Impact of Rotorwash on Atmospheric Profiling

Ryan Martz Author

08/02/2021 Added

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This research focuses on using rotary wing UAVs for atmospheric profiling as a supplement to weather balloon soundings

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[00:00:01.710]Hello.
[00:00:02.070]My name is Ryan Martz and I am a junior meteorology major with minors in math,
[00:00:05.910]Spanish, computer science, and applied climatology.
[00:00:09.300]My UCARE project this summer was to investigate the usefulness of rotary wing
[00:00:12.480]Unmanned Aerial Vehicles,
[00:00:13.980]which will be referred to as UAVs for the rest of this video for taking vertical
[00:00:17.760]atmospheric profiles.
[00:00:19.230]Atmospheric profiles are measurements of how atmospheric properties like
[00:00:22.170]temperature and humidity change with height.
[00:00:26.100]The most prominent current method for atmospheric profiling is by many National
[00:00:29.670]Weather Service sites launching two weather balloons each day,
[00:00:32.400]12 hours apart, equipped with a measurement device called a radiosonde.
[00:00:36.090]The problem with this is that these measurements are spaced quite far apart by
[00:00:39.270]both distance and time. So it is hard to get a clear picture of the atmosphere.
[00:00:43.530]Additionally,
[00:00:44.100]weather balloons are not often retrieved, so they generate a lot of waste.
[00:00:47.460]For certain situations,
[00:00:48.630]a clear picture of the current state of the atmosphere is vital for accurate
[00:00:51.840]forecasts,
[00:00:52.680]with one such situation being short-term severe weather forecasting. UAVs could
[00:00:57.210]fill in the gaps because they can be launched often and be reused,
[00:01:00.000]so there are fewer cost and waste concerns,
[00:01:02.340]and since it requires less equipment to take a profile with a UAV,
[00:01:05.610]they could be launched from more locations when a severe weather event is taking
[00:01:08.640]place. However, a problem with UAV is,
[00:01:13.530]is that the spinning rotors pull air from above and push it out below.
[00:01:16.920]So the air that we are measuring on the UAV may be coming from a different
[00:01:19.890]height, and this has been shown by some experiments,
[00:01:21.960]but few have tried to quantify it with the focus of atmospheric measurements.
[00:01:25.830]One such experiment that did try to quantify this occurred in 2015,
[00:01:29.310]but it was conducted within about five meters of the ground.
[00:01:31.770]And as we can see from the figure on the right
[00:01:33.390]from a study conducted by Zheng et al in 2018,
[00:01:36.270]the wake still interacts with the ground at about five meters,
[00:01:39.180]so the ground pushing the air back up could have influenced their finding of an
[00:01:42.180]average of a two meter rotor wash.
[00:01:45.870]Based on previous research,
[00:01:47.250]I expected to find a rotor wash effect of about two to three meters,
[00:01:50.760]depending on several factors including wind speed, atmospheric stability,
[00:01:54.300]the size of the rotors, and the ascent and descent speeds of the drone during the
[00:01:57.600]profile. However,
[00:01:58.950]one important detail to analyze was whether any rotor wash that was found had a
[00:02:02.430]significant impact on the temperature recordings.
[00:02:04.920]That is to say what a rotor wash of two to three meters,
[00:02:07.260]if true, impact the observations of temperature more than just the natural
[00:02:11.100]margin of error of the temperature sensors, which was 0.3 degrees Celsius.
[00:02:15.060]During this experiment,
[00:02:18.660]hundreds of profiles were conducted over four days at the Southern Great Plains
[00:02:22.230]ARM research facility in Lamont, Oklahoma.
[00:02:25.260]My research focuses on the flights from March 31st and April 1st when the
[00:02:28.740]flights were conducted near a 60 meter tower with reference sensors at 25 meters
[00:02:32.730]and 60 meters.
[00:02:34.260]Each flight had two to six profiles and sometimes a several minute loader period
[00:02:37.920]at 60 meters.
[00:02:39.300]The flights varied in atmospheric conditions with wind speeds and atmospheric
[00:02:42.510]stability. And they also varied in terms of the M600 itself.
[00:02:46.710]The ascent speed was either one or three meters per second.
[00:02:49.170]And the sensor housings,
[00:02:50.610]the white trumpets in the image, were rotated so that they faced inward instead
[00:02:53.910]of outward for some flights. Lastly,
[00:02:56.280]the M600 itself was rotated so that different sensors were facing into or out
[00:03:00.880]of the wind on different flights. In addition to the 24 hour tower data,
[00:03:05.170]there were nine soundings each day and a ground reference called a Comet with
[00:03:08.440]continuous data for much of each day.
[00:03:12.640]There were several data sets that needed corrections. First,
[00:03:15.850]the XQ sensors that were attached to the M600,
[00:03:18.550]shown in the yellow boxes before, were allowed to record for several minutes at ground
[00:03:22.600]level
[00:03:22.930]at the same time as the Comet to allow for an inter comparison to identify
[00:03:26.320]biases.
[00:03:27.850]The tower temperature data were compared with contemporaneous soundings to
[00:03:31.090]determine biases in the tower data.
[00:03:33.970]Because the M600 data had unreliable altitude and the tower had no pressure
[00:03:38.080]observations,
[00:03:39.310]I needed to use the pressure readings from another drone called the meteodrone
[00:03:42.580]to estimate the pressure at 25 meters and 60 meters,
[00:03:45.670]so the tower and M600 data could be compared. Lastly,
[00:03:49.260]the M600 profiles needed a lag correction due to the fact that the
[00:03:52.750]sensors take time to record the temperatures after sensing them.
[00:03:59.110]All of my analysis, including the dividing of flights by atmospheric stability,
[00:04:02.770]was done using potential temperature as opposed to regular temperature.
[00:04:06.820]First I identified the difference in potential temperature from the low,
[00:04:09.910]from the tower to the M600,
[00:04:11.680]when the M600 reached the same pressure to see if the difference was within the
[00:04:15.280]XQ sensors' margin of error. Second, I identified the pressure.
[00:04:19.060]at which the XQ on the M600 reached the same potential temperature as the
[00:04:22.870]tower to calculate a rotor wash.
[00:04:25.330]The difference in pressure can be used to estimate a difference in meters
[00:04:28.210]because the change in pressure with respect to height is nearly linear
[00:04:31.230]nearly linear,
[00:04:32.560]so close to the surface. To analyze the loiter period I identified two points
[00:04:37.570]during the loiter to compare with the tower.
[00:04:40.180]I used the potential temperature difference in combination with the lapse rate
[00:04:43.450]around the loiter pressure to calculate a rotor wash. Lastly,
[00:04:47.140]I used two pressures, one, three hecta pascals lower than the first point,
[00:04:51.760]and one six hecta pascals lower than the first point of the ascent to
[00:04:55.330]calculate the difference between the ascent and descent in a similar manner to the
[00:04:58.750]tower. In this case, the ascent was the reference like the tower was,
[00:05:02.500]and the descent was the experimental observation, like the M600.
[00:05:05.710]was before.
[00:05:09.460]The results of this experiment were inconclusive for both the inversion flights
[00:05:13.060]and the superadiabatic flights.
[00:05:14.710]The difference in potential temperature at the same pressure on the tower,
[00:05:17.710]was within the margin of error of the sensors on most profiles.
[00:05:21.070]So any difference in temperature was not significant enough to be attributed
[00:05:24.250]to rotor wash. However, this does not mean that there is no rotor wash effect.
[00:05:28.630]One possibility is that the housing pictured previously was successful in
[00:05:31.870]sampling air from outside the rotor wash zone.
[00:05:35.260]Another possibility is that the inversions and superadiabatic layers were
[00:05:38.500]simply not strong enough for rotor wash to make a difference. With the lapse
[00:05:42.160]rates in the profiles for this experiment,
[00:05:43.960]the rotor wash effect would have needed to be larger much larger than two to
[00:05:47.020]three meters.
[00:05:48.160]It would've needed to be on the scale of 30 or more meters to get a significant
[00:05:51.610]potential temperature difference. There are times however, when
[00:05:56.530]an inversion could be strong enough to generate such steep lapse rates that
[00:05:59.720]rotor wash could have an impact.
[00:06:01.850]One strategy to identify this would be to fly early in the morning after the
[00:06:06.170]inversion has had an entire night to strengthen and also flying in the winter
[00:06:09.950]would give a better chance of a stronger inversion as well. Additionally,
[00:06:13.610]being able to fly the meteodrone or another drone without the sensor housing
[00:06:16.850]could be helpful to determine the impact of the sensor housing on mitigating the
[00:06:20.180]rotor wash. For this research.
[00:06:22.160]I am planning to continue a little bit farther to fine tune the tower
[00:06:25.460]temperature bias,
[00:06:26.750]as well as investigating the loiter periods of the flights to see if these
[00:06:29.690]periods show any rotor wash effect.
[00:06:32.030]I'm hoping to present my full results at the American meteorological society's
[00:06:35.600]annual meeting in January. Acknowledgments:
[00:06:40.280]Dr. Adam Houston,
[00:06:41.360]my advisor helped guide me through each step of the research process.
[00:06:45.350]Daniel Rico and Ashraful Islam helped us set up the experiments and Ashraful
[00:06:49.430]piloted the M600 drone. Dr.
[00:06:52.640]Carrick Detweiler also helped with experiments set up and took the pictures of
[00:06:56.540]the M600 used in this presentation and the ARM SGP site provided
[00:07:01.490]vital reference data through the tower onsite.
[00:07:04.160]And many of the radiosondes soundings that were taken. Thank you for your time.

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Impact of Rotorwash on Atmospheric Profiling

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