the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating turbulent energy flux vertical profiles from uncrewed aircraft system measurements: Exemplary results for the MOSAiC campaign
- 1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
- 2National Snow and Ice Data Center (NSIDC), University of Colorado Boulder, Boulder, CO, USA
- 3Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
- 4Smead Aerospace Engineering Sciences, University of Colorado, Boulder, CO, USA
- 5NOAA Physical Sciences Laboratory, Boulder, CO, USA
- 6Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
- anow at: National Renewable Energy Laboratory (NREL), Golden, CO, USA
- 1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
- 2National Snow and Ice Data Center (NSIDC), University of Colorado Boulder, Boulder, CO, USA
- 3Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
- 4Smead Aerospace Engineering Sciences, University of Colorado, Boulder, CO, USA
- 5NOAA Physical Sciences Laboratory, Boulder, CO, USA
- 6Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
- anow at: National Renewable Energy Laboratory (NREL), Golden, CO, USA
Abstract. This study analyzes turbulent energy fluxes in the Arctic atmospheric boundary layer (ABL) using measurements with a small Uncrewed Aircraft System (sUAS). Turbulent fluxes constitute a major part of the atmospheric energy budget and influence the surface heat balance by distributing energy vertically in the atmosphere. However, only few in-situ measurements exist of the vertical profile of turbulent fluxes in the Arctic ABL. The study presents a method to derive turbulent heat fluxes from DataHawk2 sUAS turbulence measurements, based on the flux gradient method with a parameterization of the turbulent exchange coefficient. This parameterization is derived from high-resolution horizontal wind speed measurements in combination with formulations for the turbulent Prandtl number and anisotropy depending on stability. Measurements were taken during the MOSAiC expedition in the Arctic sea ice during the melt season of 2020. For three example cases from this campaign, vertical profiles of turbulence parameters and turbulent heat fluxes are presented and compared to balloon-borne, radar and near-surface measurements. The combination of all measurements draws a consistent picture of ABL conditions and demonstrates the unique potential of the presented method for studying turbulent exchange processes in the vertical ABL profile with sUAS measurements.
Ulrike Egerer et al.
Status: open (until 07 Feb 2023)
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RC1: 'Comment on amt-2022-314', Anonymous Referee #1, 24 Jan 2023
reply
General comments:
This paper introduces an innovative effort to reconstruct turbulent energy fluxes in the Arctic ABL using limited measurements from an sUAS – DataHawk. Because there is no direct vertical velocity measurement from the DataHawk, the authors derived the flux gradient based on a parameterization, which combines the HR horizontal wind speed measurements with formulations for the turbulent dimensionless numbers and anisotropy depending on stability. This unique method has great potential to extract valuable information from the sUAS, especially in the undersampled area. However, the below clarifications should be made to aid the audience in understanding the approach and to evaluate the suitable parameter ranges for the application.
- Figure 2 shows a 2 s spectrum for pitot and hotwire fluctuations. Is 2 seconds interval spectrum sufficient for such a turbulence structure analysis? For example, a typical ECOR system uses at least 10 mins data.
- Line 200: How does the author select the value of C? What is the appropriate range of the C values? Similar situation for the appropriate range for the Kolmogorov constant (in line 222). Will you please provide a guideline for those parameters' determination? For example, are those constants unique to the Arctic environment or general cloudy conditions?
- How many flights do you compare the DH2 measurements with BELUGA? Do you have some statistics to confirm the DH2 performance?
- Figures 9, 10, and 11 show that the temperature difference between DH2 and BELUGA (for the potential temperature profiles) is 2-4 C. That is relatively large. Do you have any explanations for the data quality and the meaningfulness of using the comparison? Do you have other ground comparisons to determine the temperature measurement's uncertainty range? Similar concerns with the dissipation rate, wind speed variance and the gradient Richardson number. They were plotted on a log scale, and it is hard to understand how accurately the new approach derived parameters compared with BELUGA.
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Specific comments:
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Equation 9 used equation 10 in Siebert et al. (2006) for the u component. How do you derive C2 =2.6 for vertical velocity components?
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In section 2.3.1, the structure of this section is confusing. Before Line 232, the author introduced the method used by Siebert et al. (2006), then starting in line 332, "a different established method is applied to derive dissipation rates." Please list the equations for the other method. What are the connections between the two methods? Do you plan to compare them? Or do they complement each other? Which method is more suitable for the Arctic environment? What are the pros and cons of choosing each method in Fig 3?
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RC2: 'Comment on amt-2022-314', Anonymous Referee #2, 24 Jan 2023
reply
Please see attached referee report.
Ulrike Egerer et al.
Ulrike Egerer et al.
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