As we head into the height of the 2017 Atlantic Hurricane Season (climatologically speaking, September is the most active month for tropical weather with the peak for activity on September 10th) I thought I'd take a moment to explain one of the biggest questions I had before studying meteorology: what exactly IS a spaghetti plot and what on earth does a delicious type of pasta have to do with forecasting tropical weather?!
I'm happy to report that, although a giant plate of carbs might be a good way to fuel up before a busy morning of forecasting, spaghetti itself has nothing to do with the forecast, rather just a more conversational name for what it really is: a model ensemble forecast plot.
When forecasters start putting together their predictions for where a specific tropical system will end up, they take a look at different model ensembles. A model ensemble is a group of different runs of the same model with just a few factors tweaked, showing where a system will end up if a number of different things happen. When a group of different models and their outcomes are then placed on the same map, the many different squiggly lines can start to look like a tangled web of tracks, similar to a plate of spaghetti!
So how exactly does this help? While some models tend to be more dependable than others, no forecaster depends on one single model for all the answers. Rather, we look for trends in the different models. If the squiggly lines are all tightly clustered in a certain spot, that means there is a high confidence and agreement between several different models that that is indeed the path the system will take. When the ensemble predictions become more scattered and less uniform, we know that the models haven't quite worked it out yet.
So when we are presenting a spaghetti plot during our forecasts, we aren't trying to make dinner suggestions, we are simply trying to explain where the highest confidence is in the eventual forecast track before the official track is released by the National Hurricane Center.
Hope this helps! Any other weather terms you'd like explained? Email me at email@example.com and you might just inspire the next blog post!