What did meteorologists have to learn to be able to reliably predict the weather accurately?
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Probably data analysis. Turning numbers and graphs into meaningful and actionable information. Also for the ones that developed the software, probably a lot of coding and understanding of how weather works in general
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Thermodynamics and Differential Equations.
Thermodynamics takes the pressure, temperature, composition (how much water vapor, carbon dioxide is present), energy input from the Sun, energy output from the Earth.
The differential equations start off with the initial conditions, (temp, humidity, cloud cover) as a function of altitude for many places on the Earth and see how they’ll change based on energy from the sun and conditions in neighboring areas.
The basics of how the weather works have been known for decades, but to get accurate predictions, you need the initial conditions for many points and computer power to model many closely spaced points. A halving the distance between points means you need 4 to 8 times the amount of computing power needed.
We won’t ever be able to predict the weather accurately many weeks in the future, because the equations that govern the weather are non-linear, meaning a small change in initial conditions will amplify and yield drastically different results in the future. This is the butterfly effect.
That is not the same as predicting the climate. With reasonable certainty I can tell you it will be 80s or 90s with a chance of thunderstorms in Chicago in July, but I won’t be able to tell you what day it will rain or what the high will be more than a week in advance. Climate change models are basically saying, the average July temp will move from 80 F today to 85 F in 50 years.
When did that start happening?
This used to be for specific questions, now people are like “Explain computer science to me”
When computers could start generating models. They can take certain conditions and parameters and run it 1000s of times and take an avg result and say that’s the most likely scenario but even then like some of the other comments said it’s still just an educated guess. The smallest of change can alter the whole system dramatically.
I would say the first big step was when there was a reliable network of observations (temperature, pressure, winds, humidity) and swift enough communication to make a reasonable weather map. Once you have the large scale patterns, you can make reasonable predictions. The big advance after that was the combined advent of powerful computing enabling large numerical models, and satellite observations giving solid coverage and a large amount of input data.
ELI5 explanation: They have a shitload of historical data. Weather tends to follow historical patterns, so what they do is to go “We have this situation, which historical situations matches this best?”. They then look at what those situations looked like a while later, does some kind of average of those historical results and calls that the prediction for, say, now +1 hour.
Then they repeat the process to move further into the future. Of course, the further you go, the more errors will add up and the less reliable the prediction will be.
This is of course vastly simplified, but it’s the basic principle.
I assume they’ll be using AI and pattern matching soon, if they aren’t already.
The other thing that has made a huge difference are satellites. Satellites give meteorologists a really good idea what’s happening now, both from what you can see, but also non visible stuff in the atmosphere, but they also give very accurate starting conditions for the computer models.
If I remember correctly, didn’t Chaos Theory help improve weather forecasting a ton?
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