the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jul 2024
The ASK-16 Motorized Glider: An Airborne Eddy Covariance Platform to measure Turbulence, Energy and Matter Fluxes
Abstract. Airborne eddy covariance measurements can bridge the gap between local (tower-based) to regional (satellite/inversion-derived) flux data, as they provide information about the spatial distribution of turbulent fluxes for larger regions. Here, we introduce an airborne eddy covariance measurement platform based on an ASK 16 touring motor glider (TMG; also referred to as a power glider, hereafter referred to as motorized glider), which is equipped to measure the three dimensional wind vector, atmospheric conditions and derive airborne turbulent fluxes for the use of measurement campaigns over European landscapes. This study describes the measurement setup of the platform, and explains the workflows that were used to calculate and calibrate the three-dimensional wind vector, turbulent fluxes and their associated source areas. The glider is equipped with an 858 AJ Rosemount five-hole probe, a Picarro G2311-f gas analyser, a Novatel FlexPak G2-V2 GNSS-INS system, Vaisala temperature and humidity sensors (HMT311), and an OMEGA CHAL-003 thermocouple temperature sensor. Measurement data is processed with PyWingpod (python) and eddy4R (R) software packages to calculate wind vectors, turbulent fluxes, and assign footprints to the calculated fluxes. To evaluate the quality of the obtained fluxes, different quality assessments have been performed, including the determination of detection limits, spectral analysis, stationarity tests, the analysis of integral turbulence characteristics, and measurement noise and error evaluation. The uncertainty of w is between 0.15 to 0.27 m/s (median = 0.23 m/s) and the uncertainty of, u and v ranges between 0.16 to 0.55 m/s (median = 0.25 m/s). Analysis of exemplary flux data from flight transects indicates that the platform is capable of producing spatially highly resolved turbulent fluxes over heterogeneous landscapes. Overall, results from our analysis suggest that the ASK-16 airborne platform can measuring turbulent fluxes with a similar quality as earlier established high quality platforms.
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RC1: 'Comment on egusphere-2024-1586', Ronald Hutjes, 15 Aug 2024
This is a generally very well written and very solid documentation of the setup and calibration procedure for flux measurments from a unique low flying aircraft. A necessary basis for any follow-on studies using observations from this particular system, but as such there is little novelty in concepts or methods or data
In highlights + associated comments in the attached document I suggest a number of mere technical/editorial improvements.
In addition a few issues require a bit more attention, some preferably in the form of an explicit discussion.
The integration of the Picarro trace gas analyser is poorly documented. E.g. there must a significantly long inlet tube between instrument in cabin and inlet (I assume) in wing pod. What flow rate/pump was used? How were unavoidable significant time delays dealt with (documented a bit late in the text)? Why are lag times of H2O treated differently from CO2 and CH4 if all 3 signals come from the same instrument/setup?
A bit more discussion discussion and/or analysis of the differences/pros/cons of wavelet vs reynolds flux calculation would be welcome. Same for use of 2km integration windows with only 200m stepsize, which artificially reduces random errors and increases (sometime unrealisticly) autocorrelations between data points. Sharp transitions in surface fluxes (e.g. lake) might be unnecessary convoluted.
Since your turbulence probe is mounted in a wingpod, ie off the symmetry-axis of the fuselage, may be you can discuss a bit the implications this has (implicitly or explicitly) for the calibration procedures/parameterisations for the true wind vector.
Unlike your near-perfect wind spectra, for the other signals they are (much) less perfect. Can you show that this does not affect calculated fluxes as you claim, eg using cospectra? How might that change if you fly lower? Somewhat related, do you do any high/low freq corrections?
- AC1: 'Reply on RC1', Inge Wiekenkamp, 19 Sep 2024
- AC2: 'Reply on RC1', Inge Wiekenkamp, 20 Sep 2024
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RC2: 'Comment on egusphere-2024-1586', Anonymous Referee #2, 19 Aug 2024
The paper describes a new airborne system intended to measure fluxes via eddy covariance and wavelet methods. The paper describes the calibration of the system in detail and presents an example of data taken over Germany. Overall this is an excellently written paper. I think it does an good job of explaining all the different possible errors that go into making turbulent wind measurements from an aircraft. It does not spend much time on the uncertainties in the gas measurements themselves or on additional errors when these instruments are put on an aircraft. I’ve made some comments about this. The gas measurements are clearly not the focus of this paper and that is fine, but it might be worth pointing that out. At least that uncertainties in the gas measurements have not been evaluated.
I have some minors comments listed below
Line 96: insert ‘and’ before (2)
Lines 120:135: It would be good to include some detail on the placement of the gas sensors and their inlets distance from the wind probe in this section. Estimated time of flight between inlet and sensor.
Table 2: Have you evaluated the accuracy of the gas sensors? Short and long term drifts which particularly could effect your flux measurements. Depending on cell flush time, have you checked that the 10 Hz data are truly independent? Are the numbers here based on your own measurements using the sensors or just copied from the spec sheet from the company?
Line 169: data ‘were’ merged (not was)
Lines 224:226 You might want to add something about the lag between the Picarro sensors here or at least mention that it’ll be discussed later as this seems a natural section to the reader.
Line 406: Could you provide some rational to using 200 m and 2000m for the distances. Did you use Ogive analysis or some other method to determine the length needed to sum over relevant frequencies. 200 m seems especially short.
Line 620:621: Did you try doing a null experiment to check that the gas measurements are uncorrelated with the wind. In general I didn’t see any description of how you calibrated the gas sensors. While in theory the noise should be uncorrelated, there may be changes in alignment, valves (leading to pressure changes), etc. that may correlate with vertical turbulence but may appear to be noise. Running calibration gas through the system and seeing that you get something close to zero flux would show that there really was not correlation between the gas measurement ‘noise’ and atmospheric turbulence.
Line 680: Do you really mean uncertainty here or variability. You are listing uncertainty with an uncertainty. And given the range of uncertainty, it may be more than 100% of the flux?
Line 681: You have clearly used twice in a row.
Citation: https://doi.org/10.5194/egusphere-2024-1586-RC2 - AC3: 'Reply on RC2', Inge Wiekenkamp, 20 Sep 2024
Status: closed
-
RC1: 'Comment on egusphere-2024-1586', Ronald Hutjes, 15 Aug 2024
This is a generally very well written and very solid documentation of the setup and calibration procedure for flux measurments from a unique low flying aircraft. A necessary basis for any follow-on studies using observations from this particular system, but as such there is little novelty in concepts or methods or data
In highlights + associated comments in the attached document I suggest a number of mere technical/editorial improvements.
In addition a few issues require a bit more attention, some preferably in the form of an explicit discussion.
The integration of the Picarro trace gas analyser is poorly documented. E.g. there must a significantly long inlet tube between instrument in cabin and inlet (I assume) in wing pod. What flow rate/pump was used? How were unavoidable significant time delays dealt with (documented a bit late in the text)? Why are lag times of H2O treated differently from CO2 and CH4 if all 3 signals come from the same instrument/setup?
A bit more discussion discussion and/or analysis of the differences/pros/cons of wavelet vs reynolds flux calculation would be welcome. Same for use of 2km integration windows with only 200m stepsize, which artificially reduces random errors and increases (sometime unrealisticly) autocorrelations between data points. Sharp transitions in surface fluxes (e.g. lake) might be unnecessary convoluted.
Since your turbulence probe is mounted in a wingpod, ie off the symmetry-axis of the fuselage, may be you can discuss a bit the implications this has (implicitly or explicitly) for the calibration procedures/parameterisations for the true wind vector.
Unlike your near-perfect wind spectra, for the other signals they are (much) less perfect. Can you show that this does not affect calculated fluxes as you claim, eg using cospectra? How might that change if you fly lower? Somewhat related, do you do any high/low freq corrections?
- AC1: 'Reply on RC1', Inge Wiekenkamp, 19 Sep 2024
- AC2: 'Reply on RC1', Inge Wiekenkamp, 20 Sep 2024
-
RC2: 'Comment on egusphere-2024-1586', Anonymous Referee #2, 19 Aug 2024
The paper describes a new airborne system intended to measure fluxes via eddy covariance and wavelet methods. The paper describes the calibration of the system in detail and presents an example of data taken over Germany. Overall this is an excellently written paper. I think it does an good job of explaining all the different possible errors that go into making turbulent wind measurements from an aircraft. It does not spend much time on the uncertainties in the gas measurements themselves or on additional errors when these instruments are put on an aircraft. I’ve made some comments about this. The gas measurements are clearly not the focus of this paper and that is fine, but it might be worth pointing that out. At least that uncertainties in the gas measurements have not been evaluated.
I have some minors comments listed below
Line 96: insert ‘and’ before (2)
Lines 120:135: It would be good to include some detail on the placement of the gas sensors and their inlets distance from the wind probe in this section. Estimated time of flight between inlet and sensor.
Table 2: Have you evaluated the accuracy of the gas sensors? Short and long term drifts which particularly could effect your flux measurements. Depending on cell flush time, have you checked that the 10 Hz data are truly independent? Are the numbers here based on your own measurements using the sensors or just copied from the spec sheet from the company?
Line 169: data ‘were’ merged (not was)
Lines 224:226 You might want to add something about the lag between the Picarro sensors here or at least mention that it’ll be discussed later as this seems a natural section to the reader.
Line 406: Could you provide some rational to using 200 m and 2000m for the distances. Did you use Ogive analysis or some other method to determine the length needed to sum over relevant frequencies. 200 m seems especially short.
Line 620:621: Did you try doing a null experiment to check that the gas measurements are uncorrelated with the wind. In general I didn’t see any description of how you calibrated the gas sensors. While in theory the noise should be uncorrelated, there may be changes in alignment, valves (leading to pressure changes), etc. that may correlate with vertical turbulence but may appear to be noise. Running calibration gas through the system and seeing that you get something close to zero flux would show that there really was not correlation between the gas measurement ‘noise’ and atmospheric turbulence.
Line 680: Do you really mean uncertainty here or variability. You are listing uncertainty with an uncertainty. And given the range of uncertainty, it may be more than 100% of the flux?
Line 681: You have clearly used twice in a row.
Citation: https://doi.org/10.5194/egusphere-2024-1586-RC2 - AC3: 'Reply on RC2', Inge Wiekenkamp, 20 Sep 2024
Data sets
Airborne Wind and Eddy Covariance Dataset - Recorded with the ASK-16 EC Platform between 2017 – 2022 Inge Wiekenkamp et al. https://dataservices.gfz-potsdam.de/panmetaworks/review/d612e47508782aaca25613f742aadeecca78b0c93430e70912927aefa60a5140/
Model code and software
PyWingpod Inge Wiekenkamp et al. https://dataservices.gfz-potsdam.de/panmetaworks/review/d90fdfb42934c80433fd023573467869deff28106003933100d7f2d78892c1fd/
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