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This might enable simulation but forecasting is a chaotic problem isn't it? It's essentially like trying to predict a stream of random numbers - even if they fo

This might enable simulation but forecasting is a chaotic problem isn't it? It's essentially like trying to predict a stream of random numbers - even if they follow a certain attractor, it's still impossible to know exactly what form they'll take.

zardo 5y ago
Forecasting is done, and it's done by creating ensembles of weather simulations. Weather is chaotic, which is why forecast accuracy rapidly drops off the further out in time you make predictions. But still, they can be accurate enough far enough out into the future to be extremely useful.
- datameta 5y ago
  A few years ago I heard it said that a 5 day forecast has the accuracy of a 48 hour forecast from two decades ago.
  - heavenlyblue 5y ago
    It does seem to me like a lot of that accuracy comes from real-time metric collection from all of the stations around the world (And there are many more stations, too). For example, in London iPhone gives you almost real-time "current weather" state. Stuff like "it's raining now and it's going to stop raining in the next 5 minutes". You can only do that via collecting the radar data in real-time and correlating it with GPS. Not so much prediction.
dragontamer 5y ago
Partial differential equations over multiple dimensions is innately chaotic, with exponential error bounds (the longer any simulation goes, the exponentially bigger the errors get). But getting more-and-more accurate predictions is the goal of any weather modeler. If the exponential error bounds gives you currently 1-day worth of predictions before the models go to crap... maybe an improved algorithm (or 10x more compute power) can get you 2-days worth of predictions instead. Even if you don't get a major change (maybe going from 1 day worth of predictions to 1.1 days of predictions), you might be able to convert that into 1/10th the compute power needed (ex: lower the accuracy down to 1-day prediction but cut back dramatically on the compute-power needed to perform the simulation).

zardo 5y ago

Forecasting is done, and it's done by creating ensembles of weather simulations. Weather is chaotic, which is why forecast accuracy rapidly drops off the further out in time you make predictions. But still, they can be accurate enough far enough out into the future to be extremely useful.

datameta 5y ago
A few years ago I heard it said that a 5 day forecast has the accuracy of a 48 hour forecast from two decades ago.
- heavenlyblue 5y ago
  It does seem to me like a lot of that accuracy comes from real-time metric collection from all of the stations around the world (And there are many more stations, too). For example, in London iPhone gives you almost real-time "current weather" state. Stuff like "it's raining now and it's going to stop raining in the next 5 minutes". You can only do that via collecting the radar data in real-time and correlating it with GPS. Not so much prediction.

dragontamer 5y ago

Partial differential equations over multiple dimensions is innately chaotic, with exponential error bounds (the longer any simulation goes, the exponentially bigger the errors get). But getting more-and-more accurate predictions is the goal of any weather modeler. If the exponential error bounds gives you currently 1-day worth of predictions before the models go to crap... maybe an improved algorithm (or 10x more compute power) can get you 2-days worth of predictions instead. Even if you don't get a major change (maybe going from 1 day worth of predictions to 1.1 days of predictions), you might be able to convert that into 1/10th the compute power needed (ex: lower the accuracy down to 1-day prediction but cut back dramatically on the compute-power needed to perform the simulation).