An aircraft study of turbulence dissipation rate and temperature structure function in the unstable marine atmospheric boundary layer

1980 ◽  
Vol 19 (4) ◽  
pp. 453-469 ◽  
Author(s):  
C. W. Fairall ◽  
Ralph Markson ◽  
G. E. Schacher ◽  
K. L. Davidson
1998 ◽  
Vol 37 (3) ◽  
pp. 308-324 ◽  
Author(s):  
Stephen P. Palm ◽  
Denise Hagan ◽  
Geary Schwemmer ◽  
S. H. Melfi

Abstract A new technique for retrieving near-surface moisture and profiles of mixing ratio and potential temperature through the depth of the marine atmospheric boundary layer (MABL) using airborne lidar and multichannel infrared radiometer data is presented. Data gathered during an extended field campaign over the Atlantic Ocean in support of the Lidar In-space Technology Experiment are used to generate 16 moisture and temperature retrievals that are then compared with dropsonde measurements. The technique utilizes lidar-derived statistics on the height of cumulus clouds that frequently cap the MABL to estimate the lifting condensation level. Combining this information with radiometer-derived sea surface temperature measurements, an estimate of the near-surface moisture can be obtained to an accuracy of about 0.8 g kg−1. Lidar-derived statistics on convective plume height and coverage within the MABL are then used to infer the profiles of potential temperature and moisture with a vertical resolution of 20 m. The rms accuracy of derived MABL average moisture and potential temperature is better than 1 g kg−1 and 1°C, respectively. The method relies on the presence of a cumulus-capped MABL, and it was found that the conditions necessary for use of the technique occurred roughly 75% of the time. The synergy of simple aerosol backscatter lidar and infrared radiometer data also shows promise for the retrieval of MABL moisture and temperature from space.


2020 ◽  
Vol 77 (7) ◽  
pp. 2311-2326
Author(s):  
Hubert Luce ◽  
Lakshmi Kantha ◽  
Hiroyuki Hashiguchi ◽  
Abhiram Doddi ◽  
Dale Lawrence ◽  
...  

AbstractUnder stably stratified conditions, the dissipation rate ε of turbulence kinetic energy (TKE) is related to the structure function parameter for temperature , through the buoyancy frequency and the so-called mixing efficiency. A similar relationship does not exist for convective turbulence. In this paper, we propose an analytical expression relating ε and in the convective boundary layer (CBL), by taking into account the effects of nonlocal heat transport under convective conditions using the Deardorff countergradient model. Measurements using unmanned aerial vehicles (UAVs) equipped with high-frequency response sensors to measure velocity and temperature fluctuations obtained during the two field campaigns conducted at Shigaraki MU observatory in June 2016 and 2017 are used to test this relationship between ε and in the CBL. The selection of CBL cases for analysis was aided by auxiliary measurements from additional sensors (mainly radars), and these are described. Comparison with earlier results in the literature suggests that the proposed relationship works, if the countergradient term γD in the Deardorff model, which is proportional to the ratio of the variances of potential temperature θ and vertical velocity w, is evaluated from in situ (airplane and UAV) observational data, but fails if evaluated from large-eddy simulation (LES) results. This appears to be caused by the tendency of the variance of θ in the upper part of the CBL and at the bottom of the entrainment zone to be underestimated by LES relative to in situ measurements from UAVs and aircraft. We discuss this anomaly and explore reasons for it.


2021 ◽  
Author(s):  
Pierre-Etienne Brilouet ◽  
Marie Lothon ◽  
Sandrine Bony

<p>Tradewind clouds can exhibit a wide diversity of mesoscale organizations, and the turbulence of marine atmospheric boundary layer (MABL) can exhibit coherent structures and mesoscale circulations. One of the objectives of the EUREC4A (Elucidating the role of cloud-circulation coupling in climate) field experiment was to better understand the tight interplay between the mesoscale organization of clouds, boundary-layer processes, and the large-scale environment.</p><p>During the experiment, that took place East of Barbados over the Western Tropical Atlantic Ocean in Jan-Feb 2020, the French ATR-42 research aircraft was devoted to the characterization of the cloud amount and of the subcoud layer structure. <span>During its 17 research flights, </span><span>it</span> <span>sampled a </span><span>large diversity of large scale conditions and </span><span>cloud patterns</span><span>. </span>Multiple sensors onboard t<span>he aircraft measure</span><span>d</span> <span>high-frequency </span><span>fluctuations of potential temperature, water vapour mixing ratio and wind , allowing </span><span>for </span><span>an extensive characterization </span><span> of</span><span> the turbulence </span><span>within</span><span> the subcloud layer. </span> <span>A </span><span>quality-controled and calibrated turbulence data</span><span>set</span><span> was produced </span><span>on the basis of these measurements</span><span>, which is now </span><span> available on the EUREC4A AERIS data portal.</span></p><p><span>The </span><span>MABL </span><span>turbulent </span><span>structure i</span><span>s</span><span> studied </span><span>using this dataset, </span><span>through a spectral analysis </span><span>of the vertical velocity</span><span>. Vertical profiles of characteristic length scales reveal a non-isotropic structure with a stretching of the eddies along the mean wind. The organization strength of the turbulent field is also explored </span><span>by defining</span><span> a diagnostic based on the shape of the vertical velocity spectrum. </span><span>The </span><span>structure and the degree of organization of the </span><span>subcloud layer </span><span>are</span><span> characterized for </span><span> different type</span><span>s</span><span> of mesoscale </span><span>convective </span><span>pattern </span><span>and </span><span>as a function of</span><span> the large-scale environment, </span><span>including</span> <span>near-</span><span>surface wind </span><span>and</span> <span>lower-</span><span>tropospheric</span><span> stability conditions.</span></p><p> </p>


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