“Chaotic wind velocities associated with the rotating turbine blades triggers the doppler radar mesocyclone detection algorithm”
Note: this essay was written by the National Weather Service Forecast Office is Burlington, Vermont and tipped to me by a reader. Vermont’s wind farm acreage pales in comparison to places like the Texas and Oklahoma, where there are literally thousands of acres of wind farms right in the middle of tornado alley. I’ve been there and seen them firsthand.
Certified Consulting Meteorologist Mike Smith writes:
While driving to Norman, OK recently I saw the newest “wind farm” to the west of Interstate 35 southwest of Tonkawa. Wind farms show up as bright ground clutter on weather radars and here it is.
One has to wonder just how much trouble wind farms are causing the nation’s doppler radar warning system. It looks like a classic case of the law of unintended consequences at work. – Anthony
National Weather Service WSR-88D Radar and Wind Farm Impacts
Introduction
The most valuable tool used by the National Weather Service (NWS) to detect precipitation is the radar. Radar stands for Radio, Detection, and Ranging, and has been used to detect precipitation since the 1940’s, with most of the technology coming from the military.
The radar used by the NWS is called the WSR-88D, which stands for Weather Surveillance Radar-1988 Doppler (1988 is when the radar was first built). The WSR-88D is a Doppler Radar, which indicates the radar detects motion toward or away from the radar as well as the location and intensity of precipitation. The ability to detect motion along with numerous other radar enhancements over the years, have enabled NWS meteorologists to examine storms with more accuracy and determine if there is rotation or hail within the area of precipitation associated with a thunderstorm. In addition, radar algorithms have helped NWS meteorologists determine strength and depth of rotation, along with intensity and type of precipitation for warning decisions.
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There are 155 WSR-88D Doppler radars in the United States, including the U.S. Territory of Guam and the Commonwealth of Puerto Rico. Most radars are operated by the NWS or the Department of Defense. Click here to view a map of the WSR-88D Doppler Radar network. Figure 1 shows the network of radars across the United States, Guam, and Puerto Rico. The NWS office at Burlington (BTV), VT has control of two radars. The first radar is located in Colchester, VT (KCXX) and is owned and operated by the NWS. The second radar is located in Montague, NY (KTYX) and is maintained and owned by the Department of Defense (DOD). Forecasters at the Weather Forecast Office (WFO) in Burlington also use surrounding radars from Albany NY, Binghamton NY, Buffalo NY, Gray ME, and Boston MA for decisions concerning watches, warnings, and advisories.
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Figure 2 shows a close up of radar sites across the Northeast United States. Each radar site is strategically placed to cover a particular part of the region, with only limited gaps in the areal coverage area usual caused by mountains.
The following products are available from each radar site: composite reflectivity, base reflectivity, velocity, storm relative motion, precipitation estimates, and numerous algorithms to enhance storm interrogation. For additional information on the NWS radar and the associated products, click here.
In this article, we will discuss how the Doppler radar works and influences of wind farms on both KTYX and KCXX radar data. We will show examples of several radar products impacted by wind farms. In future articles, we will examine severe weather radar signatures and how NWS meteorologists use them, winter weather precipitation returns, beam blockage issues from the Green and Adirondack Mountains, and other non-meteorological artifacts detected by the NWS Doppler radar.
Doppler Radar Theory
The WSR-88D radar obtains weather information (precipitation and wind) based upon returned energy. The radar emits a burst of energy, when the energy strikes an object (raindrops, snowflakes, bugs, birds, mountains etc), the energy is scattered in all directions. A small fraction of the scattered energy is directed back toward the radar. The radar receives this reflected signal during its listening period, and then computers analyze the strength of the returned pulse, time it took to travel to the object and back, and phase shift of the pulse. This process of emitting a signal, listening for any returned signal, then emitting the next signal, takes place very fast, up to 1300 times each second. The better the target is at reflecting radio waves (i.e. more raindrops, large hailstones, ice, etc), the stronger the returned energy or echo will be.
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The WSR-88D pulses have an average transmitted power of 450,000 watts. By comparison, a home microwave has about 1000 watts of energy. Figure 3 shows the radar transmitting a pulse in green toward an object in blue. As the pulse intersects the object return energy is sent back toward the radar, and the intensity and distance can be determine from this energy.
Doppler radar systems can provide information on movement of targets and their position. The Doppler principle for radar is similar to that observed with sound waves. An object emitting sound waves will transmit those waves in a higher frequency when it is approaching your location (inbound velocity) and as the object moves away from your location, the sound waves will be stretched and have a lower frequency (outbound velocity). This is the phenomena responsible for the change in pitch of a train whistle as the train or moves past you.
The echo intensity (reflectivity or Z) is measured in dBZ (decibels of Z) during each volume scan. The dBZ values increases as the strength of the signal returned to the radar increases and usual the dBZ values range from 5 to 75. Light rain or snow typically has a dBZ return between 20 and 30, while severe thunderstorms can have dBZ values, which reach 65 to 75. Typically the higher the dBZ value, the higher the rainfall or snowfall rate.
The WSR-88D employs scanning strategies in which the antenna automatically raises to higher and higher preset angles or elevation slices, as it rotates. These elevation slices comprise a volume coverage pattern (VCP). The two main operating VCPs used by forecasters are clear air mode and precipitation mode. These different VCPs have varying numbers of elevation tilts and rotation speed, which help to analyze and provide different perspectives of the atmosphere. Click here to learn more about different VCP’s and the associated elevation slices.
Forecasters at WFO BTV use VCP 212 for severe weather operations, because it provides the best low-level vertical scan of the atmosphere in the shortest amount of time (less than 5 minutes). We use VCP 121 for light to moderate widespread rain or snow and VCP 31 or 32 for clear air operations, when no or very light precipitation is falling.
Click here for a VCP quick reference comparison chart used by forecasters at WFO BTV. Josh Korotky (Science and Operation Officer at WFO State College) developed this chart. The right part of the chart shows the different elevation slices with each volume scan, while the middle part of the chart indicates the VCP and the time it takes the volume scan to be complete, along with maximum range R (reflectivity) and V (velocity) data can be observed. Finally, the remarks section on the left side provides user with a brief description along with strengths/weaknesses of each VCP.
Wind Farm Impacts on Doppler Radar
In this section, we will discuss the impacts that numerous wind energy generating turbines or wind farms have on radar sampling and the potential for erroneous data. Wind farms impact radars in several ways, especially whenever the wind turbine blades are in motion and located within the radar’s line of sight. The turbines can block a significant percentage of the radar beam and decrease the radar signal power down range of the wind farm, particularly if the wind farm is within a few miles of the radar. The wind farm can reflect energy back to the radar system and this appears as clutter or false reflectivity data. This contaminated data can create false precipitation estimates and disrupt precipitation algorithms used by the radar and other software programs.
Wind farms located within 30 nm or within sight of the radar, can significantly impact velocity and spectrum width data, which can cause bad data sampling of rotating storms and false storm motions, along with impacting algorithms used by the radar to process this data. Schemes designed to filter out the ground clutter do not work properly.
Other impacts include, multi-trip/multi-path returns, which creates a false signal down the radial from the wind farm reflectivity region. This can cause confusion and distraction to forecasters, especially during widespread convective or thunderstorm events.
Both KTYX and KCXX have wind farms located within 30 nautical miles (nm) of the radar, which affects data sampling. For KTYX, the Lowville Wind Farm near Lowville, NY on the Tug Hill Plateau is located 2 to 10 nm from the radar, covering from north (010 degrees) to southeast (120 degrees) with over 100 total wind turbines. Additional proposed wind farms to the west of the radar, and to the southeast of the radar, would further influence radar sampling and data quality. Meanwhile, the wind farm near Altona, NY is located in northern Clinton County, NY and is between 25 and 35 nm northwest of the KCXX radar in Colchester, VT.
Wind farms affect both radars in several ways; first, the turbines can block a significant percentage of the radar beam and decrease the radar signal power down range of the wind farm, particularly if the wind farm is within a few miles of the radar. Second, the wind farm can reflect energy back to the radar system and this appears as clutter or false reflectivity data. This reflectivity can create false precipitation estimates and disrupt precipitation algorithms used by the radar and other software programs. Finally, wind farms can significantly influence velocity and spectrum width data, which can cause bad data sampling of rotating storms and false storm motions, along with impacting algorithms used by the radar to process this data. Since the wind turbines have motion and produce reflectivity, schemes designed to filter out the clutter do not work properly.
Given the Lowville Wind Farm near Lowville, NY is within 5 nm of the KTYX radar, several other impacts have been observed, which affect radar sampling. They include, multi-trip/multi-path returns, which creates a false signal down the radial from the wind farm reflectivity region. This can cause confusion and distraction to forecasters, especially during widespread convective precipitation events. For addition information on impacts of wind farms on NWS radar, click here.
Examples of KCXX radar products impacted by Wind Farms
In this section, we will show the locations of the wind farms in relation to the radar sites, samples of wind farm effects on velocity (V), reflectivity (Z), and radar estimated precipitation products. We will show an example of a thunderstorm that developed very close to the wind farm in Clinton County, New York.
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Figure 4 shows the KCXX radar location, along with the composite reflectivity in clear air mode, and the associated ground clutter caused by the wind farm near Altona, NY, which is located northwest of the radar. The small blue crosses indicate the location of the two wind farms in Clinton County, New York, while other returns in the composite reflectivity is associated with areas of ground clutter caused by buildings, hills, mountains, and other non-meteorological targets.
Figure 4 also shows the KCXX radar is located between 25 and 30 nm miles from the Altona Wind Farm (blue crosses) and results in the white colored dBZ returns, which indicates ground clutter caused by the wind farm. The wind farm impacts the 0.5° and 0.9° radar elevation slices, because the turbines are located on the eastern slopes of the Adirondack Mountains at an elevation of 1350 feet. However, the wind farm near Chateaugay, NY in northwest Clinton County, NY is located greater than 35 nm from the KCXX radar and no ground clutter is created, because the lowest radar elevation scan is above the height of the wind turbines.
Note the red and purple returns near (between 5 and 10 nm) the KCXX radar associated with ground clutter, such as buildings and the clutter associated with the Green Mountains to the east and the Adirondack Mountains to the west across northern New York. These mountains create ground clutter and contaminate our data, but also block the beam and create poor data sampling, especially in the lowest elevations scans.
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Figure 5 shows the impact the Altona, NY Wind Farm has on the lowest four elevation scans (0.5°, 0.9°, 1.3° and 1.9°) from the KCXX radar in Colchester, VT.
Note the ground clutter in the first two elevation scans (0.5°, 0.9°), indicated by the red dBZ returns (circled in yellow), but the higher scans (bottom two displays) show no clutter, because the height of the radar beam is higher than the wind farm.
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Figure 6 shows the KCXX storm total precipitation (STP) display across the Champlain Valley and extreme northwest NY. Note the purple to white pixels (circled yellow in figure 6 above) which indicates precipitation estimates of 10 to 15 inches. This over-estimate in storm total precipitation is caused by the radar detecting motion and reflectivity produced by the wind farm.
Forecasters at WFO BTV have since developed precipitation exclusion zones, which prevent accumulating precipitation from occurring near the wind farms and over the mountains, where clutter is usually detected.
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Figure 7 shows the KCXX 0.5° base reflectivity and the associated ground clutter caused by the Altona, NY Wind Farm. Note the red dBZ returns of 50 to 55, which would suggest heavy precipitation if the returns were real.
The white crosses in the image indicate the location of the wind turbines, 25 to 30 nm from the KCXX radar. The wind farm produces reflects energy from the radar system that appears as clutter, and contaminates the reflectivity data.
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Another product from the KCXX radar impacted by the Altona, NY wind farm is the 0.5° base velocity. Remember, Doppler radar can measure winds moving toward the radar (green color) and away from the radar (red color). This helps forecasters determine winds associated with precipitation, strength/depth, and movement of a storm.
Figure 8 shows the 0.5° base velocity from the KCXX radar. Once again, you can see erroneous velocity data caused by the wind farm located between 25 and 30 nm from the radar. The yellow circled area on figure 8 shows velocity wind measurements of up to 80 knots (redish white color), caused by the movement of the wind turbine blades. This bad data sampling can cause tornado vortex and meso-cyclone algorithms to detect false circulations and alert forecasts, which can be distracting during the warning decision making process.
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The next example shows a thunderstorms developing near Altona Wind Farm. Weather patterns show winds from the Champlain and Saint Lawrence River Valleys converge across the northern Adirondack Mountains. This converging of moist/unstable air rises due to the slope of the terrain causing thunderstorms to develop and given the location of the wind farm, these storms can be difficult to detect, especially in the early developmental stages.
Figure 9 shows a looping 0.5° base reflectivity from the KCXX radar. This loop shows the Altona Wind Farm located just south of Altona NY, which is displayed on the image by the higher (yellow/red color) dBZ returns. As the image loops, note the bright yellow and red dBZ returns that occur near the wind farm, and then moves northeast toward the Champlain Valley. This figure shows a clutter of showers and thunderstorms that developed on 26 June 2009 at 1651 UTC in the vicinity of the wind farm. Note this cluster of showers continued to intensify as they encountered the Champlain Valley. Forecasters at WFO BTV have witness this type of activity several times during 2009 convective season.
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Figure 10 shows a frame-by-frame 0.5 base reflectivity of a developing shower over the Altona Wind Farm. The first image (left) at 1651 UTC on 26 June 2009 shows no shower, just the wind farm. Meanwhile, at 1656 UTC and 1701 UTC you can see the development of the shower over the wind farm, then moving toward the Champlain Valley by 1706 UTC and intensifying.
Note the strongest dBZ returns are stationary just south of Altona, associated with the Doppler radar detecting the wind farm in the lowest elevation scan.
Examples of KTYX radar products impacted by Wind Farms
In this section, we will show examples of KTYX radar data being impacted by the Lowville Wind Farm, which is located 2 to 10 nm from the radar. The KTYX radar is located on the Tug Hill Plateau east of Lake Ontario near the town of Montague, NY. The Lowville Wind Farm consists of over 100 turbines, which extent from north (010°) to southeast (120°) of the KTYX radar. The greater the number of wind turbines within a few miles of the radar, increases the amount of beam blockage and attenuate the radar signal down range of the wind farm. In addition, more clutter is created, along with higher precipitation estimates, from the greater number of wind turbines.
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Figure 11 shows the KTYX composite reflectivity and associated ground clutter created by the Lowville Wind Farm. In the image, you can see the strong dBZ returns of 60 to 70, which suggest very high-energy returns, created by the wind turbines.
The closeness of this particular wind farm to the radar site significantly blocks radar data and produces very poor sampling across the western Adirondack Mountains and parts of southern Saint Lawrence County in northern New York. We will show examples of the Lowville Wind Farm impacting reflectivity and velocity data, along with some radar algorithms being affected.
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Figure 12 shows the Lowville Wind Farm from the KTYX radar tower, looking toward the northeast. It is interesting to see how high the wind turbine blades extend above the tree line. The wind turbines are between 300 and 400 feet above the ground.
Figure 13 shows the KTYX 0.5° base reflectivity and the location of the Lowville Wind turbines and the associated ground clutter they produce. The wind turbines are labeled in white crosses in the figure and the associated ground clutter caused by the wind turbines is outlined in white.
The KTYX radar location is labeled in yellow and the 1 nm, 3 nm, and 10 nm range rings are in yellow.
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Figure 14 shows the KTYX 0.5° base reflectivity (left), velocity (right) data, and the mesocyclone detection algorithm in yellow. The white circled areas on the images shows ground clutter from the Lowville wind farm. The velocity figure on the right shows contaminated velocity data due to the rotating turbine blades located down range of the wind farm.
The chaotic wind velocities associated with the rotating turbine blades triggers the KTYX radar mesocyclone detection algorithm (yellow circles) to alert forecasters of potential rotation. These yellow circles indicate an inbound/outbound wind couplet. However, because no precipitation is detected and only a small and nearly stationary velocity couplet is displayed near the wind turbines, meteorologists can determine these low-level circulations are not real.
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Figure 15 shows the KTYX storm total precipitation (left) and radar composite reflectivity (right). Note the significantly reduced storm total precipitation highlighted by the white circle 15 to 20 nm from the radar. In addition, the image on the right shows significantly weaker reflectivity returns due to The Lowville Wind Farm blocking the beam and reducing the signal power of the radar to detect precipitation.
The two images show lines of poor radar data sampling (white arrows), due to the angle at which the radar signal intersects the wind turbines. Meteorologists use surrounding WFO radars, surface observations, and interpolation techniques to estimate the reflectivity and precipitation, which is reduced by the wind farm.
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Figure 16 shows the KTYX storm total precipitation product before the development of precipitation exclusion zones (lower left) and then storm total precipitation after the creation of exclusion zones (lower right). Note the high storm total precipitation amounts of 6 to 9 inches associated with the contaminated data from the wind farm.
This bad data is excluded from the storm total precipitation product by the use of precipitation exclusion zones and results in a much better representation of actual rainfall estimates. This contaminated data, if not corrected, can be ingested into river forecasting models and can cause erroneously high forecast river levels.
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Meteorologists at WFO BTV have noticed on numerous occasions the Lowville Wind Farm has produced very poor data, influencing areas far from the radar across southern Saint Lawrence County and western Adirondack Mountains in northern New York. Figure 17 shows the KTYX base velocity with spurious multi path scattering caused by the wind farm turbines. The green and red single lined pixels extending down the radial are caused by multi-path and inter-turbine scattering of the radar beam. The white arrows identify areas where multi-path scattering is occurring. This scenario causes significant reduction in data quality many miles from the radar and wind turbines. In addition, this poor data quality can lead to false low-level circulations or false areas of light precipitation. In some strong wind conditions, when environmental conditions are just right, false tornado vortex signatures (TVS) will be detected by the radar. In these cases, we try to invoke more suppression to reduce the clutter and impacts caused by the wind turbines.
As the demand for energy continues to increase, the development of clean efficient wind energy produced by wind farms will continue to grow. The development of wind farms within the line of sight of radars will continue to increase, resulting in the continued reduction in data quality. Meteorologists have noticed impacts to reflectivity, velocity, storm relative motion, and precipitation estimate data with radar located within 30 nm of a wind farm. WFO BTV continues to monitor wind farm development across the North Country and the potential impacts to our radar quality.
WFO BTV continues to adapt our radar data interpretation techniques and training to provide the public with the best possible watches, warnings, and advisories products for the protection of life and property. Finally, we continue to update our clutter suppression methods and precipitation exclusion zones to provide our users with the best radar data quality possible.
References
Information on radar and how Doppler Radar works:
http://www.srh.noaa.gov/jetstream/doppler/doppler_intro.htm
Information on Doppler Radar and Associated Impacts Caused by Wind Farms:
http://www.roc.noaa.gov/WSR88D/WindFarm/WindFarm_Index_GreatFalls.aspx?wid=*
Just west of KDFR (Frederick Oklahoma), EVERY day beginning this last year … just across the river into Texas is the site of the windmill farm …
http://www.mesonet.org/index.php/weather/radar/KFDR
Look at the bright side- the radar will show increased precipitation and correct the models.
I will, once again, suggest that someone sample surface and air temps at one of California solar farms. Solar panels are designed to absorb radiation and I wonder just how much warming
they might cause. My estimates are that 80,000 acreas of solar farms would be required to
produce the same gross amount of power that a 1500 MW nuclear plant could produce.
Is our Whooping crane population being exterminated by wind turbines? One bird fancier seems to think so.
As with these subsidy farms killing bats and eagles, they will get a free pass for this too. However, the meteorologists that miss a tornado that takes out a town will be vilified. This is no longer a scientific issue, it is political; only political defeat will change anything.
It’s not just weather radars that are affected, for years the only organisation able to block a wind farm in the UK was the RAF due to the interference they caused to air defence radars. Since it has been decided that global warming is a greater threat to security than Russian bombers, now even the RAF can’t over -rule the planners.
On the bright side, weather radar may no longer be necessary.
http://wattsupwiththat.com/2013/11/05/move-over-millibars-flickr-is-the-new-atmospheric-metric-for-hurricane-central-pressure/
‘the development of clean efficient wind energy’
OK! Who wants to list all the problems with that one, beginning with ‘efficient’?
” £48,000 wind turbine has been generating around £5 of electricity every month because it is thought to have been put in the wrong place ”
http://www.telegraph.co.uk/earth/energy/windpower/10431944/Was-wind-turbine-put-in-wrong-place.html
The wind farm outside Rio Vista CA always looks like an area of precipitation on radar. (Maybe it’s the splattering blood of hawks/eagles getting hit by the turbines….) (sarc…and in admittedly very poor taste, but I loathe the gawd-awful things.)
This is the same radar that Environment Canada sites use, to detect the heat haze over airport tarmacs as light rain?
Thanks for the explanation of the radars, the effects of wind farms, and a nice tour through the recent natural (or unnatural) history of the USA.
Its time these windmills are demolished.
John from the EU says:
November 7, 2013 at 12:42 pm
Its time these windmills are demolished.
I am willing to bet that in the eagerness for getting the subsidies paid out to their supporters, friends and relatives, none of the politicians have thought of what to do when these windturbines cease to work. Life expectancy seems to be a lot lower than the 25 years quoted and the maintenance costs will be huge in around 7 – 10 years time. At which point it can be expected that the subsidy farmers will declare bankruptcy and go in search of some other subsidies from weak minded politicians. Meanwhile the landscape will be dotted with corroding hulks that nobody thinks it is their job to remove. Ideally, the landowners who had their snouts in the subsidy trough should be told to make good the land. But if you do an internet search on “abandoned windmills” there seems to be significant precedent for the rotting stumps being left in place as a monument to political stupidity.
“Chaotic wind velocities associated with the rotating turbine blades triggers the doppler radar mesocyclone detection algorithm”
No big deal, they just have to put a patch that ignores windfarm areas. Software is never done and never gets simpler. Elsewise we’d be using WordPerfect in windows 3.01.
All that is necessary is to incorporate the correct clutter map at each radar site.
http://www.wdtb.noaa.gov/buildTraining/papers/clutter/CLUTTER.pdf
@KevinM
http://www.wdtb.noaa.gov/buildTraining/papers/clutter/CLUTTER.pdf
@Ian
There are windmills at Tehachapie that still produce that are 30 years old
http://windsystemsmag.com/article/detail/133/tehachapi-planned-for-prosperity
Isn’t the PR that if we build enough wind farms and solar farms the climate won’t change anymore? Isn’t weather part of the climate? Build more wind farms and we won’t need forecasting!
“New day. Same old……”
So, if a severe storm is hidden by the clutter then the storm does grievous harm, who pays the bill to make the affected party ‘whole’?
Mission creep is the new normal.
That way a large wind farm can transfer quite some angular momentum from earth to atmosphere, which means average vorticity is no longer zero, that is, it can’t be homogenized by atmospheric diffusion, only surface friction can attenuate it. That’s a pretty unusual state for the atmosphere, I can imagine it may have large scale consequences for weather over wind farm country.
Is anyone aware of a study on this topic?
KevinM says:
November 7, 2013 at 1:11 pm
“Chaotic wind velocities associated with the rotating turbine blades triggers the doppler radar mesocyclone detection algorithm”
No big deal, they just have to put a patch that ignores windfarm areas. Software is never done and never gets simpler. Elsewise we’d be using WordPerfect in windows 3.01.
So you ignore the windfarm area – and it becomes a dead spot with no ability to see anything. But of more concern and the reason that windfarms were not allowed in some areas in UK, is the sometimes extensive radar shadow beyond the windfarms. This can be mitigated by the windfarms paying to have radars placed beyond their windmills to fill in the area of shadow. As the subsidy farmers are very money conscious this is unpopular.
From –
http://windsystemsmag.com/article/detail/133/tehachapi-planned-for-prosperity
“Getting the transmission expansion scaled to the proper level and getting the costs rolled into rates paid by users of the system is extremely critical,” he continues. “This is one of the biggest problems blocking transmission expansion success across the country. To be proper, such user funded expansion needs to be part of a regional transmission planning process so that the expansion is properly scaled and will best serve broad needs. One issue is that in some states users are strongly objecting to pay for transmission used to transport new renewable energy across their state to others in remote locations. A strong national transmission grid is critical for our country, however, and we are not getting what we need with so much provincialism.”
*IF* the electricity is so cheap and it’s value on the open is so much, why should people that don’t use the electricity have to pay for it’s delivery to other markets…
Box of Rocks: “So, if a severe storm is hidden by the clutter then the storm does grievous harm, who pays the bill to make the affected party ‘whole’?”
Rather nice summation of a strain of argument in this. To wit, who owns wind speed? If it’s meteorological forecasters then they have a cause of action against wind farms. But by corollary, then they’re also the ones with a cause of action against CO2 sources. For regardless of what effect CO2 has on surface temperatures overall, it will change wind velocities if it does anything except stay the same value in perpetuity.
This same sort of notion can be found in investments also. That the people with the models ‘own’ the stock market. For if anyone comes along and alters the behaviour of the market, they have altered the signals and trends of the market. And thus owe the crystal ball set some remuneration.
The same issue can be found in the insurance market when it cries about ‘climate change.’ Since if the climate changes, the insurers might have to alter their models.
The answer to every one of these arguments is the same: If you have a model, you own the model. Not the reality you attempt to apply it to. And this simple notion would save a lot of problems regarding IPCC policy recommendations and the utter and complete failure of their models to have any utility.
I should have added earlier (busy here!), but, if one takes Google earth and ZOOMS into the area west of KFDR WSR-88D site (in the area of the return seen on the link I posted above) ONE CAN ACTUALLY SEE the wind farm and the access roads put in to each wind mill’s ‘pad’ location.
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Only, there is some amount of ‘physics’ involved; as poster Ian W points out in his post of November 7, 2013 at 2:18 pm: “But of more concern and the reason that windfarms were not allowed in some areas in UK, is the sometimes extensive radar shadow beyond the windfarms.”
The gist of it is, the RADAR imagery at the lowest (.5 deg for WSR-88D) elevation angle for that solid azimuth angle encompassing the windfarm will be contaminated both for magnitude and Doppler beginning at the wind farm and outward from there to as far as the RADAR is set to process ‘returning’ (‘reflected’ or back-scattered) transmitted RF pulses.
.
Jquip:
You missed the point.
Since we know that a wind mill farm can negatively affect NWS radar, and said radar is used by the NWS to gauge the severity of the weather needed to trigger the various types of warnings that save human life, the questions become –
How does the court make an aggrieved party ‘whole’ when the proximate cause of the lack of the detection of severe weather is due a windmill farm?
It is not about wind speed or models. It is about the windmill negative unintended consequences…. who pays for those?
KTYX Montague is run by KBUF Buffalo, not Burlington Vermont. KBUF has a well established and documented wind farm anomaly southeast of the KBUF radar – one of the first NEXRAD installations to raise serious concerns about the wind turbine anomalies ( http://www.erh.noaa.gov/buf/windfarm.htm ). Perched on high ground in the Southern Tier, its a pretty constant high reflectivity anomaly ( reg 50dBz+) Between the false TVS signals and meso trips from Niagara Falls’ vapor, and the steel mills in Hamilton Ont, and the lake effect enhancement, sorting out what’s happening near the ground gets challenging around Buffalo at times.
Don’t forget those bright guys in the UK who have ‘solved’ the problem for off shore wind farms: stealth blades invisible to radar. Not kidding by the way. I await the collision between a container ship and a wind turbine in fog due to it being invisible.
IIRC our SAAB 340B aircraft (twin turboprop GE CT7-9B turbines) with honeywell weather radar had issues when near windfarms.
It was so long ago I don’t remember the details though so I EASILY could be wrong.
makes you think though….
You’d have to be in pretty close a ground based radar system to image something like this. IMO, there are lots of bigger ground clutter issues than this.
Box of Rocks: “How does the court make an aggrieved party ‘whole’ when the proximate cause of the lack of the detection of severe weather is due a windmill farm?”
Yes, I understand. And if there was no future-prediction models, then who would the court put on the hook for not predicting the future?
There is a fortune teller aspect in the sense of reliability of service. Fortune tellers aren’t reliable and so it’s considered ‘fraud’ if the service is not sold for ‘entertainment purposes only.’ But if reality changes, and you move from forecaster to fortune teller, then whose problem is it that you continued to sell your product as if you were not fortune teller?
http://www.express.co.uk/news/uk/441679/Is-this-the-world-s-most-wasteful-green-idea-Wind-turbine-produces-just-5-energy-a-month
Carrick says:
November 7, 2013 at 4:08 pm
You’d have to be in pretty close a ground based radar system to image something like this. IMO, there are lots of bigger ground clutter issues than this.
__________________________
Check the link in my post above – that anomaly is plenty big enough to mask a funnel, or a SRV couplet. Its also the biggest anomaly around, even after decluttering for the urban development and humid industrial basin issues. You have to remember that the further the reflector is, the bigger the bins are and the lower the resolution to begin with. Because of lake effect the south shore of Lakes Erie and Ontario get a lot of locally severe thunderstorms that drop F0-F2 tornados in the summer. You’re frequently dealing with small tight couplets that can be difficult to interpret at the best of times.
Jquip;
What part of a model is not needed don’t you understand?
Radar data is not an output from a model. The output on a screen is nearly real time data. I say ‘nearly’ due to the processing delay due to digitization. The fire control radar on a F/A-18 doesn’t use fortune telling to tell the pilot where the bogey is….
Watching a radar screen and watching a tornado form in the images on a screen have nothing to do with modeling, witch craft or fortune telling.
Obscuring data that is beneficial to the public is in some instances just down right criminal.
Carrick (and others), among other things 🙂 I’m a licensed private pilot and my “base” airport is Burlington (KBTV). That Altona wind farm really sticks out like a sore thumb when you’re flying in the general area – it’s something like 65 (?!) turbines. (And, BTW, that facility has already had its share of troubles – four years ago one of the turbines collapsed, and last year one caught fire.) That’s a heck of a radar target, and don’t take my word for it. Here’s a radar link that I’ve long used (including inflight on my mobile device) for local weather monitoring:
http://classic.wunderground.com/radar/radblast.asp?zoommode=pan&prevzoom=zoom&num=0&frame=0&delay=15&scale=0.233&noclutter=0&ID=CXX&type=N0R&showstorms=0&lat=44.47183&lon=-73.15333&label=KBTV&map.x=400&map.y=240&scale=0.233¢erx=460¢ery=334&showlabels=1&rainsnow=0&lightning=0&lerror=20&num_stns_min=2&num_stns_max=9999&avg_off=9999&smooth=0&MR=1
For clarity, run the “Animate Map” and you’ll see the Altona splotch in all its immobile glory.
Also, if you look off to the north-northeast of the CXX radar site, you’ll see a small immobile yellow dot (just to the right of the Interstate “89” shield). That’s a new (last year) facility of just four wind turbines that were built on top of Georgia Mountain. Even such a small facility is enough to show up on the radar.
For what it’s worth (speaking as a pilot), these things are also an aviation hazard. (Their only use, in mountainous facilities, is to serve as crude probes of above-ground-level winds.) And at night they are indeed an aviation obstruction hazard….. requiring that they be topped with a red hazard beacon. If I recall correctly, the FAA regulation on those beacons is that they have to be 1 kW – so every turbine is, by law, a 1 kW sink for half of the time.
Box of Rocks: “Obscuring data that is beneficial to the public is in some instances just down right criminal.”
Obscuring data? So now your claim is that the radar operators are hiding data — like Climatologists — or fabricating it outright for their political goals — like Climatologists? If that’s the case then then the modelers would have a tort against the radar operators if the product was misrepresented.
Or do you really want to polevault into the idea that because your property has a radar signature, that we should ban your house: Because, Social Justice, man.
Jqiup:
It is not the operators that is the problems. It is the whirly gigs interfering with a Radar”s systems ability to gather data.
All a radar system does is use an active system to gather data. Weather data in this case. The sad part is the data can be corrupted.
Box of Rocks: “The sad part is the data can be corrupted.”
This is the source of your confusion. The data you get isn’t corrupted, it’s the data. It’s only modelers that expect a static universe that have any problems.
Here in the plains of the US there are lots of windmill farms. The weather forecasters from NOAA know where they are. The forecasters, as professionals, know the limitations of their equipment, the effects of the topography, the birds that migrate, and the other surroundings, including the wind farms, and make their predictions accordingly.
This effect, like most effects of technology, are met and conquered every day.
The first image appears to me as a “snow” of a large number of very small vortices. This appears easy to distinguish from a tornado or a mesocyclone, and as incapable of hiding a mesocyclone. It appears to me that it will only slightly reduce visibility of a tornado, although it may be able to hide an extremely small and weak tornado or a gustnado (a non-tornado whirlwind found briefly existing at some severe thunderstorm gust fronts). One more thing – tornadoes small enough to be hidden by this clutter tend to not last long – they are mostly on the ground for around a mile or less, often less than half a mile. Sadly, many of these small tornadoes drop suddenly from very small, short-lived mesocyclones, which are often in thunderstorms other than supercells. Often in and near Philadelphia, which gets closeto its share of these small ones, the first warning of a tornado is blown windows or your roof is torn – despite lack of nearby wind farms.
Reason number 43,567, chapter 667, paragraph B#, subparagraph x-T for demolishing these obscenely environmentally destructive contraptions – bird killers, habitat destroyers, landscape despoilers, and FOSSIL FUEL WASTERS – and making the “investors” in them pay for their removal and disposal.
” Radar stands for Radio, Detection, and Ranging”
RADAR stands for Radio Assessment of Direction And Range.
ie it’s DIRECTION not detection ! (Despite what mis-information Wikipedia may currently be projecting ).
RADAR was developed in Britain during WWII primarily for detecting in coming German aircraft. The direction is just as important as the range.
RADAR provides a two dimensional polar coordinate for the target. Hence: “Direction And Range”
In 2011 I distributed a letter in the Internet:
“More Rain for the Town of Norden” …
http://docs.wind-watch.org/More-Rain-for-the-Town-of-Norden.pdf
The Danish wind farm really generates clouds.
Greetings from Germany
Prof. Dr.-Ing. Jürgen Michele, very interesting observation.
I don’t think it is as much the air being forced to rise as in the case of mountains but the drop in pressure due to turbulence.
Turbulence and depression behind the blades causes condensation when surrounding condition are close those required.
Once beyond the disturbance this will take some time to evaporate into clear sky conditions again. It may rain in vicinity before this happens.
I think this is what is hinted at in the link you included to another paper using GCM and increased “roughness”. This roughness of the terrain would cause turbulence and hence possibly produce a similar effect in the model.
I would say the above depression causing condensation is similar to the vapour trails that form from the tips of air craft wings in certain conditions.
Turbulence is important.
But as can be seen on the official photo of the Danish wind park, the clouds keep rising, because of higher temperature compared to the suurounding air due to condensation
Greg Goodman says:
November 8, 2013 at 1:37 am
” Radar stands for Radio, Detection, and Ranging”
RADAR stands for Radio Assessment of Direction And Range.
ie it’s DIRECTION not detection ! (Despite what mis-information Wikipedia may currently be projecting ).
RADAR was developed in Britain during WWII primarily for detecting in coming German aircraft. The direction is just as important as the range.
RADAR provides a two dimensional polar coordinate for the target. Hence: “Direction And Range”
***************************************************************************************
I think this needs checking. Can somebody ask Al baby as it was probably him that invented RADAR.
Do I really need a sarc off tag?
Coming soon to a hill near you — a “stealth” pinwheel that absorbs radar.
I’m sort of curious about radar now, with the kW power radar pumps out, does it raise the temperature of close objects it interacts with by a detectable amount, or does it interfere with electronics, like say for example a temperature monitoring station at an airport where they may be all sorts of radar in operation (land and airplane) at varying distances to the monitoring station?
@scott. Yes. you can fry if you stand right in front of a powerful radar transmitter BUT remember this is a case where PEAK power in a pulse, is what you need to get the greatest range. Then that’s gone and you ‘listen’ for echoes. (In acoustic terms its like setting off a banger and listening for slap-back echoes – try clapping your hands on quiet day or night and listening. Here I can hear reflections of a bank of trees 100 meters away and another 300 meters away). So the average power is not great.
To those who say ‘operators adjust or software can be made to adjust’ that’s BS with respect. Drivers can also adjust to driving in dense fog, but not without compromising their performance…with doppler radar you are performing spectral analysis on the returns, and if the spectrum is dominated by other traces from whirling blades, and you are also masking what’s behind the turbines, no amount of software or operator training will remedy that.
In short you can adjust, but not fully recover performance of doppler radar systems affected by wind farms. To reduce false positives you will be reducing detection range of real positives as well. And a large part of the reaosn for that is that relative to the ground different parts of the blades are all travelling at different speeds, so the return will be a spread spectrum smear. wideband noise. Unfortunately a rotating tornado is pretty much the same.
And the same problem manifests itself in adaptive digital communications of all sorts – mobile phone and terrestrial digital television. The algorithms can cope wit static reflections by fancy DSP algorithms, but rapidly changing ones are simply almost impossible to compensate for.
Al in all, where RF is concerned wind farms are a problem we would all rather do without.
Just another damaging environmental effect to offset the benefit they don’t actually in real terms generate anyway.
As an electrical and electronic engineer who has worked of radio and radar in the past, and has a decent understanding of the theory and practice of heat engines electrical machines and the like, as well as more than a passing acquaintance with cost accounting, I can assure you that of all the possible ways to power an electricity grid, with the possible exception of methedrine infused rats on a treadmill, wind power is the worst.
Wind farms contribute nothing to CO2 emission reduction in the vast majority of cases either. Certainly in terms of bang for your buck, a nuclear power stations is infinitely more effective with far fewer downsides and irresolvable issues.
If you consider emissions reduction a target worth pursuing, at all.
Jquip:
Plalm plant to forehead…
How did modeling get into the mildly technical discussion about RADAR.
As for RADAR standing for Radio Assessment of Direction And Range that is new to me. I was taught by the US Navy thirty years ago that RADAR stand for Radio, Detection, and Ranging.
As for Scott, yes. The SPY-1(series) radar on Aegis cruisers and destroyers posses unique operating challenges. The electronic equipment on helo’s is degraded by the radio waves put out by a SPY-1 radar system among other things.
As for a Sea Story – a young radar tech (female) who repaired radars set for P-3 and helo in Sigonella, Sicily would aim the transmitter of the radars at the men in her workshop then laugh when they complained that the work space was hot.
A microwave oven is the transmitting half of a radar…
From my early days in the TV biz: A friend is filling in as the weekend weather guy on the Dallas CBS affiliate in 1970. It’s a clear night and he switches the WWII-era radar on to show folks. “No clouds out there tonight. What you see to the left is just ground clutter. The people who live there call it Fort Worth.”
He was suspended for three days.
It’ll be kinda funny when a Tornado dead centers one of those wind farms and no one sees it coming. (hopefully those sites are uninhabited so there will be no human casualties)
Given where they are, you know it’s going to happen sooner or later.
I was taught by the US Navy thirty years ago that RADAR stand for Radio, Detection, and Ranging.
Likewise – 50 years ago. It might be a Brit thing though.
Leo says “yes” but I’m going to disagree; siting is ALWAYS a concern for life, particularly human life (termed “exposure”). This means that ‘potent’ power levels are well away from ground level *and* the lowest elevation angle is restricted to 1/2 degree in the case of the WSR-88D dish antenna when mounted to its corresponding rotating ‘pedestal’.
Also, any power levels that could influence temperature measuring apparatus would also have to have an ‘apparatus’ made out of materials that were *absorptive* to RF energy, not merely transparent (plastics, glass) or reflective (metal).
Anything equipped with only a thermistor and a ‘dumb’ meter movement is impervious to RF effects (neglecting direct exposure resulting in physical temperature rise.) Once PN junctions (diode and transistors) are introduced ‘RF effects’ become (owing to ‘rectification’ of the RF energy) a consideration, whether from cell phones of 5-Watt handheld two-way radios (at close range) or nearby paging or broadcast transmitters. Proper circuit design (bypassing and RF filtering of power and I/O leads and wiring) and adherence to EMI susceptibility testing would yield a product suitable for field use most anywhere.
I recall an occasion years ago near a pier (may have Muskegon) on Lake Michigan where a small boat with a shipboard navigation RADAR passing up the channel would induce a ‘zing’ sound in a portable AM/FM/SW radio I had when the orange-peel dish rotated past my position … years later I could create the same effect at about 30 or 40 feet with an AN/SPS-35 3 cm (10 GHz) shipboard RADAR which had a peak-pulse power of around 10 kW and an average power of just a few Watts … I suspect it was either the envelope (the AM) detector or maybe the audio stages themselves which ‘saw’ the RF energy and rendered the ‘zing’ (due to the PRF or pulse repetition frequency) form the operating RADAR set.
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I submit one refer to page 1 of the pre-internet age publication titled: “Introduction to RADAR Systems” by Merril I. Skolnick. Lacking that, maybe an old copy of the MIT “Rad Lab” (Radiation Laboratory) series: http://www.jlab.org/ir/MITSeries.html About the MIT Rad Lab series: After the end of World War II, the United States government continued to pay key people who had worked at the Radiation Laboratory for six months to enable them to write about their work.
From: “Radar System Engineering, volume 1 (by Louis N. Ridenour) of MIT Radiation Laboratory Series. McGraw-Hill, New York, 1947” we find on page 3:
The coined word radar is derived from the descriptive phrase “radio detection and ranging.”
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In summary, corrective measures amount to judiciously junking data. How far can that go before forecasting capacity is itself junked? How many man-hours are eaten by this effort to degrade available data?
A couple tornado touchdowns and runs through these installations should fix the problem by deconstructing the irritant and escalating the financial risk associated with building them in these dangerous areas.