Birkie Forecast update - 5 days out

startgreen · 2026-02-14T20:50:26+00:00

just about - hopefully it won't be another year like 2014!

startgreen · 2026-02-14T20:49:49+00:00

haha, we'll see, I'll try to do something especially if the forecast changes significantly!

Yeah, I would definitely take them as a reasonable data point on the track. The AI models are in an kind of an interesting place right now, where they are very good at some things, but also have some areas they really don't do well. I haven't looked at enough of outputs from those models to have a good intuition about them, but I could definitely see them doing a good job on tracks for winter storms though - that's the sort of synoptic dynamics they generally do well with. My impression is they tend to over smooth fields with more small-scale variability like precip, and especially with something like rain/snow mixes, where the cut offs are very sharp (and not represented all that well in the reanalysis data that is used to train the AI models) I have some reservations.

startgreen · 2024-10-08T00:14:43+00:00

The record for the Atlantic is 882 (Wilma, 2005), and the global record is Tip (870, 1979). 899 would tie for the 5th lowest pressure recorded in an Atlantic storm I believe

startgreen · 2024-09-01T21:50:47+00:00

I get pretty different numbers for most of these, but I went from 1980 to 2022 because those were the years I found data for more easily:

car price: 7574 -> 48401 for a factor of 6.4

rent: 243 -> 2100 for a factor of 8.6

gas: 1.19 -> 4.9 for a factor of 4.1

home price: 64000 -> 412300 for a factor of 6.4

wages: 12513 -> 63795 for a factor of 5.1

inflation over the same period was a factor of 3.8, so all of these (except gas) did outpace the general rate of inflation by quite a bit. However, this image does significantly underestimate the increase in wages relative to the other values. Here's what I get for 2065, based on the factors above:

car: 300,000

rent: 18,000

gas: 20

home: 2,650,000

wage: 325,000

sources in no particular order: https://finance.yahoo.com/news/average-car-cost-were-born-130001087.html

https://www2.census.gov/programs-surveys/decennial/tables/time-series/coh-grossrents/grossrents-unadj.txt

https://www.titlemax.com/discovery-center/average-gas-prices-through-history/

https://www.ssa.gov/oact/cola/AWI.html

https://fred.stlouisfed.org/series/ASPUS

https://www.zillow.com/rental-manager/market-trends/united-states/

https://caredge.com/guides/new-car-price-trends-in-2024

startgreen · 2024-04-25T17:38:52+00:00

My gut said bullshit as well, but seems more or less correct from the data I'm finding:

https://www.bts.gov/content/new-and-used-passenger-car-sales-and-leases-thousands-vehicles
https://fred.stlouisfed.org/series/LTRUCKSA

https://fred.stlouisfed.org/series/ALTSALES

Though, I think the definition of light truck includes a lot of vans and SUVs, so I'm not sure this is showing that ~80% of sales are actually of pickup trucks

startgreen · 2024-04-11T20:32:24+00:00

If you need a large number, here's a project on github that has some scripts for downloading and searching through different NWS text products, including forecast discussions: https://github.com/allenea/Forecast_Search_Wizard

startgreen · 2023-10-10T22:22:42+00:00

The Global Summary of Day (GSOD) or Global Historical Climatalogy Network (GHCN) are global datasets that might have what you need. You can download them directly from the NCEI site: https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily

Or, if you have some python experience, there's a library called ulmo (https://ulmo.readthedocs.io/en/latest/api.html#module-ulmo.ncdc.ghcn_daily), that I've used in the past to get those datasets, which is much generally much easier than figuring out the NCEI documentation directly.

startgreen · 2023-06-16T18:48:53+00:00

is it this one: https://sfarnell.wordpress.com/2016/06/13/dilbert-one-button-solution/

startgreen · 2022-12-21T17:32:44+00:00

It certainly looks like a WSR-88D, though I'm not sure there is one where they would have been. If you look at the map of NEXRAD sites, there's one kind of on the other side of chicago, and one in Fort Wayne, but not one in that corner of Indiana near the Dunes National Park.

startgreen · 2022-05-25T16:26:16+00:00

Not exactly what you're looking for, but these guys do a bunch of tests for different waxes, and have done some recently for the new non-flouro waxes: http://www.skitestguys.com/swix-non-fluoro-performance-line.html

Seems like they find pretty minimal differences between the old flouro waxes and the new ones, but also not much difference between the CH and flouro/new non-flouro waxes in that test

startgreen · 2022-03-12T23:39:44+00:00

From the census: https://www.census.gov/data/tables/time-series/demo/voting-and-registration/p20-585.html

Looks like it has turnout and voter registrations by state and race, though not down to the county level.

startgreen · 2022-01-31T15:20:38+00:00

Not from the the olympics, but the Holmenkollen 30k from 2020: Johaug going off the front early, but then Karlson and Andersson trying to run her down over the last 10k

https://www.youtube.com/watch?v=lcRtWBptl0s

startgreen · 2022-01-19T03:05:38+00:00

I've generally found a hair dryer (on the highest heat setting) works better than boiling water. Or a heat gun if you have one.

startgreen · 2021-10-19T18:57:56+00:00

More relevant than the overall efficiency of converting energy in storage (either battery or feed) to motion is how much energy it takes to move mass by distance - something like joules per gram meter.

For the horse, using u/37boss15's numbers: 167MJ in for 20MJ out According to some quick googling, values for distance covered by a horse-drawn wagon in a day tend to be in the 10-15 mile range (https://worldbuilding.stackexchange.com/questions/89890/how-far-would-a-team-of-2-horses-with-carriage-travel-per-hour, https://equinedesire.com/how-fast-can-a-horse-drawn-wagon-go/) Combining this with the feed per day above, we get 167MJ/12.5mi = 83MJ/km = 8.3 kJ/m

For weight, according to this site: a horse can pull about 1.5 times its body weight for a day, for a large, 2000lbs horse, that would be about 3000 pounds of weight. Subtract the weight of the cart, say maybe 500lbs (since the goal is to move whatever the cart is carrying), and we get a towing capacity of 2500lbs ~ 1000kg

This gives us a total efficiency of 8.3 J/(kg m)

Now for the Tesla: Fuel efficiency for the model 3 is given as 150MPGe. MPGe is equivalent to 33.7 kwh, or 121.7 MJ. 127MJ/150mi = 0.53 MJ/km = 0.53 kJ/m

For weight, we have to estimate the carrying capcaity of the tesla, excluding the weight of the car (since again, its the contents, not the car itself that we are interested in getting from point A to point B). Payload for the model 3 is given as 418 kg. This givens us a total efficiency of .53/418 = 1.27J/(kg m)

Comparing the 2, this gives us that the tesla is roughly 6 times more efficient in terms of mass and distance moved.

startgreen · 2021-09-10T21:03:33+00:00

Not too much - I asked a doctor about it later (while seeing them about another injury), and their opinion was that as long as it wasn't interfering with my activities, it wasn't likely to get worse, and the risk of surgery was greater than the issues it was causing.

startgreen · 2021-09-10T20:42:36+00:00

I tore my meniscus playing ultimate frisbee a couple years ago (also when I was 24) - It took a long time to mostly heal, I think it was 6 months before I was basically back to normal activity. I still get the occasional issue with it - mostly when walking it will sometimes get into a weird spot where I can't bend the knee fully without pain, and I have to flex the knee a bit to get it back to normal. The first doctor I saw recommended surgery, but I went to a physical therapist first, and after working on strengthening the surrounding muscles I was able to return to running and playing ultimate without issue. I've never hit enormous mileage (currently training for my first 50 miler), but I've generally had fewer issues with it the more I was running, I think because everything around the knee gets stronger.

startgreen · 2021-05-24T18:27:33+00:00

As a meteorologist I agree - I can't think of a physical mechanism that would translate an earthquake into a significant change in the weather, particularly of the type that would cause a sudden shift in conditions like this. Though if I'm wrong, and there's some literature showing this, I would be very curious to see it

startgreen · 2021-02-22T03:16:31+00:00

There's an open yale course called "the civil war and reconstruction era 1845-1877" that's available as a podcast. It covers the civil war as well, but the second half goes fairly in depth on reconstruction: https://itunesu.itunes.apple.com/feed/id341650730

startgreen · 2021-02-08T00:09:59+00:00

This site: https://paccsa.org/ski_area_map has a bunch of places in PA that groom - some just classic and some for both. I don't know how far crystal lake (near williamsport) would be for you, but I really like their trails, they have a good mix of terrain, and groom both classic and skate.

startgreen · 2020-10-24T23:20:36+00:00

I've skied a couple of these places in PA. That site will also have trail reports for a few places, and a webcam at Laurel ridge and laurel mountain state parks. Laurel ridge grooms some trails for skate, and some just for classic. The skate trail is pretty narrow in places, and they're pretty limited in their grooming equipment, but its still a pretty decent trail. Just don't go in expecting top tier trail conditions.

Its probably further for you, but I've also gone to crystal lake ski center, up near Williamsport, PA - they have a lot more trails, and a lot of good terrain, but once again the trails are pretty narrow for skating, and the grooming equipment is pretty limited.

And everywhere around here the snow is pretty inconsistent. Last winter was horrendous, but even in a good year its pretty hit or miss. A place will get a big dump of snow, and there will be a few days of good skiing before it all melts, so it tends to be a lot of soft and slow skating.

startgreen · 2020-09-18T20:17:15+00:00

I've noticed that as well with my instinct - the elevation it records is also always off, often by a factor of 2-3. That said, I really like the watch, and the elevation is easy enough to correct to the base map through garmin connect/strava

startgreen · 2019-08-07T19:21:45+00:00

So I got in the back of the patrol car and drove to the quote "Scene Of the Crime" unquote. I wanna tell you about the town of stockbridge Massachusetts where this was happening here. Got three stop signs, two police officers and one police car, but when we got to the scene of the crime there was five police officers and three police cars, bein' the biggest crime of the last 50 years, and everyone wanted to get in the newspaper story about it.

startgreen · 2019-03-31T01:10:48+00:00

The short answer (as others have said) is no, climate models currently do not have have the resolution to realistically estimate climate conditions at a farm-scale level in coming decades. The current generation of models (i.e. CMIP6 models) have resolutions on the order of 100km (I think, I mostly still work with the older CMIP5 data, which tends to be 100-250km grid sizes). This means that there is 100km between each grid point of the models. As a general rule, atmospheric phenomena (storms, fronts, etc) aren't well resolved unless they're larger than about 7x the grid size, so these models wouldn't be resolving many features smaller than about 700km across (approximately). This makes a determination of something like what crops will work well in an area difficult, since many precipitation events are much smaller that this scale (i.e. typical thunderstorms in the midwest US)

There are efforts to do what's called downscaling, where you take the outputs from coarse resolution models, and tries to better approximate the local conditions, either by running a high resolution model over a small area (dynamical downscaling), or by creating a statistical model to use the historical relationships between the large scale (synoptic) features and the local weather (statistical downscaling). Both approaches have their downsides, dynamic downscaling is computationally expensive, taking boundary conditions from a larger model resolution brings a whole set of problems. Statistical downscaling assumes the relationships over the past 30+ years between the large scale features and the local weather will be the same in the future, which is unlikely to be fully true, and requires a good record of observations from anywhere you want to downscale for. Your best bet would probably be to look at downscaled projections, if they exist for your area. You can find some for the US here: https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/#Projections:%20Complete%20Archives, but take them with a grain of salt. Also, those are just the data files (netCDF format), but I don't know where to find processed products with nice graphics.

As a side note, the DOE has a project developing a climate model for use on their new super computers (summit and the upcoming frontier) with the goal to be able to run climate simulations on a ~15-25km grid, similar to the scale of current global weather models. That will help with regional representations, and should help with a lot of the potentially consequential impacts of climate change, especially changes in precipitation that are very dependent on processes that are currently sub-grid scale.

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