2 ms·

I think it was in an interview with someone from the ECMWF where they claimed that for each decade of progress, the accuracy of their models improved by one day

I think it was in an interview with someone from the ECMWF where they claimed that for each decade of progress, the accuracy of their models improved by one day. So we're currently at around three days of good forecasting. These models do not use AI, they work by extrapolating, like you say. If you have access to DWD's RADOLAN image data, for example as rendered images through the DWD WarnWetter-App (you need to pay a small one-time fee to access the radar data), you can clearly see how much this extrapolation leaves to be desired (even though it is extremely useful as it is). Actually, almost every German weather data provider which offers radar precipitation predictions is based on the raw data provided by DWD, this raw data can also be downloaded for free at https://opendata.dwd.de/weather/radar/radolan/rw/ https://opendata.dwd.de/weather/radar/radolan/rw/ Anyway, if you look at the predictions, they are pretty simple. As if the wind direction at two different altitudes is determined for each point, and then applied to the current precipitation data. These wind vectors don't change during the (short term, max 2h) prediction, so you see the parts of the image moving at a constant velocity as soon as you're talking about the future. This neglects two things: wind direction will change during these two hours, which is why you as the app user need to check often to verify if it is still accurate, but most importantly this simple model does not take into account the humidity in the air. So sometimes the rain will arrive sooner not because the wind got faster, but because new clouds are starting to build faster in your direction than the old cloud systems get to travel towards you with the wind. And in both these cases AI provides a significant potential of improvement. By looking at more of the surrounding weather dynamics it will be able to predict better what is actually happening in the weather system. Currently we can only improve this by adding more sensors and more frequent radar scans, but AI can really start to interpret the past one-hour-weather and "understand" what is happening there in order to predict what will happen later. And there is a ton of data available for training.

counters 5y ago
A quibble with wind direction and humidity. Really, what's needed is to address the kinematic/thermodynamic parameters more generally that might support (a) maintenance of the existing convection and (b) the propagation of new convective cells. The steering currents really don't change much over 2 hours for an organized system. You can get some rotational motion with a large cyclone but modern OF methods do just fine with that, and you can always remove the divergent component of the flow field. An example of (b) can be found in any Spring season convective outbreak in the Central US; once a squall line congeals along a front you'll see pioneer convection propagate along a vector somewhat orthogonal to the squall line's motion (there are heuristics for the propagation vector that work OK for curved hodographs except in inhomogeneous environments, e.g. Fig 8.10 from Markowski and Richardson). It's the 3D wind shear that matters here, augmented with the lapse rate / profile for (a). It's hard to bullish on the AI applications here until we see them start to account for these larger input parameter spaces. But of course, where is this data going to come from? Mesoscale or convection-permitting models. And if you already have the capability to run these models in a cost-efficient manner, do you need the AI system in the first place?

counters 5y ago

A quibble with wind direction and humidity. Really, what's needed is to address the kinematic/thermodynamic parameters more generally that might support (a) maintenance of the existing convection and (b) the propagation of new convective cells. The steering currents really don't change much over 2 hours for an organized system. You can get some rotational motion with a large cyclone but modern OF methods do just fine with that, and you can always remove the divergent component of the flow field. An example of (b) can be found in any Spring season convective outbreak in the Central US; once a squall line congeals along a front you'll see pioneer convection propagate along a vector somewhat orthogonal to the squall line's motion (there are heuristics for the propagation vector that work OK for curved hodographs except in inhomogeneous environments, e.g. Fig 8.10 from Markowski and Richardson). It's the 3D wind shear that matters here, augmented with the lapse rate / profile for (a). It's hard to bullish on the AI applications here until we see them start to account for these larger input parameter spaces. But of course, where is this data going to come from? Mesoscale or convection-permitting models. And if you already have the capability to run these models in a cost-efficient manner, do you need the AI system in the first place?