arome and ecmwf in chart
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for arome model in forecast graph, an arrow shows up pointing forward with the other model; does this mean that arome can only forecast to that point? and why the forecast is so short then?
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Yes exactly, AROME is a high resolution model with a short forecast time frame. AROME shown on Windy has a 1.3 km resolution (Rounded to 2 km by Windy). You can imagine that it requires a high computing capacity which need to reduce the time frame.
https://community.windy.com/topic/8106/huge-differences-between-models/2?_=1599653060626 -
@Rovina
Today, you can get forecast with AROME up to 48h but in the past it was only 24h or slightly more. Computational calculations is clearly the limitation, the more you reduce the grid the more computer has to calculate -
@idefix37 ah ok, i got it. interesting...
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thanks all. but does this mean it is a more accurate model? i mean + computetional power = + accuracy
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@Rovina
As usual in meteo, there is no simple answer to such a question. It all depends what you need as info and what you want to know. If you need to undestand what it happens at synoptic level (1000km or more), for instance if a front will reach soon your country or not, IFS from ECMWF is probably the best model to understand and forecast such a situation.AROME is a regional model, let's say more "local". Thanks to that, you can get access to local forecast that will be difficult to get accuratly with other models. In the post above from @idefix37 , there is a link to another post illustrating the impact of the accuracy of the model for the same location for the temperature. This is a good example to understand what AROME can do and the others not. AROME is also more accurate to predict thunderstoms (timing and localizastion)
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@Yves70 and so i guess + resolution = + computational power?
and is arome specific for europe?
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@Rovina
Arome is specific for Europe and only for some countries in Europe. Look at the following map, this is what AROME is covering :
And yes, if you increase the resolution, for instance by reducing the grid or integrating more data, you will definitely need more computational power
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@Yves70 oh, much thanks.
