the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Momentum fluxes from airborne wind measurements in three cumulus cases over land
- 1Delft University of Technology
- 2Deutsches Zentrum für Luft- und Raumfahrt
- 1Delft University of Technology
- 2Deutsches Zentrum für Luft- und Raumfahrt
Abstract. This study combines airborne Doppler Wind Lidar (DWL) observations with high-frequency in situ wind measurements from a gust probe, a combination that to our knowledge has not been used before. The two measurement techniques show a similar mean in the wind components throughout the flights and are then used to study momentum transport in relation to shallow cumulus over land. We present three case studies ranging from forced cumulus humilis to thicker clouds associated with stronger popcorn-like convection after a cold front passage. The wind profiles obtained with the DWL are helpful in explaining the momentum fluxes that are calculated from the 100 Hz in situ data using the eddy covariance method. Most of the momentum flux profiles revealed down-gradient momentum transport that was generally strongest within the mixed-layer and decreasing towards cloud tops. Comparing clear-sky and cloud-topped transects, the cloudy skies revealed a substantial enhancement in the mixed-layer momentum flux (more than twice as much). On one track during the third flight, after a post-cold-front passage and displaying thicker clouds, shows a momentum flux profile that did not decrease linearly with height as expected from shear-driven small-scale turbulence. The momentum in the mixed layer was very small, but a very strong flux has been observed in the cloud layer. Moreover, the updraft contribution to the flux was much larger in this case than in all other tracks that have been flown during the campaign. Last, we look into how much flux the different scales contribute to the overall transport. There we find that the largest scales (up to 7 km) usually carry most flux. However, sometimes the larger scales have opposite contribution to the flux than the scales smaller than 7 km, which can then result in a smaller or almost no net flux.
Ada Mariska Koning et al.
Status: closed
- RC1: 'Comment on acp-2022-59', Margaret LeMone, 08 Mar 2022
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RC2: 'Reviewer CFairall Comment on acp-2022-59', Christopher Fairall, 08 Mar 2022
This paper is a description of a dual aircraft observations of cloudy boundary layers in Germany. One aircraft made turbulence and mean profiles and the other overflew the first aircraft with a down-looking profiling Doppler wind lidar (DWL). The DWL used the VAD method processed in legs of 7 km length and about 2 km width. The flights were made on three days with quite different cloud properties but broken convective cloud were the dominant mode. The analysis includes scale dependence of the fluxes, flux-gradient relationships, updraft portioning, and turbulence distributions.
The paper is well reasonably written; the introduction section is thorough and well referenced and does a good job of setting the stage. Dual aircraft studies of this type are unusual and the DWL wind profiles are of high quality and add significant value to interpretation of flux profiles. The conditions are so different on each day (indeed, almost on each segment) that the results cannot really be combined – they serve mostly to indicate how different turbulent transport can vary with cloud mode. So the paper provides little closure – it is mostly in the vein of “On this day the clouds were like this and the profiles were like this, whereas on this day…”. The various descriptors are somewhat convoluted (subcloud, east track, clear sky, etc) that I found it difficult to synthesize anything. I am not an expert in convective theory or analysis so perhaps the results speak volumes to those practitioners. Because the paper shows the potential of this observation technique and the results show amusing variability that suggest a larger, more systematic study, I recommend the paper be published. I have a few minor editorial suggestions listed below.
Figure 2 would be more illuminating to turbulence people is the streamwise, cross stream, and vertical spectral components were presented on the same graph. Perhaps a 4-panel figure with a panel for a different height. Also, suggest plotting frequency*Spectrum to be area conserving in log-log space.
*Computation of fluxes via eq 1 is equivalent sampling the time series with a square window, computed the cospectra, and integrating that to get <w’x’>. The authors use Hann window for their variance spectra, which has advantages over the square window. I suggest they use Hann or Hamming window for flux computation. This will reduce leakage from lower frequencies.
*Eq 2 introduces the K coefficient but the authors don’t do anything with it except to say fluxes tend to be down gradient. I would not mind seeing some values and relationship to sigma_w and/or the scale associated with the peak of the vertical velocity spectrum.
*I did not find Figs. 10 and 11 to be that helpful. Perhaps they could be dropped.
- AC1: 'Final response to reviews', Mariska Koning, 08 May 2022
Status: closed
- RC1: 'Comment on acp-2022-59', Margaret LeMone, 08 Mar 2022
-
RC2: 'Reviewer CFairall Comment on acp-2022-59', Christopher Fairall, 08 Mar 2022
This paper is a description of a dual aircraft observations of cloudy boundary layers in Germany. One aircraft made turbulence and mean profiles and the other overflew the first aircraft with a down-looking profiling Doppler wind lidar (DWL). The DWL used the VAD method processed in legs of 7 km length and about 2 km width. The flights were made on three days with quite different cloud properties but broken convective cloud were the dominant mode. The analysis includes scale dependence of the fluxes, flux-gradient relationships, updraft portioning, and turbulence distributions.
The paper is well reasonably written; the introduction section is thorough and well referenced and does a good job of setting the stage. Dual aircraft studies of this type are unusual and the DWL wind profiles are of high quality and add significant value to interpretation of flux profiles. The conditions are so different on each day (indeed, almost on each segment) that the results cannot really be combined – they serve mostly to indicate how different turbulent transport can vary with cloud mode. So the paper provides little closure – it is mostly in the vein of “On this day the clouds were like this and the profiles were like this, whereas on this day…”. The various descriptors are somewhat convoluted (subcloud, east track, clear sky, etc) that I found it difficult to synthesize anything. I am not an expert in convective theory or analysis so perhaps the results speak volumes to those practitioners. Because the paper shows the potential of this observation technique and the results show amusing variability that suggest a larger, more systematic study, I recommend the paper be published. I have a few minor editorial suggestions listed below.
Figure 2 would be more illuminating to turbulence people is the streamwise, cross stream, and vertical spectral components were presented on the same graph. Perhaps a 4-panel figure with a panel for a different height. Also, suggest plotting frequency*Spectrum to be area conserving in log-log space.
*Computation of fluxes via eq 1 is equivalent sampling the time series with a square window, computed the cospectra, and integrating that to get <w’x’>. The authors use Hann window for their variance spectra, which has advantages over the square window. I suggest they use Hann or Hamming window for flux computation. This will reduce leakage from lower frequencies.
*Eq 2 introduces the K coefficient but the authors don’t do anything with it except to say fluxes tend to be down gradient. I would not mind seeing some values and relationship to sigma_w and/or the scale associated with the peak of the vertical velocity spectrum.
*I did not find Figs. 10 and 11 to be that helpful. Perhaps they could be dropped.
- AC1: 'Final response to reviews', Mariska Koning, 08 May 2022
Ada Mariska Koning et al.
Ada Mariska Koning et al.
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