Accurate long-term forecasts of temperature and precipitation are an essential tool for helping people and communities prepare for and adapt to 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.
In this project,focus on longer-term weather forecasting to help communities adapt to extreme weather events caused by climate change by generating forecasts of temperature and precipitation for one year