Better 'Nowcasting' Can Reveal What Weather is About To Hit Within 500 Meters (technologyreview.com) 45
Weather forecasting is impressively accurate given how changeable and chaotic Earth's climate can be. It's not unusual to get 10-day forecasts with a reasonable level of accuracy. But there is still much to be done. One challenge for meteorologists is to improve their "nowcasting," the ability to forecast weather in the next six hours or so at a spatial resolution of a square kilometer or less.
From a report: In areas where the weather can change rapidly, that is difficult. And there is much at stake. Agricultural activity is increasingly dependent on nowcasting, and the safety of many sporting events depends on it too. Then there is the risk that sudden rainfall could lead to flash flooding, a growing problem in many areas because of climate change and urbanization. That has implications for infrastructure, such as sewage management, and for safety, since this kind of flooding can kill. So meteorologists would dearly love to have a better way to make their nowcasts. Enter Blandine Bianchi from EPFL in Lausanne, Switzerland, and a few colleagues, who have developed a method for combining meteorological data from several sources to produce nowcasts with improved accuracy.
Their work has the potential to change the utility of this kind of forecasting for everyone from farmers and gardeners to emergency services and sewage engineers. Current forecasting is limited by the data and the scale on which it is gathered and processed. For example, satellite data has a spatial resolution of 50 to 100 km and allows the tracking and forecasting of large cloud cells over a time scale of six to nine hours. By contrast, radar data is updated every five minutes, with a spatial resolution of about a kilometer, and leads to predictions on the time scale of one to three hours. Another source of data is the microwave links used by telecommunications companies, which are degraded by rainfall.
Their work has the potential to change the utility of this kind of forecasting for everyone from farmers and gardeners to emergency services and sewage engineers. Current forecasting is limited by the data and the scale on which it is gathered and processed. For example, satellite data has a spatial resolution of 50 to 100 km and allows the tracking and forecasting of large cloud cells over a time scale of six to nine hours. By contrast, radar data is updated every five minutes, with a spatial resolution of about a kilometer, and leads to predictions on the time scale of one to three hours. Another source of data is the microwave links used by telecommunications companies, which are degraded by rainfall.
I have this already (Score:4, Funny)
I have this already. I call it a "window".
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Try a Weather Rock [wikipedia.org].
- If the rock is wet, it's raining.
- If the rock is swinging, the wind is blowing.
- If the rock casts a shadow, the sun is shining.
- If the rock does not cast a shadow and is not wet, the sky is cloudy.
- If the rock is difficult to see, it is foggy.
- If the rock is white, it is snowing.
- If the rock is coated with ice, there is a frost.
- If the ice is thick, it's a heavy frost.
- If the rock is bouncing, there is an earthquake.
- If the rock is under water, there is a flood.
- If the rock is
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A window gives current conditions, not a forecast for six hours from now.
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The viability of that bet depends on where you live, particularly how far you can see (distance to horizon), how fast storm systems move through your area, and whether your window points upwind of the prevailing surface winds. Where I live, I've seen weather change from plentiful sun to a thunderstorm in less than two hours.
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I think it will depend on your location. If you live in a mountainous area, your climate and weather is different every kilometer. It can be pouring rain on one side of a mountain, and sunny on the other side.
Where I live I tend to have less snow then just 10 kilometers away from me, because of altitude, and the fact there are some mountains that take the brunt of the weather.
However I find the 10 day forecast to be good enough to know if I should plan an outdoor activity over the weekend or not.
Yep. It's just averages at 10 days. Why not 11 (Score:2)
Indeed. That's why they don't even bother with 11 days or 12 days. By the time you get 10 days out, you're mostly looking at the average for this time of year. Current conditions, existing weather patterns, don't tell you much about 10 days from now.
Seven days out you can say "there is a higher than average chance of rain" or "it may be warmer than normal".
Interesting (Score:4, Interesting)
I live on the edge of a small town with farms around. It seems like we've often had in the past couple years a lot of months where we got the northern edge of one storm, the southern edge of the next and end up without much rain overall compared to everyone around us. As a result the farmers have to start getting out the huge sprinklers for their crops, and the next day a storm hits us head on while the farmers are sprinkling. Seems like it could save a lot of gallons of irrigation, or alternately crop damage knowing if weather than day is actually going to rain here or miss again.
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The PWSs should be networked and have machine learning built in. Over time, they will learn which factors and which of their neighbor stations correlate with future weather. Something like Weather Underground would correlate the PWSs, and learn which ones make the better predictions.
They may be doing this already.
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In general farming is better off with better data predictions.
Not written by a Brit (Score:5, Informative)
"It's not unusual to get 10-day forecasts with a reasonable level of accuracy"
A UK 10-day forecast consists of the words "The sun is likely to come up; you may or may not be able to see it."
5-day forecasts are generally little better than flipping a coin to see whether it will rain or not. 3-day isn't too bad and 1-day forecasts are reasonably good for much of the summer and winter; in spring and autumn they're pretty rough.
None of which prevents the Met Office from showing weather maps with a ludicrous level of precision completely unmatched by their accuracy.
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Absolutely. And I suspect that is the source of the claim in the post - it's based on areas with relatively predictable weather. The UK sits on the meeting point of at least three major weather systems (Atlantic, Arctic, and Continental European) and is much harder to predict than, say, Kansas.
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You presume that because that is what you WANT to be true. It isn't. Instead of assuming, go fucking test. It requires work and ill show you you are wrong, neither of which you want to accept so will not put the effort in to.
I think you've posted to the wrong thread. This is an argument; you probably wanted "abuse".
Nonsense article (Score:5, Informative)
This story is nonsense. I'm a meteorologist and I do severe storms research. There are a number of factual errors present, even in the summary. Nowcasting is a short term forecast, generally in the 0-6 hour time frame. That's one of the few things this story got right.
Forecasters rely heavily on numerical models to make predictions. On a regional scale, these models numerically integrate a number of partial differential equations forward on a 3D grid. Many of these models are different configurations of the Weather Research & Forecasting (WRF) model. WRF can be run across many cores with shared memory (OpenMP) or distributed memory (MPI). Domains with very large numbers of grid points can be run across thousands of cores. If the spatial size of the domain size remains the same, adding grid points means decreasing the space between each grid point. Not only does this increase the processing requirements because of more grid points, but also the time step of the numerical integration generally has to decrease. High resolution domains require very large amounts of computing resources in order to produce a forecast in a reasonable amount of time.
The highest resolution model that's regularly run operationally in the US is the High Resolution Rapid Refresh (HRRR) model, and is a specific configuration of WRF. The HRRR is run hourly and has a horizontal grid spacing of 3 km. This is well above the supposed precision of 500 meters. Furthermore, even if the HRRR was run at 500 m, it doesn't mean the forecast would be accurate on such small spatial scales. The big difference between the 3 km HRRR and coarser resolution models like the 13 km RAP (also, WRF-based) is that the HRRR doesn't parameterize convection. That means it runs at a high enough resolution that it can directly simulate phenomena like thunderstorms.
The resolution of radar data in the US is about 500 m, and has been for roughly the past decade. The best weather satellite right now is GOES-16, with a resolution of 500 m-2 km, depending on the type of product. That's a huge difference from what's described in the summary. Forecasts that rely on extrapolating radar and satellite data might be accurate for 30 minutes or perhaps even an hour or two. Beyond that, numerical models are going to produce better forecasts.
The radar and satellite data, along with a lot of other data sources, are assimilated into models like the RAP and HRRR. Assimilation basically means updating the state of the 3D domain based on the new observations. Data assimilation of conventional observations like winds, temperature, pressure, and humidity generally produces good results. However, assimilating radar and satellite data isn't as simple.
Reflectivity and radial velocity are generally assimilated from radar data. Radial velocity is generally assimilated in areas where there isn't precipitation, and is a lot like assimilating wind data. Reflectivity is the amount of power that's scattered back to the radar, and is generally larger if there's heavier precipitation. It's not nearly so simple to assimilate reflectivity because you also need to update variables like temperature, wind, humidity, and pressure in the 3D domain, even though the radar isn't directly measuring them. Those variables are going to be quite a bit different inside a thunderstorm than they are outside it. If you want to update the position and strength of thunderstorms in a model, you need to update quite a few variables in the model that you probably aren't measuring at all in those areas. If you want accurate forecasts of thunderstorms, you need to update the model based on radar reflectivity data.
There are techniques like the Ensemble Kalman Filter (EnKF) that update unobserved variables based on measurements of variables that are observed. However, even with the best EnKF techniques at present, assimilating reflectivity data often doesn't really improve the forecast beyond an hour or two. Perhaps more observations and better techniques will im
Characteristics of winter sunlight (Score:2)
Winter sunlight is incident sunlight during meteorological winter (December 1 through February 28 or 29, as meteorological seasons lead solstices and equinoxes by 3 weeks). Some key characteristics of winter sunlight:
1. Noontime angle of incidence is farthest from overhead.
2. Daily duration of sunlight is shortest.
3. Snow albedo: Accumulated snow reflects much of the light rather than allowing the ground to absorb it.
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#1 has described sunrise/sunset.
The sun doesn't rise as high during winter as it does during summer.
What is Winter Sunlight?
To put it as short as I can: sunlight during winter, characterized by a lower sun, a shorter day, and often snow.
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pretty impressive with severe weather (Score:2)
Good for bikes (Score:2)
I don't subscribe to MIT Technology Review. But more accurate hourly forecasts are useful to pedestrians and cyclists, as they can make a trip earlier or later to avoid hazardous weather.
General pattern for ./ stories lately (Score:2)
Headline: This thing is true now
Body: People have some ideas and are hoping to eventually get to where this thing is true
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40 years late (Score:2)
Weather radar works pretty good on its own. (Score:2)
I have a link to the local radar feed on my phone. It shows a three hour color coded animation with updates every ten minutes. I can guess with excellent accuracy how long till a storm system will hit where I am, you can usually see it coming hours away, how hard it is raining or snowing, if there is hail or it is likely, how long it will take to pass over, or if it is going to be a long term thing, etc. It is not 100 percent, sometimes a storm will veer off or break up at the last minute, occasionally t
Re: Weather radar works pretty good on its own. (Score:2)
Depending upon where you live, you can now possibly do *far* better than every 10 minutes. TDWR has had 1-minute reflectivity updates for tilt 1 from most/all sites during storm events for a couple of years, now, and 2-minute updates for other tilts.
Likewise, wsr88d radar now grabs an extra scan of the 0.5-degree tilt halfway through the scan (Google: "SAILS"). One thing I really don't understand, though... since the 0.5-degree sweep is the one most useful for tornado tracking, but (pre-SAILS) was ALSO the