Week 3: Technical Difficulties and Refined Methodology

This week I focused on setting up my methodology, which I have recently refined to make more actionable.

First, historical weather data will be collected from NOAA, including variables such as temperature, wind speed, pressure, and precipitation. Next, historical forecasts from the Global Forecast System (GFS) will be obtained. I will check the accuracy of the historical forecasts to determine the functionality. An artificial intelligence model will then be implemented using a cloud-based platform such as (GraphCast). This study will reference advanced AI models such as GraphCast to provide context for modern forecasting techniques.

The location used will be Herndon, Virginia. Forecasts will be evaluated at different time intervals, and accuracy will be measured. Finally, results will be analyzed to determine whether AI models can match or improve upon traditional NWP forecasts, especially at longer prediction times.

In terms of implementing my method, I have taken two steps. First, I went onto the National Oceanic and Atmospheric Administration (NOAA) website in order to find the GFS (Global Forecasting System) historical forecasts. Historical forecasts, in this context, meaning historical analysis and predictions of historical data. Second, I have been setting up GraphCast, which is the most suitable model for my needs that can safely function from a home computer. GraphCast does not require a supercomputer like the models weather stations use (though it does take a very long time to load, simply because it is dealing with so much data). I have been troubleshooting errors with GraphCast which are as plentiful as they are annoying. I have managed to get it running. Now, I simply have to make it run in a non-erroneous manner.

Overall, this week has been less about reviewing literature and more focused on setting up the technical groundwork for my method.