![]() ![]() It even allows you to pull up archived imagery. In addition to conventional visible, infrared and water vapor imagery, you can also view GOES global lightning mapper data. You can choose between GOES-East, GOES-West, Himawari-8 (west Pacific), even JPSS for views of polar regions. CIRA/RAMMB satellite slider: A spectacular site for satellite imagery.However, meteorologists will always come back around to the usual map showing one forecast track with an area of uncertainty around it, because that is the most reliable forecast. Spaghetti plots are popular because they're easy to produce and put up on the screen, allowing meteorologists to quickly explain what you are looking at and letting you to see all of the potential tracks a tropical system could take. Identifying which model forecasts are more trustworthy than others, watching trends in changes to the forecast tracks, identifying potential features that could steer the tropical system in another direction, and determining where the storm might go based on its strength are some of the other major factors that go into a meteorologist's forecast. So why not just use spaghetti plots instead of a single forecast track with an area of uncertainty surrounding it? While identifying a most likely forecast track is somewhat easy on a spaghetti plot, those model forecasts are just some of the tools meteorologists use to make their own forecast. This can help shape the level of certainty in a tropical system's forecast, with more of an "either or" track scenario versus "it could be any one of these lines scattered about the map". There can also be scenarios where there are "multiple camps" of forecast model plots, where these plots cluster into two or three possible tracks. A dozen models show a storm staying out over open water, but a couple show a hurricane landfall in Miami? Time to fire up the hype train! While it can be tempting to go after the high impact solutions to get more attention, it is almost always better to use the scenario shown by the vast majority of the forecast models, because that has a much greater chance of being a more correct forecast. One other problem with spaghetti plots is cherry-picking. Without labels, it is hard to tell whether you are looking one of the more reliable GFS or ECMWF model forecasts, or if it is one of those more basic, rough track estimates. Some of the plots aren't really even models so much as a rough track estimate based on one or two variables. These are important, because not all models are created equal. Sometimes you will see these lines labeled with a bunch of acronyms that represent the names of each of the forecast models used on the map. The inverse works as well, with different forecast tracks going all over the place showing low forecast confidence. If there are a bunch of weather models showing a similar forecast track, the plots will be tightly clustered together, signifying good forecast confidence. The biggest upside is conveying the storm track's uncertainty. Also, it's a lot more fun to say "spaghetti plot" than it is to say "model ensemble forecast plot". ![]() While these plots are a handy tool for showing potential paths the tropical system will take, there are upsides and downsides to them.īut why is it called a spaghetti plot? That has a somewhat obvious and straight-forward answer: because a bunch of the forecast plots placed on the map can be so scattered about that it looks like a plate of spaghetti. One of the most popular tropical forecast maps you see floating around on social media and on the local newscast is the spaghetti plot, which shows many different model outputs of the tropical system's potential track. What do the rainbow-colored squiggly lines on a tropical forecast map mean, and why are they called "spaghetti plots"? ![]()
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