cos2pi

Allez!

A wealth of knowledge here, especially helpful for wheelbuilding and checking the compatibility of archaic sizing systems. Lennard Zinn is another great reference in bike maintenance: https://lennardzinn.substack.com/

Haha, thanks for sharing. Really enjoyed how you captured the essence of Freak on a Leash.

Perhaps https://news.ycombinator.com/item?id=27950416 ?

For the interested, real-time data from the ongoing aircraft reconnaissance into (now) Hurricane Ida can be seen at https://www.tropicaltidbits.com/recon

Windy doesn’t make forecasts, rather it visualizes output from forecast models.

It's worth mentioning here that heatwaves are the leading cause of weather-related death in the US for the past 30 years.

https://www.weather.gov/hazstat

I'm not advocating for the personification of all extreme weather events, but the lethality of heatwaves is often overlooked.

Don't forget the oud!

https://www.youtube.com/watch?v=tsHeDpGcZVM

Give eBird [0] a try, also from the Cornell Lab of Ornithology.

[0] https://ebird.org/home

Umberto Eco as well, who wrote a book on the topic: https://mitpress.mit.edu/books/how-write-thesis

The article rightfully mentions Dickey Kerr as a hero of the series by winning two complete game starts.

Curiously, Shoeless Joe Jackson set a World Series hit record that was unbroken until 1964 and had the highest series batting average of either team. He also hit the only home run of the 1919 World Series.

Agreed, this has repeatedly been one of the biggest failings in communicating the reality of anthropogenic climate change.

Climate change is not a single event, nor a cluster of events, that occur(s) over a summer. Strong hurricanes have occurred well before Dorian [1]. So have heat waves [2].

Extreme weather happens. The change in the frequency of (some) extreme events can be attributed to anthropogenic climate change, but a single event cannot.

[1] https://en.wikipedia.org/wiki/1935_Labor_Day_hurricane

[2] https://en.wikipedia.org/wiki/1936_North_American_heat_wave

I'm the same way with scp, I never remember the order and then it changes if you're uploading or downloading.

Also, an obligatory xkcd: https://xkcd.com/1168/

I had a lot of fun with this.

I surprised myself by remembering some symbols I haven't used in a while but was stumped on a few I should have known (nth roots).

Thankfully typesetting here meant math formulas, not minipage layouts.

https://news.ycombinator.com/item?id=9224 seems relevant here.

Who is the author of this blog? My searching hasn't been able to turn up a name.

Rather than try and set up MADIS, look into something like the siphon [0] Python module.

MADIS is a bit dated, data dissemination that uses OPeNDAP [1] and ERDDAP [2] is much friendlier.

[0] https://github.com/Unidata/siphon

[1] https://opendap.github.io/documentation/

[2] https://coastwatch.pfeg.noaa.gov/erddap/information.html

For hurricane forecasts issued by the NHC, you can see the official error statistics here [0]. Note that 96 and 120-hour forecasts were so poor prior to year 2003 that they were not issued.

Note these error statistics do not represent true model error as the official track and intensity forecast -- while informed by model output -- are determined by human forecasters.

[0] https://www.nhc.noaa.gov/verification/verify5.shtml

What Singer book was it that you read?

What are the best ways, if any, to test for the convergence (or pseudo-convergence) of the Markov chain to the equilibrium distribution?

I don't get anything out of this, frankly. There's no scientific context, exposition of the data/data collection methods or even what ppm means.

For the interested, the ESRL Global Monitoring Division (https://www.esrl.noaa.gov/gmd/ccgg/index.html) is a more complete treatment of work being done in global greenhouse gas monitoring -- charts, maps and data included.

Computer models certainly produce forecasts, but humans are needed to interpret these forecasts.

At each National Weather Service office, forecasters typically use model output to guide their daily forecast products, such as temperatures and precipitation probabilities. However, these forecasters are aware of smaller-scale nuances of their forecast area, such as topographic influences, land-sea interactions or urban heat island effects, which models may struggle to resolve. Human knowledge of model limitations helps to produce a better forecast.

A further example would be hurricane forecasting, where National Hurricane Center forecasts typically outperform most models (see http://www.nhc.noaa.gov/verification/verify6.shtml for starters).

Gravity waves are waves in a fluid that obtain their restoring force from their buoyancy relative to the surrounding fluid. See [0] for the gory details.

Some examples of gravity waves in the atmosphere: [1] [2]

[0] http://glossary.ametsoc.org/wiki/Gravity_wave [1] http://cimss.ssec.wisc.edu/goes/blog/archives/2051 [2] https://www.youtube.com/watch?v=yXnkzeCU3bE

If you'd like to submit your own weather observations from anywhere on the globe, the National Severe Storms Laboratory has created an app[0] to report them.

[0] http://mping.nssl.noaa.gov

You can follow the event per livestream on 3 February 2016 at 2.45 p.m. CET.

The program should begin at any time.

EDIT: Err, CET... UTC+1. So, perhaps it is over?

Given these points, what protocol(s) do you think is the best candidate for development?

I always think of Feynman's minority report [0] on the Challenger Disaster when these types of risk assessments pop up.

[0] http://science.ksc.nasa.gov/shuttle/missions/51-l/docs/roger...

For those interested, the monthly report the article refers to is the NOAA/Climate Prediction Center ENSO discussion:

http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/en...

These plots are very difficult for me to read. I'm not sure why the color maps span 90F+ when discussing nighttime radiative cooling on the order of 12F. I'm also not sure how a regression line with a negative slope constitutes a "weak" temperature inversion -- it's not an inversion at all.

It would be interesting to see the station data integrated with an urban land cover map. Surface air temperature is sensitive to the locations of buildings, roads, soil type/water/ground cover, etc., not just elevation.

Because now they can write a blog post about it, which duly appears on HN's front page.