mturmon

Matlab license server goes down, for example

I also enjoy this classic meme. The full version is here: https://www.dourish.com/goodies/see-figure-1.html

Some phrases have stuck with me, like “mandatory defaults“, “they told their users to see figure 1 a long time ago” and the flippant “sometimes we blow it though”.

Like the article hints at, some of the particular strengths of this new measurement:

- frequent revisit, so can track even sub-monthly changes

- the L-band radar is at a wavelength (24cm) that penetrates vegetation canopy, removing a confounder from the measurement

- excellent spatial resolution that is relevant to urban scenes

The data volume is exceptionally high and required a lot of engineering effort. All radars are demanding, but this one was a new high-water mark.

(https://www.earthdata.nasa.gov/news/now-that-nisar-launched-...)

Just as a fun fact, here are some images of the extent of subsidence (due to groundwater pumping for agriculture) in the California Central Valley: https://www.usgs.gov/centers/land-subsidence-in-california/m...

Note in particular the last one, which is a classic. Roads, buildings, and all underground infrastructure is affected. As well as anyone else who uses that groundwater, as well as future users - because come groundwater reservoirs do not recover, the compaction is permanent.

I believe you are correct. The person who delivered this threat is a grandson of William Colby (ex-CIA director, ex-OSS, arch-Catholic).

Elbridge Colby is also Catholic, and some of his religious beliefs factor into his policy preferences. Groton, Harvard BA, Yale Law, hard-to-get service medals.

I’m not saying I like the guy, but his knowledge and background should not be underestimated as some nearby are doing.

Can’t tell if missing /s, but the analogy is:

Jewish/socialist physicists:atomic weapons::”foreign“ AI scientists:automated targeting

I’m not looking forward to the loss of innocence of computer science that is parallel to that of physics from 1945 onward, but here we are.

Replying very late, but with an actual answer.

It turns out that yes, better forecasts is a large part of what motivated the launch of this instrument.

High-spectral-resolution IR spectra at GEO allow estimation of vertically-resolved temperature and water vapor (over large spatial areas, at high temporal cadence), which are then assimilated. Forecasts and nowcasts thus improve.

These "spectra-to-get-temperature-and-water" measurements were pioneered by other instruments in LEO (e.g., NASA's AIRS, https://airs.jpl.nasa.gov/mission/overview/), but LEO does not provide enough coverage to help forecasts.

To understand the benefits of GEO IR spectra, we do "OSSE's" (Observing System Simulation Experiments) to quantify how much improvement you get. You take a "Nature Run", make simulated observations (existing and proposed), and see if there is an improvement. (Since the Nature Run, which you made, provided ground truth, you can judge if there really was an improvement.)

Thankfully, many people have already done this. See: https://www.ssec.wisc.edu/geo-ir-sounder/osse/

In particular, looking at the figure there from Li et al., compare panels:

* (d) -- (Nature Run) - (existing data) ("CNTRL")

* (e) -- (Nature Run) - (existing data + GEO IR)

which both show differences between the Nature Run (NR) and the forecast.

The RMSE improvement (on a CONUS storm) is given as RMS of 0.55 (existing) versus 0.43 (with GEO IR), in degrees Kelvin. So that's 0.12 Kelvin or 0.22 Fahrenheit. Also, and probably more interestingly, the spatial pattern changes.

There are a lot of OSSE's reported on that page for these sounders. NASA is also conducting OSSE studies for a more ambitious multi-spacecraft observing system (https://science.nasa.gov/earth-science/decadal-surveys/decad...).

Studies like this (i.e., OSSEs like the ones above) are one of the main ways we decide how to build the next instruments -- what provides the most benefits vs. cost, which system parameters to push to improve and which are good enough.

They also feature that the IR hyperspectral measurement is new -- 1700 channels in IR for a telescope in GEO seems new to me, but I'm not sure what exists now in this space.

They say they hope to retrieve trace gases at that global scale (seemingly with 30 minute cadence), which I think would be new. Also, they seem to say that this spectral resolution would enable them to retrieve temperature and humidity as a function of height -- not just surface temperature and column-integrated water content ("humidity").

Aha, here's a nice link (https://www.ssec.wisc.edu/geo-ir-sounder/) on exactly this question, pointing out the NASA IR sounders that have existed for many years (AIRS). These instruments get vertically-resolved atmospheric information, but they are not at GEO so their coverage is different. This makes them less useful for NWP.

Getting close to the "why Dropbox when you can rsync" mistake (https://news.ycombinator.com/item?id=9224)

@vicapow replied to keep the Dropbox parallel alive

I meant this more as a rueful acknowledgment of an academic truism - not all citations are read by those citing. But I have touched a nerve, so let me explain at least part of the nuance I see here.

In mathematics/applied math consider cited papers claimed to establish a certain result, but where that was not quite what was shown. Or, there is in effect no earthly way to verify that it does.

Or even: the community agrees it was shown there, but perhaps has lost intimate contact with the details — I’m thinking about things like Laplace’s CLT (published in French), or the original form of the Glivenko-Cantelli theorem (published in Italian). These citations happen a lot, and we should not pretend otherwise.

Here’s the example that crystallized that for me. “VC dimension” is a much-cited combinatorial concept/lemma. It’s typical for a very hard paper of Saharon Shelah (https://projecteuclid.org/journalArticle/Download?urlId=pjm%...) to be cited, along with an easier paper of Norbert Sauer. There are currently 800 citations of Shelah’s paper.

I read a monograph by noted mathematician David Pollard covering this work. Pollard, no stranger to doing the hard work, wrote (probably in an endnote) that Shelah’s paper was often cited, but he could not verify that it established the result at all. I was charmed by the candor.

This was the first acknowledgement I had seen that something was fishy with all those citations.

By this time, I had probably seen Shelah’s paper cited 50 times. Let’s just say that there is no way all 50 of those citing authors (now grown to 800) were working their way through a dense paper on transfinite cardinals to verify this had anything to do with VC dimension.

Of course, people were wanting to give credit. So their intentions were perhaps generous. But in no meaningful sense had they “read” this paper.

So I guess the short answer to your question is, citations serve more uses than telling readers to literally read the cited work, and by extension, should not always taken to mean that the cited work was indeed read.

Totally! If you haven't burrowed in the stacks as a grad student, you missed out.

The real challenges there aren't the "biggies" above, though, it's the ones in obscure journals you have to get copies of by inter-library agreements. My PhD was in applied probability and I was always happy if there were enough equations so that I could parse out the French or Russian-language explanation nearby.

I was an area chair on the NeurIPS program committee in 1997. I just looked and it seems that we had 1280 submissions. At that time, we were ultimately capped by the book size that MIT Press was willing to put out - 150 8-page articles. Back in 1997 we were all pretty sure we were on to something big.

I'm sure people made mistakes on their bibliographies at that time as well!

And did we all really dig up and read Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953)?

Edited to add: Someone made a chart! Here: https://papercopilot.com/statistics/neurips-statistics/

You can see the big bump after the book-length restriction was lifted, and the exponential rise starting ~2016.

My son went to LA-area and LAUSD schools, and the echo of that same commitment from those years in California was still faintly detectable in the 2010s, highly attenuated by Prop 13, as you mention.

On the other hand, right when you think you have her pegged as contemptuous, or boring, this beautiful essay on accidental wonders and catastrophes:

https://loa-shared.s3.amazonaws.com/static/pdf/Didion_Malibu...

Katherine Carpenter Elementary, OP, KS for me, in the same era -- just a couple miles from your school.

I share some of the same disappointment, especially going back and noticing disinvestment in the schools, which were one of the gems of the area.

Here's a nice animation tracking this (covers up to 2022, not sure about 2025): https://grace.jpl.nasa.gov/resources/42/grace-and-grace-fo-t...

The southern end of the central valley (San Joaquin region, whole central valley is outlined in red) is particularly hard-hit by groundwater depletion. Some of that storage does not come back, because the ground compacts after the groundwater is withdrawn.

Thanks for contributing these insights. Having worked with hydrologists for 15 years or so -- water is complicated, and people who say there are simple solutions generally do not know the domain.

A moment's reflection should make this clear. It's such a fundamental resource, touching everything we do. We just tend to take it for granted.

Yeah, and with California's typical topography (relatively younger mountains), there's a lot of sediment at the ready than can fill dams and render them worse than useless -- i.e., costs money, loses capacity fast, alters river and coast.

E.g.: https://en.wikipedia.org/wiki/Matilija_Dam#History

Almost immediately after construction, the dam began silting up. The dam traps about 30% of the total sediment in the Ventura River system, depriving ocean beaches of replenishing sediment. Initially, engineers had estimated it would take 39 years for the reservoir to fill with silt, but within a few years it was clear that the siltation rate was much faster than anticipated.

There are similar sites all over the state. If you happen to live in the LA area, the Devil's Gate Dam above Pasadena is another such (but originally built for flood control, not for storage).

It's just not as easy as GP comment imagines.

Michelson interferometry is also used to measure the spatially-resolved velocity and magnetic field of the solar photosphere: https://en.wikipedia.org/wiki/Solar_Dynamics_Observatory -> Instruments

For real: Earth science is complex. When you have domain experts literally saying the opposite of your guesses, in a section of an outreach webpage devoted to "Myths," reconsider your position.

(Related, and profound apologies for the fb.com link: https://www.facebook.com/DLJCSS/posts/small-quakes-do-not-pr...)

(Source: Work with Earth science domain experts in $dayjob, and am often surprised when my basic intuitive arguments turn out to be wrong.)

I don't believe the video quite says this (I watched the relevant section).

It's worth noting that they are mostly interested in critical phenomena in general, and earthquakes are kind of a drive-by application, treated along with fires and sand piles.

They do hint around the edges, but they don't head-on make the claim for earthquakes that small EQs materially lessen stress buildup and thereby make larger EQ's less likely.

I was looking for a credential of one of the people they interview, to see if they are really a solid earth person or more of a critical phenomena person -- their names aren't easy to find. This particular myth ("small earthquakes relieve stress") is a bit of a stinker in the solid earth community, and I think a solid earth person would be quite careful about their words as they discuss this.

I think you intended this to be a validation of the idea that small quakes relieve stress and therefore lower the chance of a large quake.

The above link does not answer that question. It is relating stress release to "fault strength", or the maximum shear stress that can be withstood by the fault. There is an incidental relationship with depth that plays a role.

The video linked nearby (on criticality) also does not address the question at issue.

I'm only replying because I work adjacent to this area, and my understanding is that the idea that small EQ's release stress is a myth. Here [1] is another link, listed as #1 in the "Myths" category. And you can dig up quotes from none other than Lucy Jones [2] saying that this is a myth.

I don't work directly in this area, so I'm not willing to say absolutely no. But I'd really like to see a head-on reference supporting the claim that it's not a myth.

[1] https://earthquakes.berkeley.edu/outreach/faq.html

[2] https://en.wikipedia.org/wiki/Lucy_Jones

I agree with our assessment of Quanta. I used to enjoy reading their articles, but the clickbait title formula has put me off. Also their status as a mouthpiece of the Simons foundation grantees.

I feel like I’m being a bit curmudgeonly, but I don’t read them much any more.

This reminds me of `PrSAT`, a satisfier for probabilistic statements. ("Does a distribution exist that satisfies the following constraints?").

See: https://fitelson.org/PrSAT/, and the linked paper: https://fitelson.org/pm.pdf

The paper starts off slow, but have patience to read up to section 4, Applications, which is kind of surprising.

Yeah, in 1988 the Internet appeared like a research network that connected universities. No money was directly at stake and the systems harmed didn't appear critical. Related to what Thomas says above, part of the response to the incident was to partition the Internet for a few days [2] - I don't know if such a thing would be possible now.

But looking into the specifics again after all these years [1], I read:

"The N.S.A. wanted to clamp a lid on as much of the affair as it could. Within days, the agency’s National Computer Security Center, where the elder Morris worked, asked Purdue University to remove from its computers information about the internal workings of the virus."

and that CERT at CMU was one response to the incident [2].

So there is a whiff of the incident being steered away from public prosecution and towards setting up security institutions.

Robert Morris did get a felony conviction, three years probation, and a $10K fine. As for hn users, aside from pg, Cliff Stoll has a minor role in the story.

[1] https://archive.nytimes.com/www.nytimes.com/times-insider/20...

[2] https://en.wikipedia.org/wiki/Morris_worm#Effects

Do you really think these cuts are done with the intent of positive effects on the space and earth science enterprise?

The model was that NASA did stuff that was pathfinding, typically in response to science objectives, and that commercial applications would follow. By design, it’s not mass production.

This works for Earth science stuff like land surface monitoring, methane monitoring, land subsidence, groundwater monitoring, sea level rise, etc. NASA developed these remote sensing technologies that have made it into commercial applications.

So there is a synergy between NASA science and commercial space. It does not have to be either/or.

FTA, Michael Garcia, ex Hubble Project Scientist:

> What surprised me was that initial budget request, which basically said, we, America, are never going to launch another space telescope. We're going to turn off 95% of the ones we have in orbit. We are getting out of that business, we don't want to ask those questions anymore.

So this hits on a few key points. It’s not just that this budget request is tossing out perfectly good technology maturation plans for getting the next large space telescope built (https://science.nasa.gov/astrophysics/programs/habitable-wor...), among other goals.

It’s also (see the second sentence) that the budget request will result in de-orbiting perfectly-functioning operational missions like OCO-2 (https://ocov2.jpl.nasa.gov/), and deactivating perfectly functional instruments onboard ISS that are returning data continuously right now. It’s a multi-billion dollar self-own. There’s no sense in it. (https://www.planetary.org/charts/fy-2026-active-mission-canc...).

For many of these missions, having a long-term continuous dataset is super-valuable —- obviously so for a CO2 monitoring mission, or missions monitoring land surface temperature, vegetation/forests, etc. They are built, launched, and returning data. It’s all gravy at this point.

As nearby commenters note, this has nothing to do with cost savings. It’s more like a mix of pure spite, owning some libs in Maryland and California, and an object lesson in who the boss is.

He had an incredible life and was such an original thinker. Just the list of people he knew (since childhood) is mind-blowing. I recommend his voluminous essay collection United States or the more personal Palimpsest.

And a large billboard blocking the view

There are straightforward emulation settings in which a trained emulator can be more accurate than a single forward run, even when both training and "single forward run" use the same accuracy settings.

Suppose you emulate a forward model y = F(x), by choosing a design X = {x1, ..., xN}, and making a training set T = {(x1, y1), ..., (xN, yN)}.

With T, you train an emulator G. You want to know how good y0hat = G(x0) is compared to y0 = F(x).

If there is a stochastic element to the forward model F, there will be noise in all of the y's, including in the training set, but also including y0! (Hopefully your noise has expectation 0.)

(This would be the case for a forward model that uses any kind of Monte Carlo under the hood.)

In this case, because the trained G(x0) is averaging over (say) all the nearby x's, you can see variance reduction in y0hat compared to y0. This, for example, would apply in a very direct way to G's that are kernel methods.

I have observed this in real emulation problems. If you're pushing for high accuracy, it's not even rare to see.

More speculatively, one can imagine settings in which (deterministic) model error, when averaged out over nearby training samples in computing y0hat, can be smaller than the single-point model error affecting y0. (For example, there are some errors in a deterministic lookup table buried in the forward model, and averaging nearby runs of F causes the errors to decrease.)

I have seen this claim credibly made, but verifying it is hard -- the minute you find the model error that explains this[*], the model will be fixed and the problem will go away.

[*] E.g., with a plot of y0hat overlaid on y0, and the people who maintain the forward model say "do you have y0 and y0hat labeled correctly?"