Introduction

For some parts of the United States, weather forecasting is vastly different, and inaccurate. In some parts of the country, the forecasting is notoriously inaccurate, ruining weekend plans and outfits on a regular basis. But why is this the case? With the datasets forecast_cities and weather_forecasts, we can take a look into the reason this may be the case. One possibility could be the months or seasons of the year. The winter season is known for its brutal polar vortexes and other weather phenomena, causing cold snaps over certain parts of the US. In addition, the summer is also known for its tropical storms, such as hurricanes, which affect a large amount of the population residing in the American South. As such, I have compiled the mean difference in forecast and observed temperature per season, to see if one season has more inaccurate forecast readings than the others. For the purposes of this table, Winter consists of December, January, and February, Spring is March, April, May, Summer is June, July, August, and Fall is September, October, and November.
season Winter Spring Summer Fall
mean_diff -0.6916594 -0.4154315 -0.2690509 -0.4090684

Analysis

As seen by the table above, the means for the forecast difference are quite close to zero. As such, it appears that a particular season may not affect forecasting as much as we initially expected. However, it is important to note that the dataset weather_forecasts contains over 600,000 observations, so that would result in the means trending closer to zero, as the forecasts in most of the country are largely accurate. However, there are still areas that experience frequent weather forecast inaccuracies, so it is still worth taking a deeper dive into what could be causing these discrepancies. I have created a graph that shows the mean forecast differences per month, in hopes of finding a hidden trend within the data.

January and February seem to be slightly lower in the average forecast difference, but curiously, the month of December does not follow the same trend. This leads us to believe that the season of the year may not have an effect on the accuracy of forecasting. It is true that winter creates difficulties for weather forecasting, due to atmospheric conditions, temperature variations, and snowfall, but it seems to not have a large effect on the accuracy of weather reporting overall. All in all, according to the weather_forecasts and forecast_cities datasets, the months and season did not have a noticable effect on inaccurate weather reporting.