I think I may be hitting "exhausted", but that's another matter.
I've had to delete so many load-bearing paragraphs!
re: general sadness
@esty Oh yikes, that’s a lot to get through. It’s awful that you’re having to go through all that in addition to what you’re grappling with outside of academic stuff, too.
re: general sadness
@esty That sounds exhausting, and I’m sorry you’re dealing with that. I hope you’re able to find some ways to rest and/or to do something else for yourself.
@esty Oh nice! Now I’m also wondering how much forecast quality in a given country is related to radar/station coverage and surface area. Weather radars in Canada are relatively sparse but that makes sense given the population density/amount of resources. I guess they probably still focus on densely populated areas, but nevertheless.
@esty I go a bit overboard because I bounce between several weather apps, though I like that Windy lets you compare multiple forecast models. That said, in practice I seem to get best results from staring at the radar and going with my hunches, haha. Storms have a habit of splitting up and avoiding downtown completely, here!
I am yet again lamenting the unpredictability of precipitation.
@esty Ah dang, that’s quite a bit of writing. Wish I had any helpful advice; in any case, I hope it goes okay for you, hang in there!
@esty Oof, that’s coming up! But at least it’ll be out of the way relatively soon? (Small consolation I guess, sorry 😅)
@esty The cool thing is that a lot of this is actually a model-observation comparison! The main part of this paper is that they produce a year-round ice thickness record (previously they were missing the melt season). I can see why they got this in Nature :P
@esty Seems like it! “Strong correlations between SIV and future SIE develop only when the sea-ice-albedo feedback acts to enhance existing SIT anomalies at the onset of the Arctic melt season.”
I’ve just been skimming it, so I don’t know all the details, though.
@esty Oh, I forgot to mention I figured it out in my earlier reply; lead refers to "lead time" (as in lead/lag correlation). Eg. for fig a, September (target day) sea ice extent is strongly correlated with volume from the previous May onwards, while December SIE is strongly correlated with mid-March-July SIV.
@esty It's this figure; a and b were confusing me: https://www.nature.com/articles/s41586-022-05058-5/figures/3
The diagonals threw me off; it finally clicked when I realized that c is a vertical slice of a and b, and the diagonals are necessary because each month has different months leading up to it.
Pronouns: he/him or they/them
PhD student in computational atmospheric physics
Also a teaching assistant!
Research interests: Arctic snow, sea ice, climate, remote sensing
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