of meteorological data offers an opportunity for data scientists to improve sub-seasonal forecasts by blending physics-based forecasts with machine learning. Sub-seasonal forecasts for weather and climate conditions (lead-times ranging from 15 to more than 45 days) would help communities and industries adapt to the challenges brought on by climate change.
This year’s datathon, organized by the WiDS Worldwide team at Stanford University, Harvard University IACS, Arthur, and the WiDS Datathon Committee, will focus on longer-term weather forecasting to help communities adapt to extreme weather events caused by climate change.
The link to the differnet datasets before processing and after processing : https://drive.google.com/drive/folders/1YDtdF9XUAQrydD9iIaUJsRmImK5_Co5_?usp=sharing
- Melanie Fayne
- Jeff Karanja
- Jupyter Notebook
- VS Code
- Git
Incase of any problems when accessing the project, or any questions, feel free to contact me via my email - melaniefayne33@gmail.com