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Messages - dosibl

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Arctic sea ice / Re: The 2017 melting season
« on: March 26, 2017, 05:02:31 PM »
Looking at the DMI 80n graphs temps consistently start rising around day 100, whats the primary driver behind this, increase in solar energy?

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Arctic sea ice / Re: The 2017 melting season
« on: March 24, 2017, 10:52:14 PM »
I think a fair amount of ice north of the Nares has been busy sliding towards the Fram instead of pressing on the arch, a trend which looks set to continue.

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Arctic sea ice / Re: The 2017 melting season
« on: March 20, 2017, 11:42:16 PM »
Thanks for the tips, plenty of things to keep an eye on.

Definitely interested in watching the Beaufort sea, comparing March 20th across the past few years shows how stark the difference is.

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Arctic sea ice / Re: The 2017 melting season
« on: March 20, 2017, 03:29:06 PM »
Before we get too deep into melting season, would some of the more seasoned posters mind giving a handful of things they will be keeping an eye on over the next few months to judge how 'good' or 'bad' the melt is going?

This will be my (and I suspect some others) first melting season so it'd be nice to know what to expect in general, as well as what to watch coming from such an extreme freezing season.

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Arctic sea ice / Re: The 2016/2017 freezing season
« on: December 12, 2016, 03:25:48 PM »
The rub on machine learning for 16/17 freezing is that unless someone made it a big project I don't see it being that useful, perhaps only as good as forecasting ice disposition a few days into the future. With how unprecedented the past few weeks have been, I'd also be wary of a model that relied too much on historic data since it would have a hard time accounting for what we are seeing now.

Its a shame the NIPS slides didn't cover any deep learning, its also a shame that NIPS'16 just ended and I can't find anything about climate modeling in the presentations. Archimid, don't give AlphaGo too much credit, the paper google released on it is a bit technical but the entire system is pretty straightforward to understand. As with all things deep learning, the end result will depend on the quality and quantity of data available.

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