2 ms·

I don't get why the put nowcast in quotes. Nowcasting is pretty common term in meteorology. It's also cool that they could get this far without physics. Of c

I don't get why the put nowcast in quotes. Nowcasting is pretty common term in meteorology. It's also cool that they could get this far without physics. Of course, "HRRR model begins to outperform our current results when the prediction horizon reaches roughly 5 to 6 hours". Simple associations that could be discovered by neural networks work in the short term, but the atmospheric physics is needed to understand the long term evolution of storms.

franch 7y ago
Also, physics-based models (Numerical weather predictions NWP) assimilate much more data than just the last few radars scan. The assimilation phase takes a couple of hours, but when the physics kicks in, the NWP models win hands down.
- labster 7y ago
  I've been out of the field for about a decade now. So how often are they running data assimilation into models now? It was basically just 0Z and 12Z back then, because it's computationally intensive. (And of course, you only have radiosondes every 12 hours, and satellites only get you so far.) As are model runs. Like, of course NWP models win. It's not just the physics, it's all of the other assimilated obs that are advecting over your area of interest. But it's the problem is always always computation time, to an extent that most people on HN won't get. Maybe the finance guys. But you have to process a mountain of new data, then run the model very quickly for it to be any use at all to the public. We should use reanalysis for nowcasting, that would be super accurate. /s
geoalchimista 7y ago
I think this blog article did not frame the novelty of this study very well. It is not the nowcast that is the problem, it is getting nowcast at a spatial resolution higher than weather forecast models. But in that case, the limitation is more about computation rather than physics. What their U-Net does seems to be a fancy way of doing statistical downscaling. Needless to say, this should not be interpreted as machine learning can replace physics in weather forecast or operational meteorology (see Lorenz 1963 paper). It shines as a great data assimilation technique though.

franch 7y ago

Also, physics-based models (Numerical weather predictions NWP) assimilate much more data than just the last few radars scan. The assimilation phase takes a couple of hours, but when the physics kicks in, the NWP models win hands down.

labster 7y ago
I've been out of the field for about a decade now. So how often are they running data assimilation into models now? It was basically just 0Z and 12Z back then, because it's computationally intensive. (And of course, you only have radiosondes every 12 hours, and satellites only get you so far.) As are model runs. Like, of course NWP models win. It's not just the physics, it's all of the other assimilated obs that are advecting over your area of interest. But it's the problem is always always computation time, to an extent that most people on HN won't get. Maybe the finance guys. But you have to process a mountain of new data, then run the model very quickly for it to be any use at all to the public. We should use reanalysis for nowcasting, that would be super accurate. /s

geoalchimista 7y ago

I think this blog article did not frame the novelty of this study very well. It is not the nowcast that is the problem, it is getting nowcast at a spatial resolution higher than weather forecast models. But in that case, the limitation is more about computation rather than physics. What their U-Net does seems to be a fancy way of doing statistical downscaling. Needless to say, this should not be interpreted as machine learning can replace physics in weather forecast or operational meteorology (see Lorenz 1963 paper). It shines as a great data assimilation technique though.