About
In this 2018 paper, “Predicting weather forecast uncertainty with machine learning” by professor Scher and professor Messori, shows an early effort to test the feasibility of the technology as a potential replacement to the popular ensemble weather model approach. The ensemble weather model approach, as the name suggest, is similar to having a panel of experts instead of just one expert; the approach produces information that is a composite of different information generated by multiple models given the same input data. Each model caters to different specialization and has their own strength and weaknesses, and by combining information from each model, the approach attempts to generate more accurate information. As such, while it increases the accuracy and precision, it became computationally very expensive to perform in a timely manner. The researchers’ goal was to provide a competitive solution to the computationally expensive ensemble weather model approach by using machine learning technology (Scher & Messori 2018).
Conclusion
In this early assessment, the researchers found that the machine learning approach proved to be less accurate than ensemble but more accurate than any other method (Scher & Messori 2018). Furthermore, their method proved to be more computationally efficient as hypothesized (Scher & Messori 2018).
References
- Scher, S., & Messori, G. (2018). Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society, 144(717), 2830-2841.
-
Comments
Post a Comment