Ask HN: Why are we still so bad a weather forecasting? Feels like an accurate hourly rain forecast is not too much to ask for in 2021.
Why is it that it's sometimes off by a whole day?
Is it lack of computing power? Unsophisticated models? Is it that the "measuring stations" are in specific locations, and you can't really generalize that to the whole city? Will we ever get to an accurate hourly (minute?) rain forecast? |
When she started working there, they didn't have satellite images, only many local measurement stations in each big city and other places. In each one, someone measure all the things manually and then send a message to the central office.[1] They have to interpolate manually the data to cover all the country.
The idea is that if you have some rain clouds here, and the wind has this speed and direction, then tomorrow they will be approximately there. The clouds may run out of water. Sometimes clouds appear out of the blue if hot humid air collide with cold air.
If the clouds were moving over the sea and they now move over land, you must make some corrections. If there are some mountains, they may change the speed or direction of the clouds. In particular the Andes may stop the clouds for a few days and the change of height make them drop most of the rain in Chile, so it's difficult to predict manually. I'm not a meteorologist, so I don't know the details, but you can see that this involve a lot of general and local knowledge, approximations and guessing.
She was involved in getting the first satellite images here, and then interpreting them, because they only know how the clouds looked from below. [2] Much later they made a transition to digital forecasting. I still remember a few maps in ASCII code with the border of the country, and the predictions of the pressure, humidity and other stuff. She was not involved too much in the modeling part, but she was using the result to make the official forecast.
The first models were not very good, and sometimes they have to use the manual predictions. In particular the mountains in the Andes were not correctly modeled, and we have a big ocean in each side to make everything more complicated.
After a few/many years the model become better and better. Also, they have satellites that can measure visible light, infrared, IIRC humidity and a lot of other stuff. Computer models use more variables, smaller grids, more accurate mountains and have better formulas. So the models now are pretty good.
She now is retired, but she still makes unofficial forecast for the family. If I'm planning a pick nick for the weekend, she will give (unsolicited) advice. [3]
[1] Sometimes the data was inaccurate. Sometimes the data was missing. Sometimes the local person was lazy and lied instead of going outside to get the data. Sometimes the problem was in the transcription.
[2] There is some standard classification of clouds and you must understand how each one looks form above. For example dark clouds are not dark from above, so the darkness is not a good indication of how much water they have.
[3] A few years ago (after she retired), we made the birthday party of my daughter in Zoo a few miles away of Buenos Aires. Two days before the party she said something like:
> It will be rainy in the morning, but after 10am it will be fine, so don't cancel the party. Also, it will rain again after 6pm, so remember to return before that time.
It was pretty accurate, like half an hour of error in the time. She was seeing the forecast of the National Office and also the forecast of a few international pages. Some of them publish the easy forecast in words, and also the maps with the predictions of the models.
https://news.ycombinator.com/item?id=19045250
I'd have to disagree that we are bad at weather forecasting. While we are surprised every so often, the progress in accuracy of the modern five day forecast has been shocking over the arc of my 30-or-so years on the planet. We almost always have a substantial heads up about weather that is going to affect our movements around the world.
I agree with you that down to the minute precipitation forecasts aren't perfect (though I'd say the alerts I get from Dark Sky are at least pretty good) but that level of fidelity is asking a lot.
For example, here is one of the measurement stations in Zurich:
http://wetterstationen.meteomedia.de/messnetz/vorhersagegraf...
This is outside of the city center (to minimize effects such as radiant heat due to pavements etc.), so it gives a good base value to compare against.
For the issue of predicting weather, the model of weather is "sensitive". The observation metaphor of the "butterfly effect" is that relatively small perturbations to the system have the observed effect of being indistinguishable between relatively large perturbations. We may substitute "small" with "deterministic" and "large" with "non-deterministic".
A summary of this statement would be that initial or past conditions of the system poorly characterize the dynamic and chaotic behavior of nonlinear dynamic systems.
The phrase is drawn from the title of the paper by EN Lorenz:
https://static.gymportalen.dk/sites/lru.dk/files/lru/132_kap...
Somber note: Weather is in fact, becoming predictable, precise and accurate: towards chaos, volatility and instability. Measurement and policy do not adequately address the ongoing issue of climate "change", which may more adequately be defined as climate chaos.
After 3 days there's just too much chaos normally for the models, but it does depend on the conditions, and what you are predicting.
"Convection is complex and hard to model because it involves multiple processes, many of which take place at scales smaller than the model’s grid cell, so they need to be parametrized. The assumptions that have to be made when parametrizing convection explain many of the systematic errors in convective precipitation."
(in other words modelling physical interactions on a relatively small scale (1km) is hard)
https://en.m.wikipedia.org/wiki/Edward_Norton_Lorenz - under scientific career and then Numerical weather prediction as well as Chaos theory, there's a good summary
Very accurate forecasts for a specific spot are possible, but today require a trained meteorologist. In practice, the only time people are willing to pay for this expertise are when they're running an airport, planning a war, or launching / landing a spacecraft.
Perhaps in the future we'll have enough compute power on demand for anyone to accurately model on-demand forecasts for specific spots.
Weather.gov is the best forecast in my experience.
And a local resource, who relies on forecasting data, is cliffmass.blogspot.com. He is a UW professor who breaks down the available models and offers a forecast of his own when there is the potential for lowland snowfall.
Both of these sources nailed the significant snow event we just had in western Washington.
Perhaps there is still just a disconnect between the simple, automated consumer forecasts and the actual prediction models that are available.
Which means that I may have a forecast of 24h of rain which is accurate, but my house will still be dry.
Obviously in reality it would never be quite that simple, but the point is that a spot forecast could be much more useful and accurate than an area forecast. (Of course, the more hours ahead the more you get into scenarios where for example just a slight drift in wind angle can give very different results on certain spots, while having more negligible impact averaged over an area.)
In any event we are not well set up for disseminating spot forecasts, but rather for disseminating area forecasts. In practice, as far as I have been able to determine, you can only easily get spot forecasts for airports.
I've also noticed over the years that different sources of weather info are more or less pessimistic. Weather radios always had very accurate forecasts that were a little pessimistic and included some discussion of the possible variances and were used by many planners, police, fire depts, transportation companies, pilots, etc. I haven't listened much to weather radio in recent years so I don't know if that has changed. However the forecasts on the NOAA web site have become progressively more cheerful over the last 10 years or so, predicting sunny skies more often, and only changing to reflect a coming storm as it gets closer. They did change the way the "percent chance" numbers work some years ago (7+ years ago?) so they tend to be more accurate (40% chance of snow used to be an almost certainty here, now 50% is close to 50%). Forecasts, at least the ones indicated by icons and a line or two of text, have become much more optimistic only shifting to become more accurate 1-3 days out from the actual approaching events. If you read the forecast discussion you'll get a more accurate picture of what to expect, but it is pretty technical and not easily understood by your average reader without spending some time getting to know what all the acronyms and special phrases mean (also there is a tendency to use names of geological features as boundaries for weather phenomena, and not many people know these names for the whole surrounding area any more, e.g., river valleys, mountain ranges, local names for areas around larger cities, etc.)
I get the feeling (no hard evidence) that the happier forecasts might have been done for political reasons; a cheery forecast makes for a happier nation. It leaves people less prepared for severe weather, but they do still have several days warning and improves the nations mental health (not an insignificant concern).
So my guess is that lately the pandemic has reduced the amount of data they have to work with (besides the aircraft data maybe companies that ran weather stations have gone out of business or states have cut back on maintaining weather stations to save money?) and that is to blame for any recent destabilizations of predictions. Possibly the polar vortex is also responsible for uncertainty in forecasts in recent months. The cheerier forecasts could be just a calculation on the part of the service itself; it's tough to be the bringer of bad news and people naturally start to hate you for it (try doing QA on software and watch people cringe as you walk towards them with news of the latest bug in their code) so maybe they tried a more positive outlook to try to get people to treat them less like ogre's. Could be something presidents ordered also, to make people happier so they'll get re-elected. Or maybe there are technical reasons like now they rely to greater degree on models whereas before they relied more on people. Possibly whoever wrote the models built in their own biases and decided all on their own that the weather should be cheerier! Maybe forecasts now use machine learning and the training data's accuracy has faded with time or had biases to begin with. Some web research would probably turn up some history of weather models in the USA. There are probably a lot of scientific papers about the models as well.
Note also that the NOAA web site typically provides some maps which show model output in more detail and which you can scroll forward in time (I wish it was easier to see past predictions also) to see how regions are expected to change over time in air pressure, temperature, precipitation, and more. There are regional differences in forecasting too, with forecasters tending to be located near major cities where forecasts are tuned for the local area. The people behind those forecasts do change over time, and likely some aspects of the forecasts also change with them.
One thing you can do to improve forecasts in your area is to set up a weather station and send the data to NOAA. That also lets you make your own assessments and get to know the seasonal changes in the behavior of your local weather. There's been some talk of microclimate forecasts; I think that would require a big increase in the number of sensors scattered around the USA, e.g., it might be possible if all the outdoor Internet of Things sensors reported weather related data to NOAA (and they had some sense of what the accuracy of different kinds of sensors was). Maybe cars could become part of the weather sensing system.
