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Comment by lenerdenator | Hacker News Reader

Hmmmmmmm.

I have a challenge for the model:

Accurate (within 3deg F) weather predictions for the Kansas City metro more than two days out. As of 2024 these were rarely accurate.[0]

[0]https://www.washingtonpost.com/climate-environment/interacti...

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> Accurate (within 3deg F) weather predictions

Sniff test: ASTM E230 standard tolerances for the venerable Type K thermocouple is +/- 2.2°C or +/- 0.75%, whichever is greater.

Expectations are in need of recalibration if anyone thinks a single number is going to meaningfully achieve that level of accuracy across a volume representing any metro area at any given point in time, let alone two days out.

I think you misunderstood the assignment. The temp predictions in my area wildly swing around, until the day of, and even then I have to adjust for my actual location. And I'm not in Kansas City. The only place that has ~3° accuracy is San Diego. And maybe Antarctica.

The bigger the volume the more accurate temperature you should be able to get.

Do you think 2C global warming is irrelevant because a single thermocouple couldn't accurately measure it?

I am not sure if type K thermocouple are used for meteorological air temperature measurements.

These sensors (based on thermal resistance) for example have an accuracy of 0.2 °C under typical conditions [1].

[1] https://www.ti.com/lit/ds/symlink/tmp1826.pdf?ts=17427997638...

That is the accuracy of one sensor. The law of large numbers comes into play when using forecasts such as this, since accross any reasonably sized metro area you will have hundreds if not thousands or even tens of thousands of weather stations, accross which you can average to bring down error thresholds.

What are your use cases for such level of accuracy on such long time frames?

Is it safe to put my seedlings outside or is there going to be a soft freeze that kills them all in two days.

Then use a thermometer. A city is a large area and can have variances of +/-10 degrees depending on where you are in the city.

a thermometer will tell me if it's going to freeze in 2 days?

here's the scenario. I have started a bunch of seeds inside my house. I am waiting for the "last frost" as per the instructions on the seed packets. Now, how do i use a thermometer to tell if the last frost has passed? You need prediction models that can be accurate out at least a few days with temperature, and a 10% error "at freezing" means my plants either live or die, based on that error.

there's no counterargument, here. "oh just cover the new plants" or "just wait longer" don't work, especially with larger gardens or "farms" or "homesteads". Most of us home-gamers just use environmental clues - number of bugs, buds on dogwood or pecan trees (native ones), when other trees flower. But this year i got a lesson that pecan trees get it wrong, too. They are in the process of leafing out and there was an unpredicted, non-forecast cold snap that took the temperature so close to 32.0F (0.00006C) that i think any plant not equipped for that would have died that night. Now, now i'm fairly certain there's no more frost chance, but it's just a guess.

Now imagine i lived anywhere outside of the subtropics, like nebraska or montana and needed to plant food for livestock or whatever.

Forecasting isn't fine grained enough to get your backyard temp within 2 degrees. What your asking for is silly. The temp across your city is going to vary by more than 2 degrees every single day.

you're still not listening. Once more, the forecast can't tell me with any certainty whether or not there is going to be a frost, specifically in my area, and apparently in Kansas City, as well.

What i am asking for is what is promised by weather forecasters and the models. If it can't say with any certainty if it's going to freeze, it's completely worthless for this common circumstance. Like most people, i don't care if the forecast is 75F and it's actually 70F or 80F (or 68F), but what i do care about is a forecast for lows of 50F and it ends up being 32.15F. If you were a roofer and you were off by 18 degrees, you'd still be in prison.

You're not listening either. Of course it can't. It's not fine grained enough to tell you what's happening in your back yard. Your back yard could be 30 degrees and 2 miles away it could be 35. You're asking for something impossible.

> but what i do care about is a forecast for lows of 50F and it ends up being 32.15F. If you were a roofer and you were off by 18 degrees, you'd still be in prison.

Well, take the actual weather observations for the past year. Take the actual weather observations for the last week. Overlay and slide this week of observations on top of the observations of the past year, until you find the window that matches "the best" — then take the day right after this window and predict that the weather will be just like that (or maybe try to tweak the values a bit).

I wonder how poorly this thing operates, and whether taking several years of history to look at would help much.

Extremely poorly, because forecasting the weather is all about forecasting the deviations from the expected seasonal patterns - the "eddies" in the atmospheric flow which give rise to storm systems and interesting, impactful weather.

According to paper model grid resolution is 1.5 degrees. I do not think it can predict accurate weather in any location. It show global weather trends.

It's lower than many other medium-range AI forecasts, but note that those other models get state-of-the-art with pretty coarse grids, 0.5° or so. The point is that upper atmosphere and broad patterns are smooth, so with ML/AI they don't require high resolution (while simulating them with physical models does require). And at the forecast lag of say 5-10 days, all local detail is lost anyway, so what skill remains comes from broad patterns, in all models. (Some extra skill can be gained by running local models initialized with the broad patterns, for there are clear cases like mountains where fine resolution is useful.)

1.5 degrees is perfectly fine for predicting large-scale (synoptic) weather patterns. They're not just "global trends." But yes, typical global NWP models and their MLWP competitors are run at 0.25 degrees or finer. All forecasts are statistically post-processed and biased-corrected to create local forecasts.

Indeed we have it tough out here. I like the excitement, though, I must say! Funny to find the sentiment here on HN, though!

Where in the city?

Eh, make it MCI or MKC or wherever they're getting the data now.

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