The reason why different forecasting models can have noticeably different predictions

If you check your weather apps or consistently look at forecasting models and their outputs, you may notice that the outcomes change constantly, especially in the longer-term forecasts. Then, once you turn on the TV, the meteorologists are sharing different information as well. This can often cause confusion and sometimes panic if only one outcome is taken as the truth from a source that may not be as credible. For example, the system next week that is expected to bring rain for Central Missouri was being forecasted by some models as a significant snowstorm just over a week ago. You might wonder, how can these outcomes be so widely different?

This all starts with what is known as "initial conditions," which, more simply put, is the current state of the atmosphere at that exact moment. To accurately predict the future, you have to know, perfectly, what the atmosphere is doing at that instant. Unfortunately, it is nearly impossible to efficiently practice this across the globe. Meteorologists attempt to combat this issue by sending weather balloons into the upper levels of the atmosphere one to two times a day to get a clearer picture of the complete vertical profile of the atmosphere in a specific area. Another way that meteorologists attempt to bridge the gap is by using the wide range of weather stations across the country to understand what is occurring at the surface of the Earth. The problem is, weather systems don't just pop up and originate in the United States; they come from all over the globe, especially from over the ocean, where methods of weather observation may be lacking.

The forecasting models have to guess what is happening in these gaps and fill in the blank spaces, even if they don't have a clear picture of what's taking place in that area. So, one model, for example, may predict that the wind speeds or moisture are higher in a system compared to the other model. They are minuscule differences, but the atmosphere is very sensitive to these types of changes. This leads to a "butterfly effect," where these small differences grow into very large discrepancies, and can result in events like the track of a large storm being hundreds of miles apart between those two outcomes.

There is also the resolution of these models. The global or large-scale models have a low resolution, seeing the world in what can be described as big pixels. This leads to areas like Central Missouri being assumed to be one flat surface. Higher resolution models that make calculations on a smaller-scale are able to account for terrain like hills, river valleys, and even cities. Due to the fact that they "see" geography differently, different weather is produced in turn.

So, if you happen to come across a model that is filled with lines and different numbers that might look like spaghetti, that is just a chart of all the possibilities thrown into one visual format. This allows meteorologists to find the pattern that most models agree with while throwing out the outliers with less reasonable outcomes. This data is just guidance; it is the job of the meteorologist to figure out which outcome has the best handle on the atmosphere, so you don't have to.