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The atmospheric boundary layer is particularly challenging to model in conditions of stable stratification, which can be associated with intermittent or unsteady turbulence. We develop a modelling approach to represent unsteady mixing possibly associated with turbulence intermittency and with unresolved fluid motions, called sub‐mesoscale motions. This approach introduces a stochastic parametrisation by randomising the stability correction used in the classical Monin–Obhukov similarity theory. This randomisation alters the turbulent momentum diffusion and accounts for sporadic events that cause unsteady mixing. A data‐driven stability correction equation is developed, parametrised, and validated with the goal to be modular and easily combined with existing Reynolds‐averaged Navier–Stokes models. Field measurements are processed using a statistical model‐based clustering technique, which simultaneously models and classifies the non‐stationary stable boundary layer. The stochastic stability correction obtained includes the effect of the static stability of the flow on the resolved flow variables, and additionally includes random perturbations that account for localised intermittent bursts of turbulence. The approach is general and effectively accounts for the stochastic mixing effects of unresolved processes of possibly unknown origin. [ABSTRACT FROM AUTHOR]

Time integration of the unsteady Reynolds-averaged Navier–Stokes (URANS) equations is the principal approach used in numerical weather prediction. This approach represents a balanced compromise between accuracy and computational cost. The URANS equations require the flow to be decomposed into an ensemble mean and excursions that are presumed to be entirely related to turbulence, thereby enabling conventional closure schemes to be used to describe their statistics. Implicit in such a decomposition is the assumption of a spectral gap between the unsteadiness in the mean flow and the scales of turbulence. Modelling challenges arise when some of the unresolved fluctuations are related to non-turbulent, structured motions that can also blur the spectral gap and render conventional closure schemes ineffective. This work seeks to clarify modelling issues that occur when unresolved fluctuations include submesoscale motions and persistent secondary circulations related to surface heterogeneities. Because submeso motions and persistent secondary circulations are not random, new theoretical tactics are discussed to represent their effects on URANS transport. By reviewing the interpretation of fluctuating terms in the URANS equations, we suggest the use of large-eddy simulations, direct numerical simulations and field measurements to guide the development of closure schemes that explicitly include fluxes due to submeso motions and persistent secondary circulations. [ABSTRACT FROM AUTHOR]

This study investigates the use of covariant Lyapunov vectors and their respective angles for detecting transitions between metastable states in dynamical systems, as recently discussed in several atmospheric sciences applications. In a first step, the needed underlying dynamical models are derived from data using a non-parametric model-based clustering framework. The covariant Lyapunov vectors are then approximated based on these data-driven models. The data-based numerical approach is tested using three well-understood example systems with increasing dynamical complexity, identifying properties that allow for a successful application of the method: in particular, the method is identified to require a clear multiple time scale structure with fast transitions between slow subsystems. The latter slow dynamics should be dynamically characterized by invariant neutral directions of the linear approximation model. [ABSTRACT FROM AUTHOR]

The lower nocturnal boundary layer is governed by intermittent turbulence which is thought to be triggered by sporadic activity of so-called sub-mesoscale motions in a complex way. We analyze intermittent turbulence based on an assumed relation between the vertical gradients of the sub-mean scales and turbulence kinetic energy. We analyze high-resolution nocturnal eddy-correlation data from 30-m tower collected during the Fluxes over Snow Surfaces II field program. The non-turbulent velocity signal is decomposed using a discrete wavelet transform into three ranges of scales interpreted as the mean, jet and sub-mesoscales. The vertical gradients of the sub-mean scales are estimated using finite differences. The turbulence kinetic energy is modelled as a discrete-time autoregressive process with exogenous variables, where the latter ones are the vertical gradients of the sub-mean scales. The parameters of the discrete model evolve in time depending on the locally-dominant turbulence-production scales. The three regimes with averaged model parameters are estimated using a subspace-clustering algorithm which illustrates a weak bimodal distribution in the energy phase space of turbulence and sub-mesoscale motions for the very stable boundary layer. One mode indicates turbulence modulated by sub-mesoscale motions. Furthermore, intermittent turbulence appears if the sub-mesoscale intensity exceeds 10 % of the mean kinetic energy in strong stratification. [ABSTRACT FROM AUTHOR]

A combination of methods originating from non-stationary time-series analysis is applied to two datasets of near-surface turbulence in order to gain insights on the non-stationary enhancement mechanism of intermittent turbulence in the stable atmospheric boundary layer (SBL). We identify regimes of SBL turbulence for which the range of time scales of turbulence and submeso motions, and hence their scale separation (or lack of separation), differs. Ubiquitous flow structures, or events, are extracted from the turbulence data in each flow regime. We relate flow regimes characterized by very stable stratification, but differing in the dynamical interactions and in the transport properties of different scales of motion, to a signature of flow structures thought to be submeso motions. [ABSTRACT FROM AUTHOR]

Regimes of interactions between motions on different time‐scales are investigated in the FLOSSII dataset for nocturnal near‐surface stable boundary layer turbulence. The non‐stationary response of turbulent vertical velocity variance to non‐turbulent, submeso‐scale wind velocity variability is analysed using the bounded variation, finite element, vector autoregressive factor models (FEM‐BV‐VARX) clustering method. Several locally stationary flow regimes are identified with different influences of submeso wind velocity on the turbulent vertical velocity variance. In each flow regime, we analyse multiple scale interactions and quantify the amount of turbulent variability which can be statistically explained by external forcing by the submeso wind velocity. The state of anisotropy of the Reynolds stress tensor in the different flow regimes is shown to relate to these different signatures of scale interactions. In flow regimes under considerable influence of the submeso‐scale wind variability, the Reynolds stresses show a clear preference for strongly anisotropic, one‐component states. These periods additionally show stronger persistence in their dynamics, compared to periods of more isotropic stresses. The analyses give insights on how the different topologies relate to non‐stationary turbulence triggering by submeso‐scale motions. [ABSTRACT FROM AUTHOR]

Flume experiments with particle imaging velocimetry (PIV) were conducted recently to study a complex flow problem where wind shear acts on the surface of a static water body in presence of flexible emergent vegetation and induces a rich dynamics of wave–turbulence–vegetation interaction inside the water body without any gravitational gradient. The experiments were aimed at mimicking realistic vegetated wetlands and the present work is targeted to improve the understanding of the coherent structures associated with this interaction by employing a combination of techniques such as quadrant analysis, proper orthogonal decomposition (POD), Shannon entropy and mutual information content (MIC). The turbulent transfer of momentum is found to be dominated by organized motions such as sweeps and ejections, while the wave component of vertical momentum transport does not show any such preference. Reducing the data using POD shows that wave energy for large flow depths and turbulent energy for all water depths is concentrated among the top few modes, which can allow development of simple reduced order models. Vegetation flexibility is found to induce several roll type structures, however if the vegetation density is increased, drag effects dominate over flexibility and organize the flow. The interaction between waves and turbulence is also found to be highest among flexible sparse vegetation. However, rapidly evolving parts of the flow such as the air–water interface reduces wave–turbulence interaction. [ABSTRACT FROM AUTHOR]

Interactions between motions on different time-scales are investigated in the SnoHATS dataset of near-surface stable boundary layer (SBL) turbulence. In an earlier study, the authors applied a data-clustering methodology based on a bounded variation, finite element, vector autoregressive factor method ( FEM-BV-VARX) to characterize the influence of non-turbulent, submesoscale motions on the turbulence in the SnoHATS dataset. Regimes were identified, two of them weakly stable and two very stable turbulence states. In each identified regime, the variability of turbulent momentum fluxes is characterized here using an extended multiresolution flux decomposition methodology. The transport properties in each regime of near-surface SBL turbulence are thereby assessed. The same methodology is used to investigate the scales of motion responsible for shear generation of turbulence. [ABSTRACT FROM AUTHOR]

Atmospheric boundary layers with stable stratification include a variety of small-scale nonturbulent motions such as waves, microfronts, and other complex structures. When the thermal stratification becomes strong, the presence of such motions could affect the turbulent mixing to a large extent, and common similarity theory that is used to describe weakly stable conditions may become unreliable. The authors apply a statistical clustering methodology based on a bounded variation, finite-element method (FEM-BV) to characterize the interaction between small-scale nonturbulent motions and turbulence. The clustering methodology achieves a multiscale representation of nonstationary turbulence data by approximating them through an optimal sequence of locally stationary multivariate autoregressive factor model (VARX) processes and some slow hidden process switching between them. The clustering method is used to separate periods with different influence of the nonturbulent motions on the vertical velocity fluctuations. The methodology can be used in a later stage to derive a stochastic parameterization for the interactions between nonturbulent and turbulent motions. [ABSTRACT FROM AUTHOR]

This study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of year-round monthly average temperature within a field study area located on the eastern coast of Sweden. A network of temperature sensors measures surface and near-surface air temperatures during a year from June 2011 to June 2012. Strong relationships between solar radiation and temperature exhibited during the growing season (supporting previous work) break down in snow cover and snowmelt periods. Surface temperature measurements are here used to estimate snow cover duration, relating the timing of snowmelt to low performance of an existing linear model developed for the investigated site. This study demonstrates that linearity between insolation and temperature 1) may only be valid for solar radiation levels above a certain threshold and 2) is affected by the consumption of incoming radiation during snowmelt. [ABSTRACT FROM AUTHOR]

This study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of monthly average temperature, and diurnal temperature variation, at different times and locations within a field study area located on the eastern coast of Sweden. Near-surface temperatures are measured by a network of temperature sensors during the spring and summer of 2011 and then used as the basis for model development and testing. The modeling of finescale spatiotemporal variation considers topography, distance from the sea, and observed variations in atmospheric conditions, accounting for site latitude, elevation, surface orientation, daily and seasonal shifts in sun angle, and effects of shadows from surrounding topography. The authors find a lag time between insolation and subsequent temperature response that follows an exponential decay from coastal to inland locations. They further develop a linear regression model that accounts for this lag time in quantifying fine-resolved spatiotemporal temperature evolution. This model applies in the considered growing season for spatial distribution across the studied near-coastal landscape. [ABSTRACT FROM AUTHOR]

Dynamic subgrid-scale models require an a priori assumption about the variation in the model coefficients with filter scale. The standard dynamic model assumes independence of scale while the scale dependent model assumes power-law dependence. In this paper, we use field experimental data to investigate the dependence of model coefficients on filter scale for the Smagorinsky and the nonlinear models. The results indicate that the assumption of a power-law dependence, which is often used in scale dependent dynamic models, holds very well for the Smagorinsky model. For the nonlinear model, the power-law assumption seems less robust but still adequate. [ABSTRACT FROM AUTHOR]

We examine the dynamics of turbulence subgrid (or sub-filter) scales over a lake surface and the implications for large-eddy simulations (LES) of the atmospheric boundary layer. The analysis is based on measurements obtained during the Lake-Atmosphere Turbulent EXchange (LATEX) field campaign (August–October, 2006) over Lake Geneva, Switzerland. Wind velocity, temperature and humidity profiles were measured at 20 Hz using a vertical array of four sonic anemometers and open-path gas analyzers. The results indicate that the observed subgrid-scale statistics are very similar to those observed over land surfaces, suggesting that the effect of the lake waves on surface-layer turbulence during LATEX is small. The measurements allowed, for the first time, the study of subgrid-scale turbulent transport of water vapour, which is found to be well correlated with the transport of heat, suggesting that the subgrid-scale modelling of the two scalars may be coupled to save computational resources during LES. [ABSTRACT FROM AUTHOR]

Data-driven stochastic parameterization methods use observational data to support and improve existing prediction systems. Specifically in atmospheric sciences, uncertainty in observations is a challenge for modeling because several aspects of the physical processes are yet to be understood in some contexts. A key goal in constructing atmospheric models is to express unresolved processes using the resolved variables of a prediction system. Estimating parameterisations for highly nonlinear systems with non stationary data requires application-specific tools. Previous work on methods for analyzing nonstationary data presents a exible model-based nonparametric clustering methodology that can be exploited for parameterization development. This paper adapts the existing framework for stochastic parameterization by combining the continuous-time formulation of stochastic differential equations (SDE) with the clustering method. We use a closed-form likelihood function approach based on a suitable Hermite expansion to approximate the parameter values of the SDE with arbitrary nonlinear drift and nonlinear diffusion. The novel parameterization framework provides a smooth classification function that allows us to recover the underlying temporal parameter modulation of a nonstationary one-dimensional SDE. The numerical examples show that the clustering approach recovers a hidden functional relationship between the parameters of the SDE model and an additional auxiliary process. The study builds upon this functional relationship to develop closed-form, nonstationary, data-driven stochastic models for multiscale dynamical systems in real-world applications. [ABSTRACT FROM AUTHOR]

The lower atmospheric boundary layer in thermally stably stratified conditions ischaracterised by intermittent turbulence. This intermittency is partially triggered by theactivity of so-called submeso motions in a complex way. This study analyses fluctuations ofwind velocity profiles relative to a logarithmically shaped background mean scale velocityprofile. The purpose is to disentangle submeso and turbulent scales in the data so as toinvestigate the relationship between the intensity of submeso motions and the patchiness ofthe turbulence. The data analysis is performed on high-resolution nocturnal turbulence measurementsthat were collected during the FLOSSII field program with a 30-meter high tower. Toinvestigate scale interactions, we use discrete wavelet transform on the wind data toseparate the scales of fluctuations. The idea is to find a scale of the mean windat which the logarithmic wind profile is clearly defined. At that averaging scale,the largest velocity is at the top measurement point. This velocity is used to scalethe turbulent intensity and the sub-mesoscale wind velocity and thereby definenon-dimensional ratios to compare the relative activity of submeso and turbulentscales. We find that for this field program, the scale of the mean wind for which a logarithmicprofile is clearly satisfied corresponds to averaging over 2 to 3 hours. Faster time scalescorrespond to localised accelerations creating inflexions in the logarithmic profile. Thevariety of scales between the mean wind scale and the turbulent scale are separated in twogroups. The larger ones having periods from 3 to 1 hour and the submeso motions havingperiods from 1 hour to 5 min. The results demonstrate how the activity of these two groups ofscales is becoming closely related to the intermittent behaviour of the turbulentintensity in the limit of low mean wind condition. Intermittent turbulence occurswhen the variability of submeso and jet scales are of the same order as the meanscale. This study brings new insights into the identification of scales originating in the spectralgrey zone of surface-layer atmospheric turbulence. [ABSTRACT FROM AUTHOR]

For near-calm atmospheric boundary-layer flows in thermally stably stratified conditions, turbulence is observed to cease partially and the turbulent flux is dominated by infrequent localized mixing events. This situation represents a highly non-stationary state of the dynamics of turbulence where relationships between fluxes and the weak mean flow break down, resulting in failure of turbulence parameterisation schemes in weather and climate models. The infrequent mixing events are related to increased shear due to localised wind accelerations on the so-called submeso-scales (scales between the largest turbulent eddies and the smallest mesoscale motions traditionally specified at 2 km). The submeso motions can exhibit structures such as ramp-cliff convective patterns, waves or microfronts but are generally unknown. Here we propose a data-driven approach to study the statistical properties of flow structures occurring in classified dynamical regimes of the stably stratified atmospheric boundary layer.A combination of methods originating from non-stationary timeseries analysis is applied to two datasets of near surface turbulence in order to gain insights on the non-stationary enhancement mechanism of intermittent turbulence in the stable atmospheric boundary layer. We identify regimes of SBL turbulence for which the range of timescales of turbulence and submeso motions, and hence their scale separation (or lack of separation) differs. Ubiquitous flow structures, or events, are extracted from the turbulence data in each flow regime. We relate flow regimes characterised by very stable stratification but different scale interaction properties to a signature of flow structures thought to be submeso motions. [ABSTRACT FROM AUTHOR]

Stable boundary layers are strongly controlled by multi-scale interactions. Here we investigate the regimes of interaction using the FLOSSII and SNOHATS datasets for nocturnal near-surface stable boundary layer turbulence. The non-stationary response of turbulent vertical velocity variance to non-turbulent, sub-mesoscale wind velocity variability is analyzed using the bounded variation, finite element, vector autoregressive factor models clustering method. Several locally stationary flow regimes are identified with different influences of sub-mesoscale wind velocity on the turbulent vertical velocity variance. In each flow regime, we analyze multi-scale interactions and quantify the amount of turbulent variability which can be statistically explained by the individual forcing variables. The state of anisotropy of the Reynolds stress tensor in the different regimes is shown to relate to these different signatures of scale interactions. Finally, we relate the different regimes and associated anisotropy to the vorticity fields and turbulence budgets. In flow regimes dominated by sub-mesoscale wind variability, the Reynolds stresses show a clear preference for strongly anisotropic, one-component stresses. These periods additionally show stronger persistence in their dynamics, compared to periods of more isotropic stresses. The analyses give insights into how the different topologies relate to non-stationary turbulence triggering by sub-mesoscale motions. [ABSTRACT FROM AUTHOR]

A recent work of Harikrishnan et al. ["Geometry and organization of coherent structures in stably stratified atmospheric boundary layers," arXiv:2110.02253 (2021)] has revealed an abundance of hairpin-like vortex structures, oriented in a similar direction, in the turbulent patches of a stably stratified Ekman flow. In this study, hairpin-like structures are investigated by treating them as slender vortex filaments, i.e., a vortex filament whose diameter d is small when compared to its radius of curvature R. The corrected thin-tube model of Klein and Knio ["Asymptotic vorticity structure and numerical simulation of slender vortex filaments," J. Fluid Mech. 284, 275 (1995)] is used to compute the motion of these filaments with the atmospheric boundary layer as a background flow. Our results suggest that the orientation of the hairpin filament in the spanwise direction is linked to its initial starting height under stable stratification, whereas no such dependency can be observed with the neutrally stratified background flow. An improved feature tracking scheme based on spatial overlap for tracking Q-criterion vortex structures on the direct numerical simulation data is also developed. It overcomes the limitation of using a constant threshold in time by dynamically adjusting the thresholds to accommodate the growth or deterioration of a feature. A comparison between the feature tracking and the filament simulation reveals qualitatively similar temporal developments. Finally, an extension of the asymptotic analysis of Callegari and Ting ["Motion of a curved vortex filament with decaying vortical core and axial velocity," J. Appl. Math. 35, 148–175 (1978)] is carried out to include the effect of gravity. The results show that, in the regime considered here, a contribution from the gravity term occurs only when the tail of an infinitely long filament is tilted at an angle relative to the wall. [ABSTRACT FROM AUTHOR]

The air-water exchange of momentum and scalars (temperature and water vapour) is investigated using the Lake-Atmosphere Turbulent EXchange (LATEX) dataset. The wind waves and swell are found to affect the coupling between the water surface and the air differently. The surface-stress vector aligns with the wind velocity in the presence of wind waves, but a wide range of stress-wind misalignment angles is observed during swell. The momentum transport efficiency decreases when significant stress-wind misalignment is present, suggesting a strong influence of surface wave properties on surface drag. Based on this improved understanding of the role of wave-wind misalignment, a new relative wind speed for surface-layer similarity formulations is proposed and tested using the data. The new expression yields a value of the von Kármán constant (κ) of 0.38, compared to 0.36 when using the absolute wind speed, as well as reduced data fitting errors. Finally, the ratios of aerodynamic to scalar roughness lengths are computed and various existing models in the literature are tested using least-square fitting to the observed ratios. The tests are able to discriminate between the performance of various models; however, they also indicate that more investigations are required to understand the physics of scalar exchanges over waves. [ABSTRACT FROM AUTHOR]

Data collected by 12 sonic anemometers over the Plaine Morte Glacier (Swiss Alps) during the Snow Horizontal Arrays Turbulence Study are used to investigate sources of anisotropy in the Reynolds stress tensor for stable boundary layers. A coarse‐graining approach is applied to evaluate transfers of kinetic energy across scales, and internal gravity waves (IGWs) are detected with a suitable criterion. Both approaches are combined with a classification of anisotropy based on the barycentric map of 1 min Reynolds stress tensors. A wind‐speed threshold is found that discriminates between regimes with a different dominant topology of the Reynolds stress tensor. One‐component and isotropic states are frequent for low wind speed and strong stratification, whereas two‐component axisymmetric states dominate the high wind speed regime with strong vertical shear. Results suggest that the presence of IGWs is mostly responsible for one‐component states, and additionally influences the relative amount of kinetic and potential energy in the perturbation field. To provide supporting evidence, a complementary analysis of a clean IGW detected during a field campaign in Dumosa (Australia) is presented. This case study highlights how waves contribute to drive the Reynolds stress tensor towards the one‐component limit. Cases are shown where a linear detrending procedure may be effective in filtering out waves and avoid their spurious contributions in turbulence statistics. [ABSTRACT FROM AUTHOR]

A statistical indicator for dynamic stability, known as the Υ indicator, is used to gauge the stability and, hence, detect approaching tipping points of simulation data from a reduced five-box model of the North Atlantic Meridional Overturning Circulation (AMOC) exposed to a time-dependent hosing function. The hosing function simulates the influx of fresh water due to the melting of the Greenland ice sheet and increased precipitation in the North Atlantic. The Υ indicator is designed to detect changes in the memory properties of the dynamics and is based on fitting auto-regressive moving-average models in a sliding window approach to time series data. An increase in memory properties is interpreted as a sign of dynamical instability. The performance of the indicator is tested on time series subject to different types of tipping, namely, bifurcation-induced, noise-induced, and rate-induced tipping. The numerical analysis shows that the indicator indeed responds to the different types of induced instabilities. Finally, the indicator is applied to two AMOC time series from a full complexity Earth systems model (CESM2). Compared with the doubling CO 2 scenario, the quadrupling CO 2 scenario results in stronger dynamical instability of the AMOC during its weakening phase. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting refers to the prediction of precipitation intensity in a local region and in a short timeframe up to 6 hours. The evaluation of spatial and temporal information still challenges todays numerical weather prediction models. The increasing possibilities to store and evaluate data combined with the advancements in the developments of artificial intelligence algorithms make it natural to use these methods to improve precipitation nowcasting. In this work a Convolutional Long Short-TermMemory network (ConvLSTM) is applied to Radar data of the GermanWeather Service. The positive effectiveness of finetuning a network pretrained at a different location and for different precipitation intensity thresholds is demonstrated. Furthermore, in the framework of two case studies the skill scores for the different thresholds are shown for a prediction time up to 100 minutes. The results highlight promising regional extrapolation capabilities for such neural networks for precipitation nowcasting. [ABSTRACT FROM AUTHOR]

The Institute of Radiation Protection and Nuclear Safety (France) is planning the set-up of an automatic nuclear aerosol monitoring network over the French territory. Each of the stations will be able to automatically sample the air aerosol content and provide activity concentration measurements on several radionuclides. This should help monitor the French and neighbouring countries nuclear power plants set. It would help evaluate the impact of a radiological incident occurring at one of these nuclear facilities. This paper is devoted to the spatial design of such a network. Here, any potential network is judged on its ability to extrapolate activity concentrations measured on the network stations over the whole domain. The performance of a network is quantitatively assessed through a cost function that measures the discrepancy between the extrapolation and the true concentration fields. These true fields are obtained through the computation of a database of dispersion accidents over one year of meteorology and originating from 20 French nuclear sites. A close to optimal network is then looked for using a simulated annealing optimisation. The results emphasise the importance of the cost function in the design of a network aimed at monitoring an accidental dispersion. Several choices of norm used in the cost function are studied and give way to different designs. The influence of the number of stations is discussed. A comparison with a purely geometric approach which does not involve simulations with a chemistry-transport model is performed. [Copyright &y& Elsevier]

Many natural systems undergo critical transitions, i.e., sudden shifts from one dynamical regime to another. In the climate system, the atmospheric boundary layer can experience sudden transitions between fully turbulent states and quiescent, quasi-laminar states. Such rapid transitions are observed in polar regions or at night when the atmospheric boundary layer is stably stratified, and they have important consequences in the strength of mixing with the higher levels of the atmosphere. To analyze the stable boundary layer, many approaches rely on the identification of regimes that are commonly denoted as weakly and very stable regimes. Detecting transitions between the regimes is crucial for modeling purposes. In this work a combination of methods from dynamical systems and statistical modeling is applied to study these regime transitions and to develop an early warning signal that can be applied to nonstationary field data. The presented metric aims to detect nearing transitions by statistically quantifying the deviation from the dynamics expected when the system is close to a stable equilibrium. An idealized stochastic model of near-surface inversions is used to evaluate the potential of the metric as an indicator of regime transitions. In this stochastic system, small-scale perturbations can be amplified due to the nonlinearity, resulting in transitions between two possible equilibria of the temperature inversion. The simulations show such noise-induced regime transitions, successfully identified by the indicator. The indicator is further applied to time series data from nocturnal and polar meteorological measurements. [ABSTRACT FROM AUTHOR]

The partitioning of solar energy over the Earth's surface drives weather and climate of the coupled land-ocean-atmosphere system. Over water surfaces, the evolution of water temperatures at a given depth in the mixed layer implicitly contains the signature of surface energy partitioning, and as such it can be used to diagnose the surface energy balance. In this study, we develop a novel numerical scheme by combining the Green's function approach and linear stability analysis to estimate the water surface energy balance using water temperature measurement at a single depth. The proposed method is capable of predicting water temperature in the mixed layer, and solving for the components of the surface energy budgets with physically based schemes. Evaluation against in situ measurement and the maximum entropy production method demonstrates that this approach is robust and of good accuracy. It is found that performance of the proposed method depends strongly on the accurate estimation of turbulent thermal diffusivity from in situ measurements, which carries information of meteorological and limnological conditions. Without explicitly using wind speed or temperature/moisture gradient, the proposed approach reduces uncertainty and potential error associated with meteorological measurements in estimation of water surface energy balance. [ABSTRACT FROM AUTHOR]

Taylor's frozen turbulence hypothesis is the central assumption invoked in most experiments designed to investigate turbulence physics with time resolving sensors. It is also frequently used in theoretical discussions when linking Lagrangian to Eulerian flow formalisms. In this work we seek to quantify the effectiveness of Taylor's hypothesis on the field scale using water vapour as a passive tracer. A horizontally orientated Raman lidar is used to capture the humidity field in space and time above an agricultural region in Switzerland. High resolution wind speed and direction measurements are conducted simultaneously allowing for a direct test of Taylor's hypothesis at the field scale. Through a wavelet decomposition of the lidar humidity measurements we show that the scale of turbulent motions has a strong influence on the applicability of Taylor's hypothesis. This dependency on scale is explained through the use of dimensional analysis. We identify a 'persistency scale' that can be used to quantify the effectiveness of Taylor's hypothesis, and present the accuracy of the hypothesis as a function of this non-dimensional length scale. These results are further investigated and verified through the use of large-eddy simulations. [ABSTRACT FROM AUTHOR]