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Monin-Obukhov similarity theory (MOST) forms the basis for parametrizations of turbulent exchange in virtually all numerical models of atmospheric flows. Yet, its limitations to flat and horizontally homogeneous terrain have plagued the theory since its inception. Here we present a first generalized extension of MOST based on the inclusion of turbulence anisotropy as an additional nondimensional term. This novel theory developed based on an unprecedented ensemble of complex atmospheric turbulence datasets covering flat to mountainous terrain, is shown to be valid in conditions in which MOST fails and thus paves the way to a better understanding of complex turbulence.

It was discovered several decades ago that eddy covariance measurements systematically underestimate sensible and latent heat fluxes, creating an imbalance in the surface energy budget. Since then, many studies have addressed this problem and proposed a variety of solutions to the problem, including improvements to instruments and correction methods applied during data postprocessing. However, none of these measures have led to the complete closure of the energy balance gap. The leading hypothesis is that not only surface-attached turbulent eddies but also sub-mesoscale atmospheric circulations contribute to the transport of energy in the atmospheric boundary layer, and the contribution from organized motions has been grossly neglected. The problem arises because the transport of energy through these secondary circulations cannot be captured by the standard eddy covariance method given the relatively short averaging periods of time (~30 minutes) used to compute statistics. There are various approaches to adjust the measured heat fluxes by attributing the missing energy to the sensible and latent heat flux in different proportions. However, few correction methods are based on the processes causing the energy balance gap. Several studies have shown that the magnitude of the energy balance gap depends on the atmospheric stability and the heterogeneity scale of the landscape around the measurement site. Based on this, the energy balance gap within the surface layer has already been modelled as a function of a nonlocal atmospheric stability parameter by performing a large-eddy simulation study with idealized homogeneous surfaces. We have further developed this approach by including thermal surface heterogeneity in addition to atmospheric stability in the parameterization. Specifically, we incorporated a thermal heterogeneity parameter that was shown to relate to the magnitude of the energy balance gap. For this purpose, we use a Large-Eddy Simulation dataset of 28 simulations with seven different atmospheric conditions and three heterogeneous surfaces with different heterogeneity scales as well as one homogeneous surface. The newly developed model captures very well the variability in the magnitude of the energy balance gap under different conditions. The model covers a wide range of both atmospheric stabilities and landscape heterogeneity scales and is well suited for application to eddy covariance measurements since all necessary information can be modelled or obtained from a few additional measurements., Competing Interests: The authors have declared that no competing interests exist.

A recalcitrant problem in the physics of turbulence is the representation of the tendency of large-scale anisotropic eddies to redistribute their energy content with decreasing scales, a phenomenon referred to as return to isotropy. An unprecedented dataset of atmospheric turbulence measurements covering flat to mountainous terrain, stratification spanning convective to very stable conditions, surface roughness ranging over several orders of magnitude, and Reynolds numbers that far exceed the limits of direct numerical simulations and laboratory experiments was assembled for the first time and used to explore the scalewise return to isotropy. The multiple routes to energy equipartitioning among velocity components are shown to be universal once the initial anisotropy at large scales, linked to turbulence generation, is accounted for.

Classic Monin-Obukov similarity scaling states that in a stationary, horizontally homogeneous flow, in the absence of subsidence, turbulence is dictated by the balance between shear production and buoyancy production/destruction, whose ratio is characterized by a single universal scaling parameter. An evident breakdown in scaling is observed though, through large scatter in traditional scaling relations for the horizontal velocity variances under unstable stratification, or more generally in complex flow conditions. This breakdown suggests the existence of processes other than local shear and buoyancy that modulate near-surface turbulence. Recent studies on the role of anisotropy in similarity scaling have shown that anisotropy, even if calculated locally, may encode the information about these missing processes. We therefore examine the possible processes that govern the degree of anisotropy in convective conditions. We first use the reduced turbulence-kinetic-energy budget to show that anisotropy in convective conditions cannot be uniquely described by a balance of buoyancy and shear production and dissipation, but that other terms in the budget play an important role. Subsequently, we identify a ratio of local time scales that acts as a proxy for the anisotropic state of convective turbulence. This ratio can be used to formulate a new non-dimensional group. Results show that building on this approach the role of anisotropy in scaling relations over complex terrain can be placed into a more generalized framework., (© The Author(s) 2021.)

Performance of solar PV diminishes with the increase in temperature of the solar modules. Therefore, to further facilitate the reduction in cost of photovoltaic energy, new approaches to limit module temperature increase in natural ambient conditions should be explored. Thus far only approaches based at the individual panel level have been investigated, while the more complex, systems approach remains unexplored. Here, we perform the first wind tunnel scaled solar farm experiments to investigate the potential for temperature reduction through system-level flow enhancement. The percentage of solar irradiance converted into electric power depends upon module efficiency, typically less than 20%. The remaining 80% of solar irradiance is converted into heat, and thus improved heat removal becomes an important factor in increasing performance. Here, We investigate the impact of module inclination on system-level flow and the convective heat transfer coefficient. Results indicate that significant changes in the convective heat transfer coefficient are possible, based on wind direction, wind speed, and module inclination. We show that 30-45% increases in convection are possible through an array-flow informed approach to layout design, leading to a potential overall power increase of ~5% and decrease of solar panel degradation by +0.3%/year. The proposed method promises to augment performance without abandoning current PV panel designs, allowing for practical adoption into the existing industry. Previous models demonstrating the sensitivity to convection are validated through the wind tunnel results, and a new conceptual framework is provided that can lead to new means of solar PV array optimization.

The development of a unified similarity scaling has so far failed over complex surfaces, as scaling studies show large deviations from the empirical formulations developed over flat and horizontally homogeneous terrain as well as large deviations between the different complex terrain data sets. However, a recent study of turbulence anisotropy for flat and horizontally homogeneous terrain has shown that separating the data according to the limiting states of anisotropy (isotropic, two-component axisymmetric and one-component turbulence) improves near-surface scaling. In this paper we explore whether this finding can be extended to turbulence over inclined and horizontally heterogeneous surfaces by examining near-surface scaling for 12 different data sets obtained over terrain ranging from flat to mountainous. Although these data sets show large deviations in scaling when all anisotropy types are examined together, the separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relations, indicating the possibility of a unified framework for turbulence scaling. A measure of turbulence complexity is developed, and the causes for the breakdown of scaling and the physical mechanisms behind the turbulence complexity encountered over complex terrain are identified and shown to be related to the distance to the isotropic state, prevalence of directional shear with height in mountainous terrain, and the deviations from isotropy in the inertial subrange.

To improve the performance of solar photovoltaic devices one should mitigate three types of losses: optical, electrical and thermal. However, further reducing the optical and electrical losses in modern photovoltaic devices is becoming increasingly costly. Therefore, there is a rising interest in minimizing the thermal losses. These correspond to the reduction in electrical power output resultant of working at temperatures above 25 °C and the associated accelerated aging. Here, we quantify the impact of all possible strategies to mitigate thermal losses in the case of the mainstream crystalline silicon technology. Results indicate that ensuring a minimum level of conductive/convective cooling capabilities is essential. We show that sub-bandgap reflection and radiative cooling are strategies worth pursuing and recommend further field testing in real-time operating conditions. The general method we propose is suitable for every photovoltaic technology to guide the research focused on reducing thermal losses.

Turbulence data from the CASES-99 field experiment, over comparatively horizontally homogeneous and flat terrain, are separated based on the anisotropy of the Reynolds stress tensor (into isotropic, two-component axisymmetric and one-component turbulence) and flux-variance similarity scaling relations are tested. Results illustrate that different states of anisotropy correspond to different similarity relations, especially under unstable stratification. Experimental data with close to isotropic turbulence match similarity relationships well. On the other hand, very anisotropic turbulence deviates significantly from the traditional scaling relations. We examine in detail the characteristics of these states of anisotropy, identify conditions in which they occur and connect them with different governing parameters. The governing parameters of turbulence anisotropy are shown to be different for stable and unstable stratification, but are able to delineate clearly the conditions in which each of the anisotropy states occurs.