Weather forecasts have become more accurate

People seem to have different opinions on how good forecasts are. I think it likely depends on which model your forecast source of choice pulls from. I notice that the weather on my Apple Watch corresponds exactly to what GFS says. GFS is OK for medium range, but I don't find it too useful for shorter range. NAM is better for a day or two out. HRRR is better for a few hours out.

Rather than letting some aggregator simplify the weather for you, you can just look at the raw data yourself: https://weather.cod.edu/forecast/

For big events, the media briefings by the National Weather Service are good resources. But they often stop the briefings early; a few weeks ago we had a high probability of a large amount of snowfall. The updates stopped at like 9AM, the snow was forecast to start around 1PM. Watching the short term models showed that the probability for snow was decreasing (NYC was just below the snow/rain line), and indeed we got pretty much no snow. (It snowed, but it didn't accumulate and the change to rain happened early.) To be fair, the briefing from the weather service said that the changeover time between snow and rain was very uncertain and that it would be the difference between a little rain and major snow event. But my point is, you can always go get yourself some more data; the closer you get to the event, the more accurate the forecast is.

(I don't know if any of you watch Skip Talbot, but he was looking at helicity swaths on the HRRR a few hours out, found a big one, and where HRRR predicted the strong rotation in the storm is pretty much exactly the path of a major tornado. HRRR is never going to be perfect, but it is right a lot.)

I remember reading in The Signal and The Noise* that people _think_ that forecasts are bad if it rains, but the chance of rain was reported as below 50%. Getting rain when the forecast told you there would probably not be rain is annoying; getting a sunny day when the forecast predicted likely rain is a pleasant surprise.

To get what people judge to be a ‘good forecast’, the chance of rain has to be adjusted to be wildly too high - so that’s what consumer-focused forecasters do.

* https://en.wikipedia.org/wiki/The_Signal_and_the_Noise

Not in Boston, they've become far far worse. I presume because of climate change but still has become frustrating in recent years.

Like many other people are commenting, I have subjectively felt that rain forecasts have gotten worse. I can think of two theories that could explain this. I'd be curious to hear from someone more knowledgeable if any of them are right or plausible.

1. High frequency 5G has thrown off rain forecasts in urban areas. Average prediction accuracy has still improved because rural/suburban areas don't have high frequency 5G.

2. The weather app now shows rain forecasts in time blocks as small as 15 minutes, even though predictions this granular are still inaccurate. This has inflated our expectations for forecast accuracy.

I'm sure they have, but I've also been drenched while reading a weather report that refused to admit it was raining in my city right now. It just told me it was cloudy, despite clear and rather heavy rain for 30+ mins straight over the whole city. To this day I haven't figured out how that's even possible.

This article mostly discusses longer-term forecasts, but I have also been impressed with the quality and reliability of imminent-storm alerts. They have saved me from getting drenched in a rain storm or allowed me to pull off the road for a break before a downpour.

It doesn't get a ton of press, but as this article highlights, progress has been steady and significant.

This article asserts that improving forecasts in low-income countries is underrated--does anyone know of studies that predict the impact better forecasts would have? Helping the poor with tech seems like the kind of project that many philanthropists could get excited about, and hopefully more effective than gravity lights and the like.

Dark Sky brought a lot of this powerful forecasting to a hyper-local level. It’s such a shame that Apple bought it up and just…threw it all away. What a waste.

Anyone who lives in a hurricane-prone area like myself (Florida) knows that while forecasts have gotten a _lot_ better, there is still so much room for improvement.

I am not affiliated, but I recommend checking out https://www.forecastadvisor.com/ to see what forecasts are best for your city. I totally changed weather providers and it seems much better now.

'The Secret World of Weather: How to Read Signs in Every Cloud, Breeze, Hill, Street, Plant, Animal, and Dewdrop' by Gooley is a fun read for anyone interested in figuring out weather without a forecast (or to supplement).

Twelve year old won the San Jose Mercury News weather prediction contest one year, by predicting each day that the weather would be the same as the weather the day before.

Consumer weather prediction isn't about being right. It's about pleasing the customer by appearing to be helpful. Which often means exaggerating the chances of abnormal weather, so if it happens you can be a hero.

Real prediction is boring.

Maybe coincidentally related that Simpson's paradox article from earlier, but if they've gotten more accurate overall, I certainly do not see it in the 24-hour forecasts. Of course, I'm also probably only paying attention to when it's wrong, not when it's right.

I plan my motorcycling based on rain, and the number of times I've gotten caught in rain when it wasn't supposed to rain at all that day is non-zero just this year.

One thing I came to appreciate about growing up on the east coast was how much more accurate the forecasts were than they are in Southern California. The winds pushing east, the expansive radar coverage over USA that's publicly available, and the commercial airlines collecting weather data means the storms and weather systems are well understood as they're coming over. Plus the storms make nice straight lines from north to south that push through. In Southern California the rain forecasts always seem off. Even right now, it's raining and the forecast told me it was just supposed to be a little cloudy.

Interesting! As a contrast, I'm using historic weather data to predict future weather - couples use my free wedding weather predictor to find the perfect date for their wedding: https://dropory.com/

There is a gap between the title of the article and the contents. Starts out with weather forecasting is improved, but spends most of the article talking about how poor people and countries have other things to spend their money on than forecasting weather.

Creator of the open-source weather API open-meteo.com here.

The future of weather forecasting is likely to rely heavily on AI models. The article discusses Pangu Weather and HN comments mention GraphCast as examples. Interestingly, on the first of March, the European weather forecast center ECMWF released their new AI weather model AIFS as open data. This model is not only more accurate than their existing numerical model, but also requires significantly less computing power to run. They've published comparisons showing AIFS outperforms other models in terms of forecast precision: https://www.ecmwf.int/en/about/media-centre/aifs-blog/2024/f...

Ever since IBM destroyed TWC and Wunderground, things have become worse. Wunderground personal weather stations were the most accurate weather forecasts.

I could not disagree more.

I paid far less attention to weather forecasts 30 years ago than I do now, but I have numerous anecdotal examples of how weather forecasting models and information provided by publicly available weather services have trended towards uselessness.

There is no publicly accessible weather information service that can accurately forecast weather at my house. One of the first purchases I made when I moved in to the house was an Ambient Weather Station resulting from pure curiosity that has evolved into an interest in keeping a historical record of "actual weather". Daily hi/low temperatures generally have positive correlation with forecasted temperatures, but the spread between forecasted temperatures and actual temperatures is generally ten degrees less than forecasted.

Long term qualitative temperature trends ("above average for the winter" and similar) are positively correlated.

But ...

- Forecasted storm intensities are wildly inaccurate. Forecasted high-intensity rain storms end up being all-day drizzle events or on and off rain showers, and visa versa. A forecast of “a passing afternoon shower” ends up being an all-day wash-out.

- Precipitation forecasts are wildly inaccurate, without correlation. Actual precipitation can be far less than forecasted or far more than forecasted, even when compared to short term forecasts--to include same day and intrahour forecasts. Just this past weekend we had accumulating whiteout snow squalls on an off all day long on Sunday, yet there was never any mention of any possibility of snow by any local meteorologists or by any weather forecasting service I routinely check.

Dark Sky was the best app I ever used for weather forecasting. Its short and long term forecasts were more than sufficient for planning purposes, but where the app to this day has had no equal was in its intrahour local forecasts and precipitation forecasts. If Dark Sky alerted me that there was going to be tornado in my area within the next 15 minutes, I saw a funnel cloud 15 minutes later. If Dark Sky alerted me that it was going to stop snowing in 15 minutes, the snow stopped 15 minutes later. Sadly, Apple lobotomized the service when they claimed to have integrated Dark Sky functionality in to Apple Weather. Even though I fairly regularly report weather accuracy issues to Apple via the Weather app, the reporting and forecasting provided by Apple Weather has never improved.

- Seasonal precipitation forecasts are wildly inaccurate without correlation. Modeling (from NOAA, local meteorologists, etc.) suggested we were to have "above average snowfall" this winter, with the official average winter snowfall being 48 inches. We have received 20 inches so far this winter. Either winter will go out with a bang in the next few weeks (which would be nice, IMO), or modeling will have predicted more than 140% of the actual snowfall. This is an altogether unfair comparison, but why not: if the executives of a publicly traded company forecasted 140% more revenue to shareholders than the company they preside over realized, they would all be immediately fired, sued, jailed, etc.

If society collectively will not tolerate 140% inaccuracy in financial matters (stock price manipulation, value destruction, and so forth), should we be content with weather forecasting and modeling that is just as inaccurate? After all, weather is treated as (only) a financial matter by insurance companies. On an individual level, viewing weather's impact through financial optics still makes sense--from lost days of work and lost wages, to insurance premiums, to food prices, to transportation costs, to taxes, to paying for the ability to get your money back for a concert ticket you bought months ago if the weather is too bad.

Climate change is certainly wreaking havoc on weather modeling, but it has been doing so for a significant period of time and the models do not appear (to me) to be getting better at adequately accounting for the effects of climate change. If current weather forecasting models cannot be adapted to accurately account for the effects of climate change, it may be time to either fundamentally change the way weather modeling and forecasting is done, or not do it at all. Taking out my broad brush and bucket of paint: are there any companies relying on AI to develop a more accurate weather forecasting service?

And if anyone has a weather service to recommend that will not “Night at the Roxbury” me with ads and that has accurate 3-day-or-less weather forecasts, I am all ears. Please post them here.

I find that 4 to 7 day forecasts tend to be 80% accurate. So probably a little bit better than they were when I was a kid.

Unfortunately, the most important part of any forecast IMO is intensity. I don't care if we're going to get snow flurries all day, but if we're going to get a foot of snow, I would like to know -- and not just when the winter storm warning goes into effect!

Similarly, I don't care if we're going to get scattered showers all day. But if we're going to get a downpour in the afternoon, I'd like to know so I can avoid getting caught in a flash flood on a trail or on the road.

Same thing applies with temperature: if it's going to be cold all day, good to know. But if a rainstorm is going to remain active during a deep freeze and create a layer of ice on every exposed surface, I need to be prepared for walking, biking, or driving.

Fortunately there's a somewhat local weather station near me that provides an RSS feed of longform weather forecasts. But I notice that more and more people wind up surprised by slightly-abnormal weather events as they rely more and more on smartphone weather apps. Weather apps that utterly lack the nuance that a paragraph of text can provide.

A more catchy title would've been "Weather has become more predictable".

But it's still a chaotic system and Lyapunov would claim we're quite vain to even try.

okay, nice, but what about software delivery forecasts?

Recommend the book The Weather Machine by Andrew Blume (also wrote Tubes) on some history of forecasting and what happens in the background nowadays:

In The Weather Machine, Andrew Blum takes readers on a fascinating journey through an everyday miracle. In a quest to understand how the forecast works, he visits old weather stations and watches new satellites blast off. He follows the dogged efforts of scientists to create a supercomputer model of the atmosphere and traces the surprising history of the algorithms that power their work. He discovers that we have quietly entered a golden age of meteorology—our tools allow us to predict weather more accurately than ever, and yet we haven’t learned to trust them, nor can we guarantee the fragile international alliances that allow our modern weather machine to exist.

* https://www.andrewblum.net/the-weather-machine-2

* https://www.goodreads.com/en/book/show/42079139

For the very early history of meteorology, see perhaps The Invention of Clouds about Luke Howard:

* https://www.goodreads.com/book/show/1148768.The_Invention_of...

* https://en.wikipedia.org/wiki/Luke_Howard

Just a reminder that weather.gov’s local hourly forecast is exceptionally good and a masterclass in synthesizing information well. (weather.gov/“zip”)>go to right hand rail > hourly weather forecast.

I haven’t found an app or tool outside of building a grafana dash that beats it.

I once listened to a podcast [0] with interviews of a couple of scientists at the ECMWF (European Center for Medium-Range Weather Forecasts).

I think it was in that episode where one said that every 10 years we improve the forecast by 1 day.

It was recorded in 2019, so AI wasn't really that much of a topic as it is today, considering that Google published an AI weather model in November of last year [1].

[0] https://omegataupodcast.net/326-weather-forecasting-at-the-e...

[1] https://deepmind.google/discover/blog/graphcast-ai-model-for...

All the stuff mentioned in the article is accurate – better raw data, faster computers, smaller grids, better predictive algorithms etc. all result in vastly better weather info in general today. This also means though that you have to put in more effort to get a better result for yourself. What algorithm is the app using? Does it localize all the way to your neighborhood, or your street? How frequently does it update? Is your GPS accurate? People generally don't think about this stuff, but some fine tuning can result in vastly better results.

Anecdotally, they have been very frustrating in the Bay Area with this El Niño rain season. Not as reliable. Many ruined plans, but I have learnt my lesson.

This might be true for some areas but maybe not for others. Where we live is on the very edge of the NWS coverage. Our forecasts and messaging is generally a lot more “loose” than folks closer to the main NWS “office”.

I am not sure if this has to do with radar capability but all the old time hams seem to corroborate this.

This speaks on long term outlooks at synoptic scale, we really should put some energy on researching mesoscale long term outlooks, or even 4 hour short term. It's a difficult problem to solve as the variables are quite complex, but the reward can be substantial -- on-land severe weather impacts less people but often is deadlier and can cause huge financial loss in areas that may not expect it.

Geezer alert: forecasts look amazingly good to me. When I was a kid in the Pacific Northwest, it was routine to miss major storms until they hit land. We didn't have satellites, oceanic buoys, etc., and I remember the TV weather guy saying things like "we've had a report from a ship at sea..." and proceeding to make wild guesses.

The weather forecasts in the UK are definitely much better than they were a decade ago, especially in the 3 to 14 day range. However, I still find my stupid heuristic works quite well for predicting tomorrow's weather: the weather tomorrow will be the same as what it was today. The UK often gets sticky ("blocking") weather patterns, so it works surprisingly well.

I can’t believe they killed dark sky. That was accurate to the minute. Incredible.

I wonder if energy market speculation is playing a role in making weather forecasts more accurate. I would not be surprised if some stock markets also get influenced by weather, especially during periods of high fluctuations in energy costs.

The question is WHEN are they more accurate, my weather stations and apps tell me as weather changes what it is, which I can usually do too by stepping outside. But weather forecast (a prediction) I have not noticed it being more accurate and often wonder what changed, is it too much reliance on this real time data?

Keep in mind, weather forecasting is something that is exceedingly difficult and that concerns just about everyone. That means it is particularly ripe for confirmation bias.

We objectively know that weather forecasts are more accurate than ever. We subjectively know that they are bad/gotten worse, because last Thursday I brought my umbrella to work for nothing.

I took meteorology in high school and our teacher made us do a daily prediction exercise that I think would benefit all people, especially those who put too much weight into their own anecdata.

We just had to make a prediction for the next day's weather. Then compare our prediction to the forecasted prediction. It didn't matter how accurate we were for the grade. Just that we systematically performed this exercise.

It really made one appreciate the quality of forecasts, and that no, the "weather man" is not always wrong at all. A lot of huffing and puffing is from people who lack any rigor in their observations. And if you're trying to contest the accuracy of weather forecasts, or any form of forecasting really, then you really should provide some hard evidence.