[glitchinc]

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.

[counters]

Climate change has no impact on weather modeling. The vast majority of weather forecasts derive from physically-based simulations of the atmosphere; the physics of the atmosphere don't suddenly change because the climate is warming. However, we rely equally heavily on statistically post-processing these physically-based simulations to correct systematic biases and better contextualize their outputs. Drift in the distribution of weather conditions - even small - can contaminate some of these types of applications. But not really in a way that you can honestly claim "climate change is making weather forecasts less accurate."

> are there any companies relying on AI to develop a more accurate weather forecasting service?

Sure there are. But AI isn't a silver bullet, and existing weather forecasting technologies are _really freaking good_. For all of the hullabaloo over AI-NWP systems like Google's GraphCast and Huawei's PanguWeather, these state-of-the-art systems are about _on par_ with the best-in-class existing numerical weather models; they offer incremental improvements in tuned forecast accuracy, but these improvements are statistical descriptions of a very, very large number of forecasts - end users really wouldn't see any practical difference in forecast quality if they relied on these forecasts. But to my point above - even AI-NWP outputs would be filtered through statistical post-processing to boost their accuracy/utility.

There are a lot of companies that _claim_ they use AI at different parts of the weather value chain to improve forecasts. A lot of them stretch the truth as to what extent they really use AI or ML. The simple reality is that the weather community has used ML since the 1970's to improve weather forecasts.