111–120 of 140 posts
Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."
Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."
I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…
What is more interesting for meteorological forecasting is the time-sensitive details such as:
1. We know severe storms will impact city X at approximately Ypm tomorrow. Will it include large hailstones? Severe and destructive downdraft / tornado? What path will the most damage occur and how much notice can we provide those in the path, even if it's just 30min before the storm arrives?
2. Large wildfire breaks out near city X and is starting to form its own weather patterns.[3] What's the possible scenarios for fire tornadoes, lightning, etc to be formed and when/where? Will the wind direction change more likely happen at Ypm or Y+2pm?
I'm skeptical that AI models would excel in these areas because of the time sensitivity of input data as well as the general lack of accurate input data (impacting human analysis too).
Maybe AI models would be better than humans at making longer term climate predictions such as "If [particular type of ENSO/IOD/etc event] is occurring, the number of cloudy days in [city] is expected to be [quantity]/month in [month] versus [quantity2]/month if the event was not occurring." It's not that humans would be unable to arrive at these type of results -- just that it would be tedious and resource intensive to do so.
[1] https://en.wikipedia.org/wiki/List_of_cities_by_sunshine_dur...
[2] https://imagehunter.apollomapping.com/search/90e4893eeeaa48a...
[3] https://en.wikipedia.org/wiki/Cumulonimbus_flammagenitus
On the advice of someone here on hackernews I tried out weawow, and though it is a terrible name it is _very_ accurate. So much better and consistent. Love it so far.
Earlier quoted context omitted.
I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…
I regularly encounter days when today's forecast is wrong and even in conflict with the current situation. E.g. the weather app tells me there's a drizzle all day and currently and yet it's entirely dry. The opposite happens too. Days of rain often shift in increments of days one or two days before as well. I'd say it's location specific how accurate predictions are.
Here in Berlin, predictions that it will rain or when it will rain are often too pessimistic because the city is a bit warmer and drier than the surrounding areas, which is where the airports are. Tegel, now closed is no the North West, Brandenburg airport is on the South East. They are about 20km apart. The long decommissioned Tempelhof is actually in the middle of the city but I doubt that there still is a weather station there.
Airports are the big consumers of, and important sources of weather data used for making predictions (in addition to satellite data, and weather stations elsewhere). It's more important that the predictions are correct there than 10-15 km away in the downtown areas.
Additionally, many weather apps aren't really precise about where their focus is. You set the city typically; not a postal code. So they'll predict it will rain in Berlin. But it's a big city and that doesn't mean it's going to rain everywhere in the city. It won't do neighborhood by neighborhood predictions. It's technically correct even if not a drop falls where you are. And of course professional users of weather predictions mainly care about the type of weather they need to plan for, which for airports is things like Thunderstorms, poor visibility, etc.
For short term planning, weather radar apps are popular here. Great stuff for guestimating whether you can get home by bike without getting caught up in a big shower. Thunderstorms are very common here throughout the summer but you can see the systems moving west to east hours in advance on the radar apps.
Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."
I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…
One of the major upgrades to the platform was to allow "day of use I-Loads." Effectively, they could update some constants in the shuttle software image, by literally patching new binary values into the code, while the vehicle was loaded and ready on the launch pad.
Then the game was to launch rockets to measure the upper atmosphere wind properties, convert them into usable constants, and then to update the software. It took the shuttle from having launch opportunities 30% of the time to having them 70% of the time later in the program.
Anyways..
Im pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction. Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between…
This reminds me of variational noise (https://www.cs.toronto.edu/~graves/nips_2011.pdf).
If it is random noise on the input, it would be like many of the SSL methods, e.g. DINO (https://arxiv.org/abs/2104.14294), right?
Im pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction. Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between…
> Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between 2 members with different random noise initialisations. I wonder if this will be applied to more traditional genai at some point. We recently had a situation where we specifically wanted to generate 2 "different" outputs from an optimization task and struggled to co…
Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."
I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…