How are the chances of rain/ snow predicted?
In: Mathematics
There is no one “perfect” model for weather forecasting, there are literally thousands of models that you plug information, for example wind speed, humidity, temperature, etc. The different models will output their prediction for tomorrow’s weather. Since there are thousands of different models, you’ll get thousands of different results, some will predict a sunny day, some will predict a hurricane. The meteorologists are trained to recognize the outliers and discard those results and predict the weather based on the most likely results. The way it’s been explained to me is if you look at all the models for next Monday’s weather you’ll get outputs like 1% of tornados and hail, 5% chance of a hurricane, 5% chance of a snowstorm, 14% chance of 110F heat, 25% of rain showers, 50% chance of a nice sunny day, a forecaster might just say 25% chance of rain.
I remember back when Hurricane Sandy hit NYC about a decade ago the weather forecasters knew something historically big was about to happen because even two weeks before the storm struck every single model predicted a huge storm. It was unprecedented that all the models agreed in such a singular manner, this was also why we had such a long time to prepare.
They collect a lot of various measurements all accross the globe (pressure, humidity, wind, satelites pictures,…) and look how the weather is a few hours later and how those measurements changed overtime. Thanks to that, they can deduce what happens before rain/snow, and they apply those observations to today’s measurements and measurement evolution.
Also the percentage likelihood is both the chance that it will rain in an area and how much of the area would be rained on. If there is a 50% chance that rain would fall on 30% of a city. That would be reported as a 15% chance of rain.
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