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We investigate the sedimentation dynamics of a binary mixture, the species of which differ by their Stokes coefficients but are identical otherwise. We analyze the sedimentation dynamics and the morphology of the final deposits using Brownian dynamics simulations for mixtures with a range of sedimentation velocities of both species. In addition, we use the lattice Boltzmann method to study hydrodynamic effects. We found a threshold in the difference of the sedimentation velocities above which the species in the final deposit are segregated. The degree of segregation increases with the difference in the Stokes coefficients or the sedimentation velocities above the threshold. We propose a simple analytical model that captures the main features of the simulated deposits.

We study a tropical linear regression problem consisting in finding the best approximation of a set of points by a tropical hyperplane. We establish a strong duality theorem, showing that the value of this problem coincides with the maximal radius of a Hilbert's ball included in a tropical polyhedron. We also show that this regression problem is polynomial-time equivalent to mean payoff games. We illustrate our results by solving an inverse problem from auction theory. In this setting, a tropical hyperplane represents the set of equilibrium prices. Tropical linear regression allows us to quantify the distance of a market to the set of equilibria, and infer secret preferences of a decision maker.

The classical Poincar\'e formula relates the rational homology classes of tautological cycles on a Jacobian to powers of the class of Riemann theta divisor. We prove a tropical analogue of this formula. Along the way, we prove several foundational results about real tori with integral structures (and, therefore, tropical abelian varieties). For example, we prove a tropical version of the Appell-Humbert theorem. We also study various notions of equivalences between tropical cycles and their relation to one another.

Stratocumulus clouds cover about a fifth of Earths surface, and due to their albedo and low-latitude location, they have a strong effect on Earths radiation budget. Previous studies using Large Eddy Simulations have shown that multiple equilibria (both cloud-covered and cloud-free states) exist as a function of fixed-SST, with relevance to equatorward advected air masses. Multiple equilibria have also been found as a function of atmospheric CO2, with a subtropical SST nearly 10 K higher in the cloud-free state, and with suggested relevance to warm-climate dynamics. In this study, we use a mixed-layer model with an added surface energy balance and the ability to simulate both the cloud-covered and cloud-free states to study both types of multiple equilibria and the corresponding hysteresis. The model's simplicity and computational efficiency allow us to explore the mechanisms critical to the stratocumulus cloud instability and hysteresis as well as isolate key processes that allow for multiple equilibria via mechanism denial experiments not possible with a full-complexity model. For the hysteresis in fixed-SST, we find that decoupling can occur due to either enhanced entrainment warming or a reduction in cloud-top longwave cooling. The critical SST at which decoupling occurs is highly sensitive to precipitation and entrainment parameterizations. In the CO2 hysteresis, decoupling occurs in the simple model used even without the inclusion of an active SST and SST-cloud cover feedbacks, and the width of the hysteresis displays the same sensitivities as the fixed-SST case. Overall, the simple model analysis and results motivate further studies using higher complexity models., Submitted to Journal of Climate 06-12-2022

In climate model configurations, standard approaches to the representation of unresolved, or subgrid scales, via deterministic closure schemes are being challenged by stochastic approaches inspired by statistical dynamical theory. Despite gaining popularity, studies of various stochastic subgrid scale parameterizations applied to atmospheric climate and weather prediction systems have revealed a diversity of model responses, including degeneracy in the response to different forcings and compensating model errors, with little reduction in artificial damping of the small scales required for numerical stability. Due to the greater range of spatio-temporal scales involved, how to best sample subgrid fluctuations in a computationally inexpensive manner, with the aim of reduced model error and improvements to the simulated climatological state of the ocean, remains an open question. While previous studies have considered perturbations to the surface forcing or subsurface temperature tendencies, we implement an energetically consistent, simple, stochastic subgrid eddy parameterization of the momentum fluxes in regions of the three-dimensional ocean typically associated with high eddy variability. We consider the changes in the modelled energetics of low-resolution simulations in response to stochastically forced velocity tendencies whose perturbation statistics and amplitudes are calculated from an eddy resolving ocean configuration. Kinetic energy spectra from a triple-decomposition reveal a systematic redistribution from the seasonal (climatological minus mean) potential energy to preferentially generate small scale transient kinetic energy while the total energy spectra remains largely unchanged. We show that stochastic parameterization generally improves model biases, noticeably so for the simulated energetics of the Southern Oceans.

In this paper, we study the influence of seasonal demands and forecasts on the performance of an Automatic Pipeline, Variable Inventory, Order-Based, Production Control System (APVIOBPCS) using linear control theory. In particular, we consider a system that uses a seasonal forecast based on a no-trend, additive-seasonality exponential-smoothing model, and compare its performance to an equivalent system using simple exponential smoothing. We find that the system with seasonal forecasting significantly outperforms the system with simple exponential smoothing under certain demand assumptions. With optimal parameter settings, the forecast error of the seasonal model can be up to 40% lower. However, we also find that the forecast superiority does not necessarily translate to the performance of the system measured through the bullwhip metrics. In addition, the seasonal forecasting model is very sensitive to the demand frequency and smoothing parameters, while the simple exponential smoothing model is very robust. This implies that the real life benefits of implementing a seasonal forecasting model are not obvious and depend on the particular situation; under a large number of settings (e.g. low seasonality, high-smoothing), the good performance of simple exponential smoothing certainly justifies its popularity in the industry and research worlds alike.

Cooling enhances the energy conversion efficiency and output of photovoltaic (PV) panels. In this work, the effects of natural convection, forced convection, and evaporative cooling on the performance of polycrystalline PV panels were investigated. The output and efficiency of a cooled PV panel were monitored and compared to those of an uncooled PV panel under the same conditions. The cooling was conducted using an insulated channel installed below a PV panel. Natural convection cooling was investigated for various channel air gaps (H = 30, 60, 90, and 120 mm). Natural convection currents in the cooling channels were capable of cooling the panel with wide air gaps. In forced convection cooling, the air was introduced by fans installed at the bottom opening of the cooling channel with various air velocities (ua = 1, 2, and 3 m/s). Evaporative natural convection cooling was performed by a wetted fabric along the lower surface of the cooling channel, whereas evaporative forced convection cooling by pushing air along the wetted lower surface of the channel. The experimental data showed that the panel efficiency and output increased due to cooling. The experimental results of natural convection cooling revealed that the use of an air gap of 120 mm to cool the solar panel contributed to an increase in the panel daily energy production and efficiency by 1.7% and 1.2%, respectively. For forced convection cooling, using air at a speed of 3 m/s increased the daily energy production by 4.4% and the efficiency by 4%. Natural convection evaporative cooling increased the daily energy production and the efficiency by 3.6% and 2.7%, respectively. Forced convection evaporative cooling contributed, at a speed of 2 m/s, to an increase in the daily energy production by 3.8% and an increase in efficiency of 3.8%.

Tropical counting tools are useful for many enumerative questions. We count tropical multinodal surfaces using floor plans, looking at the case when two nodes are tropically close together, i.e., unseparated. We generalize tropical floor plans to recover the count of multinodal curves. We then prove that for $\delta=2$ or $3$ nodes, tropical surfaces with unseparated nodes contribute asymptotically to the second order term of the polynomial giving the degree of the family of complex projective surfaces in $\mathbb{P}^3$ of degree $d$ with $\delta$ nodes. We classify when two nodes in a surface tropicalize to a vertex dual to a polytope with 6 lattice points, and prove that this only happens for projective degree $d$ surfaces satisfying point conditions in Mikhalkin position when $d>4$., Comment: 15 figures, 37 pages + appendix and references

Using the large-eddy simulation framework, effects of an aerosol layer on warm cumulus clouds in the Korean Peninsula when the layer is above or around the cloud tops in the upper atmosphere are examined. Also, these effects are compared to effects of an aerosol layer when it is around or below the cloud bases in the low atmosphere. Simulations show that when the aerosol layer is in the low atmosphere, aerosols absorb solar radiation and radiatively heat up air enough to induce greater instability, stronger updrafts and more cloud mass than when the layer is in the upper atmosphere. As aerosol concentrations in the layer decrease, the aerosol radiative heating gets weaker to lead to less instability, weaker updrafts and less cloud mass when the layer is in the low atmosphere. This in turn makes differences in cloud mass, which are between a situation when the layer is in the low atmosphere and that when the layer is in the upper atmosphere, smaller. It is found that the transportation of aerosols by updrafts reduces aerosol concentrations in the aerosol layer, which is in the low atmosphere, and in turn reduces the aerosol radiative heating, updraft intensity and cloud mass. It is also found that the presence of aerosol impacts on radiation suppresses updrafts and reduces clouds. Aerosols affect not only radiation but also aerosol activation. In the absence of aerosol impacts on radiation, aerosol impacts on the droplet nucleation increases cloud mass when the layer is in the low atmosphere as compared to a situation when the layer is in the upper atmosphere. As aerosol impacts on radiation team up with those on the droplet nucleation, differences in cloud mass, which are between a situation when the layer in the low atmosphere and that when the layer is in the upper atmosphere, get larger. This is as compared to a situation when there is no aerosol impacts on radiation and only aerosol impacts on the droplet nucleation.

The atmosphere transports energy polewards by circulation cells and eddies. To the present day, there has been a knowledge gap regarding the preferred spatial scales and physical mechanisms of eddy energy transport. To fill the gap, we separate the meridional atmospheric energy transport in the ERA5 reanalysis by spatial scales and into quasi-stationary and transient flow patterns and latent and dry components. Baroclinic instability is the major instability mechanism in the transient synoptic scales and is responsible for forming cyclones, anticyclones, and small-scale Rossby waves. At the planetary scales, circulation patterns are often induced by other mechanisms such as flow interaction with orography and land–sea heating contrasts. However, a separation between circulation patterns at the synoptic and planetary scales has yet to be established. We find that both baroclinically induced and transient energy transport is predominantly associated with eddies at wavelengths between 2000 and 8000 km. The maxima in both types of transport occur at wavelengths around 5000 km, in good agreement with linear baroclinic theory. Since these results are independent of latitude, we adapt the scale separation of the energy transport to be based on the wavelength instead of the previously used wavenumber. We define the synoptic transport by the wavelength band between 2000 and 8000 km. We analyse the annual and seasonal mean in the energy transport components and their inter-annual variability. The scale-separated transport components are fairly similar in both hemispheres. Transport by synoptic waves is the largest contributor to extra-tropical energy and moisture transport, mainly of a transient character, and is influenced little by seasonality. In contrast, transport by planetary waves depends highly on the season and has two distinct characteristics. (1) In the extra-tropical winter, planetary waves are important due to a large transport of dry energy. This planetary transport features the largest inter-annual variability of all components and is mainly quasi-stationary in the Northern Hemisphere but transient in its southern counterpart. (2) In the sub-tropical summer, quasi-stationary planetary waves are the most important transport component, mainly due to moisture transport, presumably associated with monsoons. In contrast to transport by planetary and synoptic waves, only a negligible amount of energy is transported by mesoscale eddies (< 2000 km).

We introduce a tropical geometric framework that allows us to define $\psi$ classes for moduli spaces of tropical curves of arbitrary genus. We prove correspondence theorems between algebraic and tropical $\psi$ classes for some one-dimensional families of genus-one tropical curves.

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

The hydrological regime is the main factor governing the functioning of floodplain rivers. A full comprehension of its dynamic leads to a better understanding of the system’s behaviour and of the proper methods that must be used. We analysed the daily water level of the Paraná River during the last century at three gauge stations using linear and non-linear tools to characterise the hydrological dynamic and to analyse to what extent chaotic behaviour prevails. The three water level time series were characterised as non-linear and non-stationary by power spectrum, autocorrelation function, and surrogate test analyses. A strange attractor was developed when the phase space was reconstructed, having a low dimensional chaos supported by correlation dimension, positive maximum Lyapunov exponents, and recurrence quantification analysis. In line with this, the system resulted unpredictable with a threshold by sample entropy, and with an intermediate hydrological complexity, while Hurst exponent characterised the system as persistent and with sensitive dependence on initial conditions. In a general overview, all the evidence obtained indicates that the Paraná River’s behaviour is at the edge of chaos. A latitudinal gradient of decreasing chaoticity was observed as the floodplain extent increased, whereas complexity was highest at the intermediate river station due to the inflow of tributaries with different hydrology. This paper attempts to offer some additional insights for understanding the hydrological behaviour of floodplain rivers and the proper methods to understand their complexity.

The aerodynamic performances of aircraft change due to the ground effect for flights near the ground. The air cushion generated near the ground will change the development of the aircraft wake vortex. Researchers have compared several different ground boundary conditions with two-dimensional airfoils, while three-dimensional cases have not been studied specifically. In this study, the effects of stationary and moving wall boundary conditions on the aircraft aerodynamic characteristics and wake development were numerically investigated in three dimensions. The results showed that the stationary wall overestimated the viscous effect of the ground, resulting in a reduction of the wingtip vortex (WTV) vorticity magnitude. The difference appeared at $ x\, = \,7b $ (wingspan of the aircraft) where the secondary vortex was induced by the wingtip and horizontal tail vortex (HTV).

This paper presents a fractional approach to study the mathematical model of tsunami wave propagation along a coastline of an ocean. Fractional Reduced Differential Transform Method (FRDTM) is used to analyze this model. The present model has been studied on the shallow-water assumption. It is represented by a time-fractional coupled system of non-linear partial differential equations. Solutions to the proposed model for different coastal slopes and ocean depths have been obtained. Effects of coast slope and sea depth variations on tsunami wave velocity and wave amplification have been demonstrated at different time levels and different orders α . The obtained results are compared with Elzaki Adomian Decomposition Method (EADM) to validate the proposed method for an order α = 1 .

As wind energy increases its share of total electricity generation and its integration into the power system becomes more challenging, accurately representing the spatio-temporal variability in wind data becomes crucial. Wind fluctuations impact power and energy systems, e.g. energy system planning, vulnerability to storm shutdowns, and available voltage stability support. To analyse such fluctuations and their spatio-temporal dependencies, time series of wind speeds at an hourly or higher frequency are needed. We provide a comprehensive evaluation of the global and mesoscale-model-derived wind time series against observations by using a set of metrics that we present as requirements for wind energy integration studies. We also perform a sensitivity analysis to find the best model setup of the Weather Research and Forecasting (WRF) model, focusing on evaluating the wind speed fluctuation metrics. The results show that using higher spatial resolution in the WRF model simulations improves the representation of temporal fluctuations; however, higher-spatial-resolution simulations often lower the correlations of wind time series with measurements. Thus, we recommend finer-spatial-resolution simulations for modelling power ramp or voltage stability studies but ERA5 rather than mesoscale simulations for studies where correlations with measurements are essential. We also show that the nesting strategy is an important consideration, and a smoother transition from the forcing data to the nested domains improves the correlations with measurements. All mesoscale model simulations overestimate the value of the spatial correlations in wind speed as estimated from observations. Still, the spatial correlations and the wind speed distributions are insensitive to the mesoscale model configuration tested in this study. Regarding these two metrics, mesoscale model simulations present more favourable results than ERA5.

We continue to study the rank functions of tropical matrices. In this paper, we explain how to reduce the computation of ranks for matrices over the `supertropical semifield' to the standard tropical case. Using a counting approach, we prove the existence of a $01$-matrix with many ones and without large all-one submatrices, and we put our results together and construct an $n\times n$ matrix with tropical rank $o(n^{0.5+\varepsilon})$ and Kapranov rank $n-o(n)$., Comment: 9 pages

Wind speed is an important meteorological factor and is a parameter used to determine the air pollution assessment and the economic feasibility of wind energy as a renewable resource. In particular, the measurement of long-term average wind speed plays a role in the estimating of chronic exposure of air pollution and the siting of wind turbines; thus this study focuses on annual and monthly wind speed. However, due to spatially sparse and irregular meteorological stations, interpolation methods are used to predict unsampled areas. Therefore, the purpose of this study is to determine the optimal interpolation method among deterministic and geostatistical interpolation techniques. While most interpolation methods rely on spatial coverage and do not give an accurate characterization of small-area variations, cokriging and regression kriging (RK) can resolve the drawback by containing regional heterogeneity as auxiliary variables. In addition, this study applied isotropic and anisotropic approaches to geostatistical interpolations. This study showed that among the sampled methods, anisotropic RK with regional heterogeneous terrains and solar insolation was most likely to produce the best estimate for the highest accuracy for annual and monthly wind speeds with relatively unbiased spatial errors. In addition, while extreme wind speed decreases the accuracy of wind speed prediction, the number, range, and variance of observed wind speed significantly influence the accuracy of wind speed prediction.

This paper presents a new multi-objective optimization dispatching method to optimize the output power of distributed generators of a micro-grid considering uncertainty in wind power forecasting with the aims of minimizing the operational cost and pollutant emission. To deal with uncertainty in wind power forecasting, an adaptive concept of confidence interval (ACCI) is proposed and probabilistic wind power interval forecasting model based on deterministic wind power forecasting and ACCI is developed to address this problem. Then, a comprehensive formulation with different operational constraints is applied to construct the multi-objective optimization dispatching model of a micro-grid considering uncertainty in wind power forecasting, and two-step solution methodology based on chaos and sinusoidal mapping multi-objective optimization bat algorithm (CSMOBA) and fuzzy theory set is employed to solve the multi-objective optimization dispatching problem. Eventually, simulation results demonstrate the proposed ACCI method can work well and obtain appropriate confidence level at each dispatching hour, and the proposed CSMOBA algorithm outperforms multi-objective optimization bat algorithm and well-known non-dominated sorting genetic algorithm II when applied in multi-objective optimization dispatching of micro-grid.

The analysis of the recent gravity measurements of Jupiter and Saturn reveal that the zonal winds observed on their surfaces reach several thousand kilometres deep into their atmospheres. However, it remains unclear which mechanism prevents them from penetrating deeper. Recent models suggest that a stably stratified region would yield the desired effect.In this study we systematically explore the dynamics in a spherical shell where the lower third is stably stratified while convection in the outer region drives multiple zonal winds, similar to those observed on Jupiter or Saturn. We perform numerical simulations with the magnetohydrodynamic code MagIC and ignore magnetic effects in order to simplify the problem. Using a rigid lower boundary condition, vigorous multiple jets begin to develop at mid to high latitudes once the stable stratification is strong enough to effectively decouple the jet dynamics from the lower boundary. We find that the jet amplitude decay at the stable layer boundary is proportional to Ω/N, where Ω is the rotation rate and N the Brunt-Väisälä frequency that quantifies the degree of stable stratification.Furthermore, the penetration depth of the jets is directly proportional to the jet width, i.e. the stable layer acts as a low-pass filter on the zonal winds. The structure of the winds also changes. In the convective region they are invariant along the axis of rotation, as expected. However, in the stable layer the location of the peaks in the zonal wind profile become more radially invariant with depth. This shift from cylindrical to a more spherical geometry inthe flow structure occurs due to meridional flows at the interface, thermal winds and the inverse buoyancy force in the stable layer.

We study the spectrum of the join of several circulant matrices. We apply our results to compute explicitly the spectrum of certain graphs obtained by joining several circulant graphs., To appear in Linear Algebra and Its Applications

Floating lidar systems (FLSs) are widely used for offshore wind site assessment, and their measurements show good agreement when compared to trusted reference sources. However, some influence of motion on mean wind speed data from FLS has to be assumed but could not have been quantified with experimental methods yet because the involved uncertainties are larger than the expected impact of motion. This study describes the motion-induced bias on horizontal mean wind speed estimates from FLS with the help of simulations of the lidar sampling pattern of a continuous-wave (CW) velocity–azimuth display (VAD) scanning wind lidar. Analytic modeling is used to validate the simulations. It is found that the mean bias depends on amplitude and frequency of motion, the angle between motion and wind direction, and wind speed and strength of wind shear. The simulations are used to quantify the measurement deviation that is caused by motion for the example of the Fugro SEAWATCH Wind LiDAR Buoy (SWLB) carrying a ZX 300M profiling wind lidar. The strongest bias of −0.67 % of the measurement values was estimated for a test case with “strong” waves aligned with the inflow wind direction. Under “normal” wave conditions the bias is smaller. The reason for these low errors lies in a fortunate combination of the frequencies of lidar prism rotation and tilt motion.

Wind stress and turbulent heat fluxes are the major driving forces that modify the ocean dynamics and thermodynamics. In the Nucleus for European Modelling of the Ocean (NEMO) ocean general circulation model, these turbulent air–sea fluxes (TASFs) can critically impact the simulated ocean characteristics. This paper investigates how the various bulk parameterizations used to calculate turbulent air-sea fluxes in NEMOv4 can lead to substantial differences in the estimation of sea surface temperatures (SSTs). Specifically, we study the contributions of different aspects and assumptions of the bulk parameterizations in driving the SST differences in the NEMO global model configuration at 1/4∘ of horizontal resolution. These aspects include the use of the skin temperature instead of the bulk SST in the computation of turbulent heat flux components and the estimation of wind stress and turbulent heat flux components, which vary in each parameterization due to different bulk transfer coefficients. The analysis of a set of short-term sensitivity experiments where the only change is related to one of the aspects of the bulk parameterizations shows that parameterization-related SST differences are primarily sensitive to wind stress differences and to the implementation of skin temperature in the computation of turbulent heat flux components. In addition, in order to highlight the role of SST–turbulent heat flux negative feedback at play in ocean simulations, we compare the TASF differences obtained using the NEMO ocean model with the estimations by Brodeau et al. (2017), who compared the different bulk parameterizations using prescribed SSTs. Our estimations of turbulent heat flux differences between bulk parameterizations are weaker than those found by Brodeau et al. (2017).

Long-span bridges in mountainous areas are greatly disturbed by wind, and the wind field in the mountain gorge bridge site is extremely complex. Therefore, it is of great engineering significance to accurately evaluate the wind field characteristics of this kind of terrain. In this paper, to enhance understanding of this kind of wind field, the wind field in a mountainous gorge is measured for a long time using wind radar, and the mean wind parameters is statistically analyzed. The results show that the mean wind parameters vary greatly under different wind directions, and the wind speed profile does not meet the power-law model. Therefore, a mixed model suitable for the wind speed profile in a mountainous gorge is proposed. Additionally, GEV distribution and Logistic distribution are found to be suitable for describing the distribution characteristics of wind speed and angle of attack, respectively. In addition, considering the correlation between wind parameters, this paper also constructs the joint probability model of wind speed and angle of attack at different heights by the Copula function. Thus, a combined wind parameters profile model is developed under different exceedance probabilities based on the inverse first-order reliability method (IFORM). This study can provide a reference for the construction of the joint probability model of wind parameters.

We describe an idealized primitive equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to make eddy-resolving calculations on the finest grids more affordable. The model has intermediate complexity, incorporating basin-scale geometry with idealized Atlantic and Southern oceans, and with non-uniform ocean depth to allow for mesoscale eddy interactions with topography. The model is perfectly adiabatic and spans the equator, and thus fills a gap between quasi-geostrophic models, which cannot span two hemispheres, and idealized general circulation models, which generally have diabatic processes and buoyancy forcing. We show that the model solution is approaching convergence in mean kinetic energy for the ocean mesoscale processes of interest, and has a rich range of dynamics with circulation features that emerge only due to resolving mesoscale turbulence.

Along-track wavenumber spectral densities of sea surface height (SSH) are estimated from Jason-2 altimetry data as a function of spatial location and calendar month, to understand the seasonality of meso- and submesoscale balanced dynamics across the global ocean. Regions with significant mode-1 and mode-2 baroclinic tides are rejected, restricting the analysis to the extratropics. Where balanced motion dominates, the SSH spectral density is averaged over all pass segments in a region for each calendar month, and is fit to a 4-parameter model consisting of a flat plateau at low wavenumbers, a transition at wavenumber $k_0$ to a red power law spectrum $k^{-s}$, and a white spectrum at high wavenumbers that models the altimeter noise. The monthly time series of the model parameters are compared to the evolution of the mixed layer. The annual mode of the spectral slope $s$ reaches a minimum after the mixed layer deepens, and the annual mode of the bandpassed kinetic energy in the ranges $[2k_0,4 k_0]$ and $[k_0,2 k_0]$ peak $\sim$2 and $\sim$4 months, respectively, after the maximum of the annual mode of the mixed layer depth. This analysis is consistent with an energization of the submesoscale by a winter mixed layer instability followed by an inverse cascade to the mesoscale, in agreement with prior modeling studies and in situ measurements. These results are compared to prior modeling, in situ, and satellite investigations of specific regions, and are broadly consistent with them within measurement uncertainties., Comment: Submitted to the Journal of Physical Oceanography. The code used for analyzing and displaying data can be found at https://github.com/albionlawrence/sm-seasonality

The ocean engine of NEMO is a primitive equation model adapted to regional and global ocean circulation problems. It is intended to be a flexible tool for studying the ocean and its interactions with the others components of the earth climate system over a wide range of space and time scales. To cite this edition: Madec, G. and the NEMO System Team, 2023.NEMO Ocean Engine Reference Manual, Zenodo, https://doi.org/10.5281/zenodo.8167700

International audience; The previsibility of Northern Africa dust events is quantified using daily numerical forecast simulations for the next three days. The dust concentrations fields, modeled by CHIMERE-DUST, are first evaluate by comparisons to AERONET surface data, OMI and MSG Seviri satellite measurements. The accuracy and spread between measurements and model are discussed focussing on the first short observation period of the AMMA experiment in western Africa, with a simulation period ranging from January to March 2006. Second, the previsibility of dust is estimated by comparing model results for different leads in a forecats mode. The model skill is evaluate in term of capability to forecast (i) the surface wind speed (the key process for dust emissions), (ii) the dust emissions (depending on the wind speed as well as numerous others uncertain parameters, including threshold values on the friction velocity) and (iii) the transport of aerosols from source to remote areas (depending of horizontal transport, convection etc.). It is shown that emissions forecast can vary up to 80% (close to the sources) and that final forecasted dust concentrations and relative optical thickness do not exceed 40% and 20% in forecast variability.

By using multiple sets of assimilation and reanalysis data, the spatial distribution and temporal evolution of the precipitation over the Tibetan Plateau (TP) in boreal summer are investigated. The results show that, the climatological precipitation tends to increase towards the southeast, while it increases in the morning, reaches a peak around 21 Beijing Time Coordinate (BTC), and tends to decay after midnight in the diurnal time scale. To understand the roles of atmospheric dynamic and thermodynamic processes in causing the diurnal variation of summer precipitation over the southeastern TP, the atmospheric precipitation and moisture budget are conducted. The budget result shows that, there is a good corresponding relationship between the precipitation and moisture tendency in the night, and the anomalous vertical motion has a key effect on the diurnal variation of the precipitation. In the developing phase of the anomalous ascending motion (17-20 BTC), the anomalous linear vorticity (temperature) advections over the southeastern TP contribute to the anomalous ascending motion, and the major contribution arises from the anomalous vorticity (temperature) field over the western TP. Due to the negative zonal vorticity (temperature) gradient, the mean westerly winds induce positive advection that support the ascending motion.

Spatiotemporal statistical learning has received increased attention in the past decade, due to spatially and temporally indexed data proliferation, especially collected from satellite remote sensing. In the mean time, observational studies of clouds are recognized as an important step to improve cloud representation in weather and climate models. Since 2006, the satellite CloudSat of NASA carries a 94 GHz cloud profiling radar and is able to retrieve, from radar reflectivity, microphysical parameter distribution such as water or ice content. The collected data is piled up with the successive satellite orbits of nearly two hours, leading to a large compressed database of 2 Tb (http://cloudsat.atmos.colostate.edu/). These observations give the opportunity to extend the cloud microphysical properties beyond the actual measurement locations using an interpolation and prediction algorithm. In order to do so, we introduce a statistical estimator based on the spatiotemporal covariance and mean of the observations known as kriging. An adequate parametric model for the covariance and the mean is chosen from an exploratory data analysis. Beforehand, it is necessary to estimate the parameters of this spatiotemporal model; This is performed in a Bayesian setting. The approach is then applied to a subset of the CloudSat dataset.

Resource shortages and environmental pollution of the world are becoming more and more serious, wind Turbine generation as a kind of clean energy power generation has attracted much attention. The difficulty of wind power generation lies in the randomness of natural wind energy, which leads to the instability of the output power of Wind Turbine Generators (WTGs). The traditional variable pitch controller cannot realize global precision control of the wind power system. To solve the problem, this paper combines Takagi-Sugeno (T-S) fuzzy model with optimal control theory to design a robust controller. The unique novelty of the controller is that when the wind speed is higher than the rated, no matter how much the wind speed is, even if it is far from the balance point of the system, the controller can make the output power at a constant value, that is, increase the global stability of the wind power system. Specifically, the wind power system was divided into several linear subsystems, and the state feedback controller of each subsystem was designed through optimal control. Then, the subsystem controllers were merged by superposing membership functions, forming a controller of the entire system. Finally, wind turbine generators (WTGs) were modeled and simulated in Matlab/Simulink. The results show that the proposed controller kept the output power of WTGs constant under the global step wind of 13–25 m/s and the stochastic wind of 14–15 m/s. Our design enables the precision control at any wind speed, and improves the stability of power output of WTGs.

Lidar-assisted windturbinecontrol has been proven to be beneficial for turbine load reduction. It relies on the preview of incoming turbulence provided by anacellelidar, which allows the turbine controller to react to the flow disturbances before their impact on the turbine. When assessing its benefits, previous studies mainly use the standard turbulence parameters suggested by the IEC 61400-1 standard and assume Taylor's frozen hypothesis. Based onatmospheric turbulenceobservations, the parameters of the Mann spectral turbulence model differ significantly from the values recommended in the standard, and they vary, e.g., with atmospheric stability. Also, turbulence evolution from upstream to therotor positionconflicts with the frozen turbulence hypothesis, and its effect on the preview quality by a nacelle lidar still needs quantification. This work presents a simple method to extend the Mann model to account for thetemporal evolutionof turbulence using a space-time spectral velocity tensor. Under various atmospheric stability conditions, we derive the space-time tensor parameters and evaluate the space-time tensor using data from a five-beam lidar and a meteorological mast, we found good agreement between the space-time tensor and the measurement data. In addition, a numerical method for simulating a four-dimensional (4D) space-timevelocity fieldbased on the space-time tensor is presented. In the end, we analyze the importance of including the temporal evolution of turbulence for assessing lidar wind preview quality.

This paper proposes an adaptive path-following controller of small fixed-wing Unmanned Aerial Vehicles (UAVs) in the presence of wind disturbances, which explicitly considers that wind speed is time-varying. The main idea was to formulate UAVs path-following as control design for systems with parametric uncertainties and external disturbances. Assuming that there is no prior information on wind, the proposed solution is based on the L1 adaptive control, using linearized model dynamics. This approach makes clear statements for performance specifications of the controller and relaxes the common constant wind velocity assumption. This makes the design more realistic and the analysis more rigorous, because in practice wind is usually time varying (windshear, turbulence and gusting). The path-following controller was demonstrated in flight under wind speed up to 10m/s, representing 50% of the nominal UAV airspeed.

Coastal erosion describes the displacement of land caused by destructive sea waves, currents or tides. Major efforts have been made to mitigate these effects using groynes, breakwaters and various other structures. We address this problem by applying shape optimization techniques on the obstacles. We model the propagation of waves towards the coastline using two-dimensional porous Shallow Water Equations with artificial viscosity. The obstacle's shape, which is assumed to be permeable, is optimized over an appropriate cost function to minimize the height and velocities of water waves along the shore, without relying on a finite-dimensional design space, but based on shape calculus., Comment: 24 pages, 7 Figures. arXiv admin note: substantial text overlap with arXiv:2107.09464

Wind power is the fastest-growing technology among alternative energy production sources. Reliable forecasting of short-term wind power plays a critical role in the acquisition of most of the generated energy. In this study, short-term wind power forecast is performed using radial-based artificial neural networks, forecast error and cost to be minimized with the harmony search algorithm. Experimented results show that, we can predict wind power with fewer features and less error by using harmony search algorithm. A %7 percent improvement in RMSE rate has been achieved with the proposed method for short-term wind power prediction.

Calculations of the aerosol direct effect on climate rely on simulated aerosol fields. The model representation of aerosol mixing state potentially introduces large uncertainties into these calculations, since the simulated aerosol optical properties are sensitive to mixing state. In this study, we systematically quantified the impact of aerosol mixing state on aerosol optical properties using an ensemble of 1800 aerosol populations from particle-resolved simulations as a basis for Mie calculations for optical properties. Assuming the aerosol to be internally mixed within prescribed size bins caused overestimations of aerosol absorptivity and underestimations of aerosol scattering. Together, these led to errors in the populations' single scattering albedo of up to -22.3 % with a median of -0.9 %. The mixing state metric χ proved useful in relating errors in the volume absorption coefficient, the volume scattering coefficient and the single scattering albedo to the degree of internally mixing of the aerosol, with larger errors being associated with more external mixtures. At the same time, a range of errors existed for any given value of χ. We attributed this range to the extent to which the internal mixture assumption distorted the particles' black carbon content and the refractive index of the particle coatings. Both can vary for populations with the same value of χ. These results are further evidence of the important yet complicated role of mixing state in calculating aerosol optical properties.

Wind profiler radars are capable of measuring three-dimensional wind profiles at various altitudes of the atmosphere, at very high temporal and spatial resolution. The Advanced Centre for Atmospheric Radar Research (ACARR), located at Cochin University of Science and Technology (CUSAT), operates the World’s first 205 MHz stratospheretroposphere wind profiler radar which provides three-dimensional wind profiles for an altitude range of 315 m to 20 km. During non-rainy condition, the radar Doppler power spectrum bears the signature of ambient air motion whereas during rainy conditions, it contains signatures of both ambient air motion and fall velocity of rain droplets. The classification of Doppler power spectra for rainy (Precipitation) and non-rainy (Clear) conditions is necessary as wind profile retrieval from the former needs careful separation of ambient air motion from fall velocity of droplets. A manual classification of the power spectrum is cumbersome, time-consuming, and therefore not practical due to the vast database. This work intends to automate Doppler power spectra classification using the deep learning Convolutional Neural Network (CNN). The proposed Convolutional Neural Network model gives a k-fold validation accuracy of 99.77 % and testing accuracy of 99.60% for power spectra classification. The performance of CNN is compared against other popular machine learning classifiers such as Support Vector Machine, Decision Tree, K Nearest Neighbour and Naive Bayes. The performance comparison results show that the proposed CNN outperforms other models in radar Doppler power spectra classification.

The drive to develop environmental prediction systems that are seamless across both weather and climate timescales has culminated in the development and use of Earth system models, which include a coupled representation of the atmosphere, land, ocean and sea ice, for medium-range weather forecasts. One region where such a coupled Earth system approach has the potential to significantly influence the skill of weather forecasts is in the polar and sub-polar seas, where fluxes of heat, moisture and momentum are strongly influenced by the position of the sea ice edge. In this study we demonstrate that using a dynamically coupled ocean and sea ice model in the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System results in improved sea ice edge position forecasts in the Northern Hemisphere in the medium range. Further, this improves forecasts of boundary layer temperature and humidity downstream of the sea ice edge in some regions during periods of rapid change in the sea ice, compared to forecasts in which the sea surface temperature anomalies and sea ice concentration do not evolve throughout the forecasts. However, challenges remain, such as large errors in the position of the ice edge in the ocean analysis used to initialise the ocean component of the coupled system, which has an error of approximately 50 % of the total forecast error at day 9, suggesting there is much skill to be gained by improving the ocean analysis at and around the sea ice edge. The importance of the choice of sea ice analysis for verification is also highlighted, with a call for more guidance on the suitability of satellite sea ice products to verify forecasts on daily to weekly timescales and on meso-scales (< 500 km).

We use wind sensitivity experiments to understand the wind forcing dependencies of the level of no motion as the depth of maximum overturning and the e-folding pycnocline scale, as well as their relationship to northward transport of the mid-depth Atlantic meridional overturning circulation (AMOC) which extends vertically to the depth of maximum overturning of the upper AMOC cell. In contrast to previous studies, we investigate the interplay of nonlocal and local wind effects on a decadal timescale. We use 30-year simulations with a high-resolution ocean general circulation model (OGCM) which is an eddy-resolving version of the Max Planck Institute for Meteorology ocean model (MPIOM). Our findings deviate from the common perspective that the AMOC is a nonlocal phenomenon only, because northward transport and its depth scales depend on both nonlocal Southern Ocean wind effects and local wind effects in the Northern Hemisphere downwelling region where Ekman pumping takes place. Southern Ocean wind forcing predominantly determines the magnitude of the pycnocline scale throughout the basin, whereas Northern Hemisphere winds additionally influence the level of no motion locally. In that respect, the level of no motion is a better proxy for northward transport and mid-depth velocity profiles than the pycnocline scale, since the wind forcing dependencies of the level of no motion and maximum overturning are equal. The changes in maximum overturning with wind forcing are explained by the changes in the level of no motion only. This is because wind-driven Ekman compensation is baroclinic and occurs above the level of no motion, and the internal vertical velocity shear that is not influenced by the external Ekman cells stays approximately constant. The analysis of the wind experiments suggests a hemisphere-dependent scaling of the strength of AMOC. We put forward the idea that the ability of numerical models to capture the spatial and temporal variations of the level of no motion is crucial to reproduce the mid-depth cell in an appropriate way both quantitatively and dynamically.

The James Webb Space Telescope (JWST) will provide an opportunity to investigate the atmospheres of potentially habitable planets. Aerosols, significantly mute molecular features in transit spectra because they prevent light from probing the deeper layers of the atmosphere. Earth occasionally has stratospheric/high tropospheric clouds at 15-20 km that could substantially limit the observable depth of the underlying atmosphere. We use solar occultations of Earth's atmosphere to create synthetic JWST transit spectra of Earth analogs orbiting dwarf stars. Unlike previous investigations, we consider both clear and cloudy sightlines from the SCISAT satellite. We find that the maximum difference in effective thickness of the atmosphere between a clear and globally cloudy atmosphere is 8.5 km at 2.28 microns with a resolution of 0.02 microns. After incorporating the effects of refraction and Pandexo's noise modeling, we find that JWST would not be able to detect Earth like stratospheric clouds if an exo-Earth was present in the TRAPPIST-1 system, as the cloud spectrum differs from the clear spectrum by a maximum of 10 ppm. These stratospheric clouds are also not robustly detected by TauREx when performing spectral retrieval for a cloudy TRAPPIST-1 planet. However, if an Earth size planet were to orbit in a white dwarf's habitable zone, then, we predict that JWST's NIRSpec would be able to detect its stratospheric clouds after only 4 transits. We conclude that stratospheric clouds would not impede JWST transit spectroscopy or the detection of biosignatures for Earth-like atmospheres., Comment: 12 pages, 6 figures, Accepted for publication in MNRAS

BACKGROUND AND OBJECTIVES: Fine topographic information is a key input parameter for a detailed flood simulation and mapping. This study aimed to compare the accuracy statistics of the flood models developed using the digital elevation datasets with different resolutions from the light detection and ranging and interferometric synthetic aperture radar systems.METHODS: The study applied the Hydrologic Engineering Center-Hydrologic Modeling System and Hydrologic Engineering Center-River Analysis System models workable within the geographic information system to simulate and map flood hazards in Maapag Watershed. The models’ validity and accuracy were tested using the confusion error matrix, f-measurement, and the root means square error statistics.FINDINGS: Results show that using the light detection and ranging dataset, the model is accurate at 88%, 0.61, and 0.41; while using the interferometric synthetic aperture radar dataset, the model is accurate at 76%, 0.34, 0.53; for the error matrix, f-measurement, and root mean square error; respectively.CONCLUSION: The model developed using the light detection and ranging dataset showed higher accuracy than the model developed using the interferometric synthetic aperture radar. Nevertheless, the latter can be used for flood simulation and mapping as an alternative to the former considering the cost of model implementation and the smaller degree of accuracy residual error. Hence, flood modelers particularly from local authorities prefer to use coarser datasets to optimize the budget for flood simulation and mapping undertakings.

A hybrid airfoil is a scaled model for generating a full-scale ice shape for icing wind tunnel tests. This is possible by matching full-scale properties such as the distributions of collection effi...

The Antarctic Slope Current (ASC) plays a central role in redistributing water masses, sea ice, and tracer properties around the Antarctic margins, and in mediating cross-slope exchanges. While the ASC has historically been understood as a wind-driven circulation, recent studies have highlighted important momentum transfers due to mesoscale eddies and tidal flows. Furthermore, momentum input due to wind stress is transferred through sea ice to the ASC during most of the year, yet previous studies have typically considered the circulations of the ocean and sea ice independently. Thus, it remains unclear how the momentum input from the winds is mediated by sea ice, tidal forcing, and transient eddies in the ocean, and how the resulting momentum transfers serve to structure the ASC. In this study the dynamics of the coupled ocean–sea ice–ASC circulation are investigated using high-resolution process-oriented simulations and interpreted with the aid of a reduced-order model. In almost all simulations considered here, sea ice redistributes almost 100% of the wind stress away from the continental slope, resulting in approximately identical sea ice and ocean surface flows in the core of the ASC in a fully spun-up equilibrium state. This ice–ocean coupling results from suppression of vertical momentum transfer by mesoscale eddies over the continental slope, which allows the sea ice to accelerate the ocean surface flow until the speeds coincide. Tidal acceleration of the along-slope flow exaggerates this effect and may even result in ocean-to-ice momentum transfer. The implications of these findings for along- and across-slope transport of water masses and sea ice around Antarctica are discussed.

We use an analog method, based on displacements of Argo floats at their parking depth (nominally located around 1000 dbar) from the ANDRO dataset, to compute continuous, likely trajectories and estimate the Lagrangian dispersion. From this, we find that the horizontal diffusivity coefficient has a median value around 500 m2 s−1 but is highly variable in space, reaching values from 100 m2 s−1 in the gyre interior to 40 000 m2 s−1 in a few specific locations (in the Zapiola Gyre and in the Agulhas Current retroflection). Our analysis suggests that the closure for diffusivity is proportional to eddy kinetic energy (or square of turbulent velocity) rather than (absolute) turbulent velocity. It is associated with a typical turbulent time scale of 4–5.5 days, which is noticeably quite constant over the entire globe, especially away from coherent intense currents. The diffusion is anisotropic in coherent intense currents and around the equator, with a primary direction of diffusion consistent with the primary direction of horizontal velocity variance. These observationally based horizontal diffusivity estimations, and the suggested eddy kinetic energy closure, can be used for constraining, testing, and validating eddy turbulence parameterization.

Compared with the impulse LiDAR, the single-photon LiDAR has higher measurement sensitivity in the prominent feature, especially for space-based long-distance imaging. The distance measurement and the detection probability are the critical performance for LiDAR. The ranging of single-photon LiDAR is mainly different from the photon ranging of pulsed LiDAR. Dead time has a significant effect on distance measurement accuracy and detection probability, which are key parameters for detectors when implementing sound control. Therefore, the model of detector dead time, measurement accuracy, and detection probability should be established, and simulation results that meet application requirements should be achieved. Based on the single-photon ranging theory, the dead time, measurement accuracy, and detection probability model of single-photon LiDAR are studied. Furthermore, the systematic simulation of different contrasts is carried out according to the model. The simulation results demonstrate that the model can accurately perform the relationship between dead time and single-photon LiDAR system parameters. The research results can prove the design and verification of single-photon LiDAR dead time.

Accurate electricity demand forecasts play a crucial role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is necessary to provide not only accurate but also understandable and actionable forecasts. To provide accurate forecasts Global Forecasting Models (GFM) trained across time series have shown superior results in many demand forecasting competitions and real-world applications recently, compared with univariate forecasting approaches. We aim to fill the gap between the accuracy and the interpretability in global forecasting approaches. In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way. It provides different types of rules that explain the forecast of the global model and the counterfactual rules, which provide actionable insights for potential changes to obtain different outputs for given instances. We conduct experiments using a large-scale electricity demand dataset with exogenous features such as temperature and calendar effects. Here, we evaluate the quality of the explanations produced by the LIMREF framework in terms of both qualitative and quantitative aspects such as accuracy, fidelity, and comprehensibility and benchmark those against other local explainers., Accepted as a conference paper to AAAI 2022

Due to global warming, the decline in the Arctic sea ice has been accelerating over the last four decades, with a rate that was not anticipated by climate models. To improve these models, there is the need to rely on comprehensive field data. Seismic methods are known for their potential to estimate sea-ice thickness and mechanical properties with very good accuracy. However, with the hostile environment and logistical difficulties imposed by the polar regions, seismic studies have remained rare. Due to the rapid technological and methodological progress of the last decade, there has been a recent reconsideration of such approaches. This paper introduces a methodological approach for passive monitoring of both sea-ice thickness and mechanical properties. To demonstrate this concept, we use data from a seismic experiment where an array of 247 geophones was deployed on sea ice in a fjord at Svalbard, between March 1 and 24, 2019. From the continuous recording of the ambient seismic field, the empirical Green's function of the seismic waves guided in the ice layer was recovered via the so-called 'noise correlation function'. Using specific array processing, the multi-modal dispersion curves of the ice layer were calculated from the noise correlation function, and then inverted for the thickness and elastic properties of the sea ice via Bayesian inference. The evolution of sea-ice properties was monitored for 24 days, and values are consistent with the literature, as well as with measurements made directly in the field.

Natural variations in the strength of the northern stratospheric polar vortex, so-called polar vortex events, help to improve subseasonal-to-seasonal (S2S) predictions of winter climate. Past research on polar vortex events has been largely focused on sudden stratospheric warming events (SSWs), a class of relatively strong weakenings of the polar vortex. Commonly, SSWs are defined when the polar vortex reverses its climatological wintertime westerly wind direction. In this study, however, we use an alternative definition, based on the weighted time-integrated upward wave activity flux at the lower stratosphere. We use a long control simulation with a stratosphere-resolving model and the ERA5 reanalysis to compare various aspects of the wave activity definition with common SSWs over the Arctic. About half of the wave events are identical to common SSWs. However, there exist several advantages for defining stratospheric weak extremes based on wave events rather than using the common SSW definition: the wave activity flux definition captures with one criterion a variety of different event types, detects strong SSWs and strong final warming events, avoids weak SSWs that have little surface impact, and potentially lengthens the prediction horizon of the surface response. We therefore conclude that the wave driving represents a useful early indicator for stratospheric polar vortex events, which exploits the stratospheric potential for creating predictable surface signals better than common SSWs.

Rocky planets with temperate conditions provide the best chance for discovering habitable worlds and life outside the Solar System. In the last decades, new instrumental facilities and large observational campaigns have been driven by the quest for habitable worlds. Climate models aimed at studying the habitability of rocky planets are essential tools to pay off these technological and observational endeavours. In this context, we present EOS-ESTM, a fast and flexible model aimed at exploring the impact on habitability of multiple climate factors, including those unconstrained by observations. EOS-ESTM is built on ESTM, a seasonal-latitudinal energy balance model featuring an advanced treatment of the meridional and vertical transport. The novel features of EOS-ESTM include: (1) parameterizations for simulating the climate impact of oceans, land, ice, and clouds as a function of temperature and stellar zenith distance; (2) a procedure (EOS) for calculating the radiative transfer in atmospheres with terrestrial and non-terrestrial compositions illuminated by solar- and non-solar-type stars. By feeding EOS-ESTM with Earth's stellar, orbital and planetary parameters we derive a reference model that satisfies a large number of observational constraints of the Earth's climate system. Validation tests of non-terrestrial conditions yield predictions that are in line with comparable results obtained with a hierarchy of climate models. The application of EOS-ESTM to planetary atmospheres in maximum greenhouse conditions demonstrates the possibility of tracking the snowball transition at the outer edge of the HZ for a variety of planetary parameters, paving the road for multi-parametric studies of the HZ., 22 pages, 14 figures, 10 tables, accepted for publication in MNRAS

We present a new elastic LIDAR concept, based on a bi-axially mounted Nd:YAG laser and a telescope with HPD readout, combined with fast FADC signal digitization and offline pulse analysis. The LIDAR return signals have been extensively quality checked and absolutely calibrated. We analyse seven years of quasi-continuous LIDAR data taken during those nights when the MAGIC telescopes were operating. Characterization of the nocturnal ground layer yields zenith and azimuth angle dependent aerosol extinction scaleheights for clear nights. We derive aerosol transmission statistics for light emitted from various altitudes throughout the year and separated by seasons. We find further seasonal dependencies of cloud base and top altitudes, but none for the LIDAR ratios of clouds. Finally, the night sky background light is characterized using the LIDAR photon backgrounds., The financial support of the Spanish grant PID2019-107847RB-C42, funded by the Spanish Ministry of Science and Innovation (MCIN)/AEI/10.13039/501100011033, the German Federal Ministry of Education and ResearchBMBF and the German Max-Planck-Gesellschaft (MPG), and by the Croatian Science Foundation (HrZZ) Project IP-2016-06-9782 and the University of Rijeka Project uniri-prirod-18-48 is gratefully acknowledged.