Week 6: Attempts to Find New Literature

Fun Fact: Did you know that in 2023, GraphCast predicted Hurricane Lee three days before the traditional forecasting models did? 

This week was also a little slow in terms of my research. I did read some more sources, including one where I learned more about the systems of the past and how they compare. I learned, specifically, about analog forecasting, the system used before computers. Forecasters would search for the closest match between the weather map of that day and a weather map from the past (an analog). Neural network forecasting (or some at the very least) is based on the principles of analog forecasting.

In addition, I also investigated the cost discrepancies between model types. I learned that GraphCast is about 1000 times cheaper in terms of energy efficiency than traditional models. I already understood that artificial intelligence models were cheaper in the long run, but require more initial costs to set up. I learned that AI-driven tools can generate results from a laptop, in contrast to the multi-million dollar supercomputers required to solve difficult equations and crunch large numbers. I read that the machine learning method developed by Oxford has proven more effective than other methods of rainfall prediction.

I am going to see if I can find more in-depth cost breakdowns of the models to use in my research. So far, cost metrics are usually a subsection within articles whose main focus is elsewhere. Hence, I am going to attempt to find a source where the main subject is the cost breakdowns and discrepancies between AI and NWP models (and maybe the ancient analog system, if relevant).

Sources:

“Neural Net Forecasting Goes Old-School.” Bulletin of the American Meteorological Society, vol. 101, no. 4, 2020, p. 284. JSTOR, https://www.jstor.org/stable/27028166. Accessed 17 Apr. 2026.

Donback, Natalie. “Traditional Weather Forecasting Is Slow and Expensive. Ai Could Help.” Grist, 3 Jan. 2025, grist.org/extreme-weather/how-ai-could-help-predict-climate-fueled-extreme-weather/. Accessed 17 Apr. 2026.

“AI Can Now Outperform Conventional Weather Forecasting – in under a Minute, Too.” World Economic Forum, 14 Dec. 2023, www.weforum.org/stories/2023/12/ai-weather-forecasting-climate-crisis/?utm_source=chatgpt.com. Accessed 17 Apr. 2026.