The role of thermals in the convective boundary layer

Lenschow, Donald H.; Stephens, Pamela L.

doi:10.1007/bf00122351

Cited by 211 publications

(140 citation statements)

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“…Conditions ideal for thermal soaring typically occur during a sunny day, when a strong temperature gradient between the surface of the Earth and the top of the atmospheric boundary layer creates convective thermals (7,8). The soaring of birds and gliders primarily occurs within this convective boundary layer.…”

Section: Modelsmentioning

confidence: 99%

“…Hydrodynamic instabilities and processes that lead to the formation of a thermal inevitably give rise to a turbulent environment characterized by strong, erratic fluctuations (7,8). Birds or gliders attempting to find and maintain a thermal face the challenge of identifying the potentially longlived and large-scale wind fluctuations amid a noisy turbulent background.…”

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confidence: 99%

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Learning to soar in turbulent environments

Reddy

Celani

Sejnowski

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

146

115

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Birds and gliders exploit warm, rising atmospheric currents (thermals) to reach heights comparable to low-lying clouds with a reduced expenditure of energy. This strategy of flight (thermal soaring) is frequently used by migratory birds. Soaring provides a remarkable instance of complex decision making in biology and requires a longterm strategy to effectively use the ascending thermals. Furthermore, the problem is technologically relevant to extend the flying range of autonomous gliders. Thermal soaring is commonly observed in the atmospheric convective boundary layer on warm, sunny days. The formation of thermals unavoidably generates strong turbulent fluctuations, which constitute an essential element of soaring. Here, we approach soaring flight as a problem of learning to navigate complex, highly fluctuating turbulent environments. We simulate the atmospheric boundary layer by numerical models of turbulent convective flow and combine them with model-free, experience-based, reinforcement learning algorithms to train the gliders. For the learned policies in the regimes of moderate and strong turbulence levels, the glider adopts an increasingly conservative policy as turbulence levels increase, quantifying the degree of risk affordable in turbulent environments. Reinforcement learning uncovers those sensorimotor cues that permit effective control over soaring in turbulent environments.thermal soaring | turbulence | navigation | reinforcement learning M igrating birds and gliders use upward wind currents in the atmosphere to gain height while minimizing the energy cost of propulsion by the flapping of the wings or engines (1, 2). This mode of flight, called soaring, has been observed in a variety of birds. For instance, birds of prey use soaring to maintain an elevated vantage point in their search for food (3); migrating storks exploit soaring to cover large distances in their quest for greener pastures (4). Different forms of soaring have been observed. Of particular interest here is thermal soaring, where a bird gains height by using warm air currents (thermals) formed in the atmospheric boundary layer. For both birds and gliders, a crucial part of the thermal soaring is to identify a thermal and to find and maintain its core, where the lift is typically the largest. Once migratory birds have climbed up to the top of a thermal, they glide down to the next thermal and repeat the process, a migration strategy that strongly reduces energy costs (4). Soaring strategies are also important for technological applications, namely, the development of autonomous gliders that can fly large distances with minimal energy consumption (5).Thermals arise as ascending convective plumes driven by the temperature gradient created due to the heating of the earth's surface by the sun (6). Hydrodynamic instabilities and processes that lead to the formation of a thermal inevitably give rise to a turbulent environment characterized by strong, erratic fluctuations (7,8). Birds or gliders attempting to find and maintain a thermal face...

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Section: Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Learning to soar in turbulent environments

Reddy

Celani

Sejnowski

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

146

115

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“…It is well known that vertical mixing of heat, moisture, momentum, aerosols, and gaseous pollution in the unstable atmospheric boundary layer (ABL) is predominantly carried out by motions occurring within discrete elements of considerable vertical extent (Lenschow, 1970;Lenschow and Stephens, 1980;Khalsa, 1982, 1987;Khalsa and Greenhut, 1985;Young, 1988a,b,c). Convectively driven updrafts formed by coalescence of smaller surface-based buoyant elements often extend through the depth of the wellmixed layer.…”

Section: Introductionmentioning

confidence: 99%

“…More than 20 years after the pioneering work by Lenschow, Greenhut, Khalsa, and Young, a first comprehensive lidar-based study of updraft and downdraft occurrence frequencies, occurrence durations, corresponding horizontal extents, and mean vertical velocities of updrafts and downdrafts is presented. In contrast to airborne in situ observations (Lenschow and Stephens, 1980;Greenhut and Khalsa, 1982;Khalsa and Greenhut, 1985;Godowitch, 1986;Young, 1988b;Williams and Hacker, 1992;Durand et al, 2000;Said et al, 2009), Doppler lidar allows us to monitor the entire mixed layer including the entrainment zone vertically resolved and continuously over long time periods so that a detailed study of the full evolution cycle of the ABL over the day is possible (Grund et al, 2001;Bösenberg and Linné, 2002;Drobinski et al, 2004;Wulfmeyer and Janjić, 2005;Lothon et al, 2006;Gibert et al, 2007;Engelmann et al, 2008;Hogan et al, 2009).…”

mentioning

confidence: 99%

Updraft and downdraft characterization with Doppler lidar: cloud-free versus cumuli-topped mixed layer

2010

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Abstract. For the first time, a comprehensive, heightresolved Doppler lidar study of updrafts and downdrafts in the mixing layer is presented. The Doppler lidar measurements were performed at Leipzig, Germany, in the summer half year of 2006. The conditional sampling method is applied to the measured vertical velocities to identify, count, and analyze significant updraft and downdraft events. Three cases of atmospheric boundary-layer (ABL) evolution with and without fair-weather cumuli formation are discussed. Updrafts occur with an average frequency of 1-2 per unit length z i (boundary-layer depth z i ), downdrafts 20-30% more frequently. In the case with cumuli formation, the draft occurrence frequency is enhanced by about 50% at cloud level or near cloud base. The counted updraft events cover 30-34%, downdrafts 53-57% of the velocity time series in the central part of the ABL (subcloud layer) during the main period of convective activity. By considering all drafts with horizontal extent >36 m in the analysis, the updraft mean horizontal extent ranges here from 200-420 m and is about 0.16 z i -0.18 z i in all three cases disregarding the occurrence of cumulus clouds. Downdraft extents are a factor of 1.3-1.5 larger. The average value of the updraft mean vertical velocities is 0.5-0.7 m/s or 0.40 w * -0.45 w * (convective velocity scale w * ), and the negative downdraft mean vertical velocities are weaker by roughly 10-20%. The analysis of the relationship between the size (horizontal extent) of the updrafts and downdrafts and their mean vertical velocity reveals a pronounced increase of the average vertical velocity in updrafts from 0.4-0.5 m/s for small thermals (100-200 m) to about 1.5 m/s for large updrafts (>600 m) in the subcloud layer in the case with fair-weather cumuli. At cloudless conditions, the updraft velocities were found to be 20% smaller for the large thermals.

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“…They usually embrace the entire convective boundary layer (of the order of 1-3 km in height) and include pronounced convergence flow patterns close to the surface. In the sheared convective flows, the structures represent largescale rolls (cloud streets) stretched along the mean wind [1,2,12] Coherent structures in convective turbulent flows were comprehensively studied theoretically, experimentally and in numerical simulations [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28]. However, some aspects related to the origin of large-scale coherent structures in non-rotating turbulent convection are not completely understood.…”

Section: Introductionmentioning

confidence: 99%

Large-scale instabilities in a nonrotating turbulent convection

2006

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A theoretical approach proposed in Phys. Rev. E 66, 066305 (2002) is developed further to investigate formation of large-scale coherent structures in a non-rotating turbulent convection via excitation of a large-scale instability. In particular, the convective-wind instability that causes formation of large-scale coherent motions in the form of cells, can be excited in a shear-free regime. It was shown that the redistribution of the turbulent heat flux due to non-uniform large-scale motions plays a crucial role in the formation of the coherent large-scale structures in the turbulent convection. The modification of the turbulent heat flux results in strong reduction of the critical Rayleigh number (based on the eddy viscosity and turbulent temperature diffusivity) required for the excitation of the convective-wind instability. The large-scale convective-shear instability that results in the formation of the large-scale coherent motions in the form of rolls stretched along imposed large-scale velocity, can be excited in the sheared turbulent convection. This instability causes the generation of convective-shear waves propagating perpendicular to the convective rolls. The mean-field equations which describe the convective-wind and convective-shear instabilities, are solved numerically. We determine the key parameters that affect formation of the large-scale coherent structures in the turbulent convection. In particular, the degree of thermal anisotropy and the lateral background heat flux strongly modify the growth rates of the large-scale convective-shear instability, the frequencies of the generated convective-shear waves and change the thresholds required for the excitation of the large-scale instabilities. This study elucidates the origins of the large-scale circulations and rolls in the atmospheric convective boundary layers and the meso-granular structures in the solar convection.

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The role of thermals in the convective boundary layer

Cited by 211 publications

References 23 publications

Learning to soar in turbulent environments

Learning to soar in turbulent environments

Updraft and downdraft characterization with Doppler lidar: cloud-free versus cumuli-topped mixed layer

Large-scale instabilities in a nonrotating turbulent convection

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