
Using Machine Learning to “Nowcast” Precipitation in High Resolution - luthfur
https://ai.googleblog.com/2020/01/using-machine-learning-to-nowcast.html
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franch
I chime in on this because I'm working on deep learning for precipitation
nowcasting using radar for my Ph.D. I was very excited when google released
the press statement at NeurIPS about their work in this area. Unfortunately,
after reading the paper, I have to say that their approach is fairly basic.
Basically they threshold the precipitation in 4 thresholds (no rain, light,
medium, heavy) and then use a U-Net like architecture, treating it as a
classification problem. I think that the works of Shi et al are much more
interesting in this regard:

[https://papers.nips.cc/paper/5955-convolutional-lstm-
network...](https://papers.nips.cc/paper/5955-convolutional-lstm-network-a-
machine-learning-approach-for-precipitation-nowcasting.pdf)

[http://papers.nips.cc/paper/7145-deep-learning-for-
precipita...](http://papers.nips.cc/paper/7145-deep-learning-for-
precipitation-nowcasting-a-benchmark-and-a-new-model.pdf)

What I think is that Google wanted to use a lighter model that can be applied
to the whole continental US. I expect them to integrate this in google
assistant, like: "hey google, tell me when it's going to rain"

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labster
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
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
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

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londons_explore
In the UK, netweather.tv shows weather radar with a 5 minute temporal
resolution, 50 meter spacal resolution, and a 10 minute latency (less if you
pay I think).

I regularly use it 'by eye' to predict when a big band of rain is coming. I
can very effectively figure out if there will be more or less rain 5 minutes
from now. "Shall I walk to the car now, or should I wait 5 mins?".

In comparison, the results of this work seem disappointing.

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luthfur
A physics-free approach to precipitation nowcasting using a CNN.

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denimboy
Sounds a lot like darksky.net they have an interesting story starting from
kickstarter to getting acquired by longnow.

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signa11
dup:
[https://news.ycombinator.com/item?id=22051889](https://news.ycombinator.com/item?id=22051889)

~~~
dang
On HN, it's not a dupe if a story hasn't had significant attention yet. See
the FAQ:
[https://news.ycombinator.com/newsfaq.html](https://news.ycombinator.com/newsfaq.html).
Allowing reposts in such cases is a way of mitigating the randomness of
/newest and giving good stories multiple cracks at the bat. If you submit a
lot of good stories (which you have!) it evens out in the long run.

By the way, from the IDs you can tell that this submission was earlier than
yours. It made the front page later because we put it in the second-chance
pool (described at
[https://news.ycombinator.com/item?id=11662380](https://news.ycombinator.com/item?id=11662380)).

~~~
signa11
ah got it thanks !

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m3kw9
Now cast, let me look outside

