Evaluating Diverse Machine Learning Models for Weather Forecast Improvement

This opportunity seeks to assess whether incorporating a broader and more diverse range of machine learning models into the IMPROVER weather forecasting blend will deliver measurable forecast improvements and operational benefits that justify the added complexity of implementation and maintenance. The research will evaluate the performance gains, reliability, and practical operational value of integrating multiple ML models compared to the current approach, while considering implementation costs, computational requirements, and system complexity. Successful proposals should demonstrate how diverse ML models can enhance forecast accuracy, extend prediction lead times, improve specific weather phenomena prediction, or provide other operational advantages that outweigh the increased operational burden.