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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.

The main energy input to the polar regions in winter is the advection of warm, moist air from lower latitudes. This makes the polar climate sensitive to the temperature and moisture of extrapolar air. Here, we study this sensitivity from an air-mass transformation perspective. We perform simulations of an idealized maritime air mass brought into contact with sea ice employing a three-dimensional large-eddy simulation model coupled to a one-dimensional multilayer sea ice model. We study the response of cloud dynamics and surface warming during the air-mass transformation process to varying initial temperature and humidity conditions of the air mass. We find in all cases that a mixed-phase cloud is formed, initially near the surface but rising continuously with time. Surface warming of the sea ice is driven by downward longwave surface fluxes, which are largely controlled by the temperature and optical depth of the cloud. Cloud temperature, in turn, is robustly constrained by the initial dewpoint temperature of the air mass. Since dewpoint only depends on moisture, the overall result is that surface warming depends almost exclusively on initial humidity and is largely independent of initial temperature. We discuss possible climate implications of this result—in particular, for polar amplification of surface warming and the role played by atmospheric energy transports.

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).

Remote Sensing satellites acquire information about an area by analyzing the transmitted and reflected radio waves. The Earth Imaging satellites capture high resolution images which occupy more storage space on-board and consume a lot of bandwidth for downlink transmission. Multispectral sensors represent information of the images in multiple bands which are typically less than 15. The multispectral image compression algorithms aim to reduce the size of the images while preserving their quality. This paper involves a study of various algorithms used for compression of multispectral imagery.

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

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.

We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the strength of the monitoring instruments. Finite-sample bounds on forecasting performance that account for estimation error are used to compute the expected loss of the competing forecasts as well as for the dynamic rotation strategy. Finally, using Monte Carlo simulations and empirical applications to forecasting inflation and stock returns, we demonstrate the potential gains from using conditioning information to rotate between forecasts.

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.

Due to the randomness of wind power generation, the output power of a wind farm will fluctuate. As a result, the power grid can face the need for increased reserve capacity, increasing scheduling difficulties and wind farm abandonment. An effective way to address these problems is to accurately predict the output power of wind farms. Traditional prediction methods usually make predictions based on wind data obtained at a single height. However, with long prediction periods, prediction errors are relatively large because the wind speed and direction at different heights have spatiotemporal correlations within a wind farm. In the model presented here, the wind power data are first decomposed by a ‘complete ensemble empirical mode decomposition with adaptive noise’ model to obtain modal components with different fluctuation characteristics. Then, the characteristics of wind speed, wind direction, air pressure and other data at different heights are extracted for spatiotemporal feature fusion. Actual measurement data from a wind farm in Chongqing, China are used to verify the feasibility and effectiveness of the proposed method.

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

Air–water mixing associated with high turbulence is a common hydraulic phenomenon, where the transport of entrained air bubbles and the dissipation of turbulent energy are key processes affecting the air–water mass transfer. Herein hydraulic jumps were selected as a seminal case of self-air-entrainment in transitional water flow, and the air–water flow properties were investigated at high Froude numbers from 10.5 to 13.5. The analysis of air concentration distributions and bubble size spectra enabled deviation of the rates of bubble diffusion and turbulence dissipation. The study was facilitated with re-analyses of earlier experimental data for smaller Froude numbers between 3.8 and 10. The spatial evolution of the diffusion and dissipation rates suggested that the diffusion of air bubbles and dissipation of turbulent structures were not independent processes, and, at high Froude numbers, the mix of air and water was subject to re-aeration of the turbulent shear layer by the roller.

With the increasing penetration of wind power generation, the fluctuating and intermittent behavior of wind power poses huge challenges to the operation and planning of distribution networks. A popular way to mitigate these challenges is to provide a group of possible wind power forecasting scenarios instead of depending on deterministic point forecasting values, so that system operators can consider the uncertainties. This letter proposes a novel WindGMMN method for wind power scenario forecasting, in which necessary modifications are made on the generative moment matching network (GMMN), and an optimization strategy is designed to find a series of wind power scenarios with similar shapes, probability distributions, and temporal correlations as potential scenarios. Simulations and analyses were performed on a public dataset with 2190 wind power generation curves and their corresponding meteorological features. The results show that the proposed WindGMMN outperforms popular baselines (e.g., variational auto-encoders and generative adversarial networks) for scenario forecasting of wind power, without any restrictions on the time horizon (e.g., times ranging from 10 min to 24 h).

Despite having plethora of works, wind speed time-series forecasting capabilities are prone to errors due to its intermittent and non-stationary nature as well as the limited generalization capabilities of forecasting methods for non-Gaussian distributed data. In this paper, a hybrid method that consists of elastic variational mode decomposition (eVMD) and forecasting random convolution nodes (fRCN) is proposed to forecast the Gaussian heteroscedastic wind speed time-series. The proposed eVMD algorithm gauges the non-stationary characteristics (complexity) of the wind speed signal and thereafter decomposes the signal into its intrinsic components (ICs) accordingly. The fRCN method rely on local receptive fields (LRF) to extract features that contribute to the local variations and the global trend in each IC. These features are subsequently learned using extreme learning machines (ELM) theories. An ensemble unit is employed to learn appropriate weightages for each forecasted IC before yielding the final forecasting values. Suitability of the proposed hybrid method for wind speed forecasting is evaluated via an actual wind speed dataset and comparing against various existing hybrid methods.

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

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.

This paper examines the physical controls of extratropical humidity and clouds by isolating the effects of cloud physics factors in an idealized model. The Held–Suarez dynamical core is used with the addition of passive water vapor and cloud tracers, allowing cloud processes to be explored cleanly. Separate saturation adjustment and full cloud scheme controls are used to consider the strength of advection–condensation theory. Three sets of perturbations to the cloud scheme are designed to test the model’s sensitivity to the physics of condensation, sedimentation, and precipitation formation. The condensation and sedimentation perturbations isolate two key differences between the control cases. First, the sub-grid-scale relative humidity distribution assumed for the cloud macrophysics influences the location and magnitude of the extratropical cloud maxima, which interrupt the isentropic transport of moisture to the polar troposphere. Second, within the model’s explicit treatment of cloud microphysics, re-evaporation of hydrometeors moistens and increases clouds in the lower troposphere. In contrast, microphysical processes of precipitation formation (specifically, the ratio of accretion to autoconversion) have negligible effects on humidity, cloudiness, and precipitation apart from the strength of the large-scale condensation and formation cycle. In addition, counterintuitive relationships—such as cloud condensate and cloud fraction responding in opposing directions—emphasize the need for careful dissection of physical mechanisms. In keeping with advection–condensation theory, circulation sets the patterns of humidity, clouds, and precipitation to first order, with factors explored herein providing secondary controls. The results substantiate the utility of such idealized modeling and highlight key cloud processes to constrain.

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.

Thermodynamical and dynamical aspects of the climate system response to anthropogenic forcing are often considered in two distinct frameworks: the former in the context of the forcing–feedback framework, and the latter in the context of eddy–mean flow feedbacks and large-scale thermodynamic constraints. Here we use experiments with the dynamical core of a general circulation model (GCM) to provide insights into the relationships between these two frameworks. We first demonstrate that the climate feedbacks and climate sensitivity in a dynamical core model are determined by its prescribed thermal relaxation time scales. We then perform two experiments: one that explores the relationships between the thermal relaxation time scale and the climatological circulation, and a second that explores the relationships between the thermal relaxation time scale and the circulation response to a global warming–like forcing perturbation. The results indicate that shorter relaxation time scales (i.e., lower climate sensitivities in the context of a dynamical core model) are associated with 1) a more vigorous large-scale circulation, including increased thermal diffusivity and stronger and more poleward storm tracks and eddy-driven jets, and 2) a weaker poleward displacement of the storm tracks and eddy-driven jets in response to the global warming–like forcing perturbation. Interestingly, the circulation response to the forcing perturbation effectively disappears when the thermal relaxation time scales are spatially uniform, suggesting that the circulation response to homogeneous forcing requires spatial inhomogeneities in the local feedback parameter. Implications for anticipating the circulation response to global warming and thermodynamic constraints on the circulation are discussed.

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.

This study explores the dynamics of intense coherent vortices in large-scale vertically sheared flows. We develop an analytical theory for vortex propagation and validate it by a series of numerical simulations. Simulations are conducted using both stable and baroclinically unstable zonal background flows. We find that vortices in stable westward currents tend to adjust to an equilibrium state characterized by quasi-uniform zonal propagation. These vortices persist for long periods, during which they propagate thousands of kilometers from their points of origin. The adjustment tendency is realized to a much lesser extent in eastward background flows. These findings may help to explain the longevity of the observed oceanic vortices embedded in predominantly westward flows. Finally, we examine the influence of background mesoscale variability induced by baroclinic instability of large-scale flows on the propagation and persistence of isolated vortices.

The neighborhood-based ensemble evaluation using the continuous ranked probability score is based on the pooling of the cumulative density function (CDF) for all the points inside a neighborhood. This methodology can be applied to the forecast CDF for measuring the predictive input of neighboring points in the center of the neighborhood. It can also be applied at the same time to forecast CDF and observed CDF so as to quantify the quality of the pooled ensemble forecast at the scale of the neighborhood. Fair versions of these two neighborhood scores are also defined in order to reduce their dependencies on the size of ensemble forecasts. The borderline case of deterministic forecasts is also explored so as to be able to compare them with ensemble forecasts. The information of these new scores is analyzed on idealized and real cases of rain accumulated during 3 h and of 2-m temperature forecast by four deterministic and probabilistic forecasting systems operational at Météo-France.

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.

Remote sensing data play a critical role in improving numerical weather prediction (NWP). However, the physical principles of radiation dictate that data voids frequently exist in physical space (e.g., subcloud area for satellite infrared radiance or no-precipitation region for radar reflectivity). Such data gaps impair the accuracy of initial conditions derived from data assimilation (DA), which has a negative impact on NWP. We use the barotropic vorticity equation to demonstrate the potential of deep learning augmented data assimilation (DDA), which involves reconstructing spatially complete pseudo-observation fields from incomplete observations and using them for DA. By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation of the “reconstruction operator,” which maps spatially incomplete observations to a model state with full spatial coverage and resolution. The CAE was applied to an incomplete streamfunction observation (∼30% missing) from a high-resolution benchmark simulation and demonstrated satisfactory reconstruction performance, even when only very sparse (1/16 of T63 grid density) observations were used as input. When only spatially incomplete observations are used, the analysis fields obtained from ensemble square root filter (EnSRF) assimilation exhibit significant error. However, in DDA, when EnSRF takes in the combined data from the incomplete observations and CAE reconstruction, analysis error reduces significantly. Such gains are more pronounced with sparse observation and small ensemble size because the DDA analysis is much less sensitive to observation density and ensemble size than the conventional DA analysis, which is based solely on incomplete observations. Significance Statement Data assimilation plays a critical role in improving the skills of modern numerical weather prediction by establishing accurate initial conditions. However, unobservable regions are common in observation data, particularly those derived from remote sensing. The nonlinear relationship between data from observable regions and the physical state of unobservable regions may impede DA efficiency. As a result, we propose that deep learning be used to improve data assimilation in such cases by reconstructing a spatially complete first guess of the physical state with deep learning and then applying data assimilation to the reconstructed field. Such deep learning augmentation is found effective in improving the accuracy of data assimilation, especially for sparse observation and small ensemble size.

The weak temperature gradient (WTG) approximation is extended to the basic equations on a rotating plane. The circulation is decomposed into a diabatic component that satisfies WTG balance exactly and a deviation from this balance. Scale analysis of the decomposed basic equations reveals a spectrum of motions, including unbalanced inertio-gravity waves and several systems that are in approximate WTG balance. The balanced systems include equatorial moisture modes with features reminiscent of the MJO, off-equatorial moisture modes that resemble tropical depression disturbances, “mixed systems” in which temperature and moisture play comparable roles in their thermodynamics, and moist quasigeostrophic motions. In the balanced systems the deviation from WTG balance is quasi nondivergent, in nonlinear balance, and evolves in accordance to the vorticity equation. The evolution of the strictly balanced WTG circulation is in turn described by the divergence equation. WTG balance restricts the flow to evolve in the horizontal plane by making the isobars impermeable to vorticity and divergence, even in the presence of diabatically driven vertical motions. The vorticity and divergence equations form a closed system of equations when the irrotational circulation is in WTG balance and the nondivergent circulation is in nonlinear balance. The resulting “WTG equations” may elucidate how interactions between diabatic processes and the horizontal circulation shape slowly evolving tropical motions. Significance Statement Many gaps in our understanding of tropical weather systems still exist and there are still many opportunities to improve their forecasting. We seek to further our understanding of the tropics by extending a framework known as the “weak temperature gradient approximation” to all of the equations for atmospheric flow. Doing this reveals a variety of motions whose scales are similar to observed tropical weather systems. We also show that two equations describe the evolution of slow systems: one that describes tropical thunderstorms and one for the rotating horizontal winds. The two equations may help us understand the dynamics of slowly evolving tropical systems.

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).