Changes In Wind Speed Research Articles

Establishing and modeling the causality relationship of hydro-climatic and land cover change variables with water quality over Lake Tana, Ethiopia

Lakes, the most widespread inland water bodies in the globe, are highly susceptible to change in trophic state due to external factors. Changing hydro-climatic conditions and land cover changes (LCC) can cause lake water quality deterioration. This study establishes the quantitative relationship between variability in the water quality index and changes in hydro-climatic and LCC variables. Water quality is represented by the Forel-ule index (FUI) whereas the hydro-climatic variables considered in this study are lake bottom layer temperature (lblt), lake total layer temperature (ltlt), precipitation, runoff, evaporation, lake skin temperature (lskt), surface wind speed and air temperature. The LCC is quantified by lower and higher level leaf area index (Lv-lai and Hv-lai). FUI has a positive relationship with surface wind speed, precipitation, runoff, ltlt, lblt, and LCC and a negative relationship with evaporation, lskt, and air temperature with 95% confidence level over most parts of the Lake. The temporal correlation is also apparent from the long-term trend pattern. A significant decreasing trend is observed in FUI and lake bottom layer temperature (lblt). In contrast, an insignificant increasing trend is observed in air temperature and lake skin temperature (lskt). The changes in LCC, runoff, precipitation, and surface wind speed is insignificant between 2000 and 2020. Moreover, the phase composites of FUI and hydro-climatic and LCC variables derived from multichannel singular spectrum analysis (MSSA) show strong seasonal modulation of water quality by hydro-climatic and LCC variables. The annual cycle represented by the first two eigenmodes (except wind speed which is represented by the second and third eigenmodes) accounts for between 27.41% (wind speed) to 52.32% (precipitation) of the total joint spatiotemporal variability of FUI and the driving variables. The convergent cross-mapping (CCM) analysis shows that cross-map skill (ρ2) is increased with increasing library length (L) and time delay (τ), which suggests significant causal effects of hydro-climatic and LCC variables on FUI and the lagged causation is consistent with maximum values of ρ2. The significant feedback of FUI to changes in hydro-climatic and LCC variables shows the possibility of hindcast/forecast of the historical/future status of water quality from hydro-climatic and LCC variables. As a result, a multivariate nonlinear regression model (MNWQFM) is developed to forecast the lake water quality index from the hydro-climatic and LCC variables. The model has high performance with R2 of 83.6% and root means square error (RMSE) of 0.15 in FUI.

Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model

The Caribbean region is prone to the strong winds and low air pressures of tropical cyclones and their corresponding storm surge that driving coastal flooding. To protect coastal communities from the impacts of tropical cyclones, it is important to understand how this impact of tropical cyclones might change towards the future. This study applies the storyline approach to show what tropical cyclones Maria (2017) and Dorian (2019) could look like in a 2 °C and 3.4 °C warmer future climate. These two possible future climates are simulated with a high-resolution regional climate model using the pseudo global warming approach. Using the climate response from these simulations we apply a Delta-quantile mapping technique to derive future changes in wind speed and mean sea level pressure. We apply this Delta technique to tropical cyclones Maria and Dorian’s observed wind and pressure fields to force a hydrodynamic model for simulating storm surge levels under historical and future climate conditions. Results show that the maximum storm surge heights of Maria and Dorian could increase by up to 0.31 m and 0.56 m, respectively. These results clearly show that future changes in storm surge heights are not negligible compared to end-of-the-century sea level rise projections, something that is sometimes overlooked in large-scale assessments of future coastal flood risk.

Simultaneous Damping and Frequency Control in AC Microgrid Using Coordinated Control Considering Time Delay and Noise

The incorporation of converter-based generating sources in utility-scale microgrids causes frequency instability and low-frequency oscillations (LFOs), which is also a reason for degeneration in system stability. Damping frequency control is an essential part of alternating current (AC) microgrid system operation and control. Sudden changes in load, variations in renewable power outputs due to changes in solar insolation or wind speed, and so on factors cause the system frequency to deviate from the nominal value. Therefore, the role of a frequency controller is to maintain the dynamic stability in an AC microgrid by retaining the system frequency at the nominal value. Again, AC microgrids with high renewable power penetration face even more difficulty in maintaining frequency stability because of their poor inertial response. The research presented here proposes a novel approach for grid-connected AC microgrid oscillation damping and frequency control that simultaneously takes into consideration time delay and noise. To improve the frequency response and dampen LFOs by providing the voltage and frequency within the specified range, a coordinated technique-based control approach is adopted. In the developed hybrid control, the frequency controller relies on active power modulation, while the power oscillation damping controller is dependent on reactive power modulation. To improve stability and reduce communication consequences such as noise and signal latency (time delay), the developed power oscillation damping controller and frequency controller are coordinated along with the robust linear quadratic Gaussian controller. The comparative investigation of the effectiveness of the coordinated control technique is employed in the software of MATLAB/Simulink for grid-connected AC microgrid. The outcome of the simulation illustrates the superior effectiveness of the suggested controller over the traditional droop controller for huge power flows under disturbance with various operating conditions, under/overfrequency events, time delay, and noise. This grid encouragement capability for AC microgrids is anticipated to lead to novel possibilities for generating revenue.

Multiple Evolution Modes of Megaripples in the Qaidam Basin and Implications for Ripple‐Like Aeolian Landforms on Mars

AbstractAeolian landforms provide valuable insights into the planetary surface environment and its evolutionary history. In this study, the formation and evolution of megaripples in the Qaidam Basin and their relationship with the development environment are analyzed. By quantifying the wind environment, morphology, grain‐size distribution, sedimentary structure, and luminescence age of megaripples, we propose for the first time that there are multiple megaripple evolution modes. Investigation revealed that three evolution modes were responsible for forming megaripples in different equilibrium states: transient, stable, and metastable. Well‐sorted coarse sand grains accumulate on ridges and overlay poorly sorted fine sand grains to form transient megaripples. Stable megaripples have alternating sedimentary bedding of coarse and fine sand grains. Metastable megaripples have a secondary ripple formation on the surface. Throughout their formation, coarse and fine sand grains undergo regrouping. The response of coarse grains to the change in wind speed lags behind that of fine grains. This process controls the erosion and accumulation of megaripples and affects their size and sedimentary structures. The evolution mode, scale, and sedimentary structure of megaripples are influenced by the grain‐size range under the same wind conditions. The luminescence ages of the coarse‐grained megaripple sediments are less than 700 years. This study provides a fresh perspective on the coexistence of various sand ripples and transverse aeolian ridges found on Mars.

Informer Model based Wind Power Forecast with Tropical Storms Present

When severe tropical storms pass through, regional wind speeds fluctuate greatly, and the volatility of wind farm output also increases significantly. At the same time, the duration of tropical storms is long, and it is difficult for short-term time series data prediction models to be effective, in which case the unstable output of wind turbines will have a greater impact on power system dispatching. This paper first examines the characteristics of tropical storm movement, namely the change in wind speed, and then uses the Informer long time series data prediction model to predict the total change in wind turbine output in the next 10 days after the storm has passed. The actual case proves that the Informer model is ideal in predicting the long time series of wind power output during a tropical storm.

An efficient MPPT techniques for inter-harmonic reduction in grid connected hybrid wind and solar energy systems

In this work, the operation of photovoltaic system, wind turbine driven doubly fed induction generator along with battery has been observed. Also, a searching space minimization-based artificial bee colony scheme is developed for tracking the maximum power in a doubly fed induction generator-based system. To track maximum power in solar systems, an improved adaptive reference voltage approach has been presented. Several conventional and optimization-based techniques are used by DFIG and photovoltaic systems to get around the non-linearity features in the output parameters. Regarding DFIG, the artificial bee colony method based on searching space minimization can be used to solve the shortcomings of the perturb and observe algorithm. Because of its weather-sensitive nature, it can withstand sudden changes in wind speed. The suggested searching space minimization based artificial bee colony strategy uses a mechanism for determining the range of optimal rotor speed in order to track the maximum power point more quickly. The maximum power point tracking performance of the adaptive reference voltage technique is superior to that of current perturb and observed-based systems. However, a huge processing memory is required in order to track the maximum possible power point. This paper proposes an enhanced maximum power point tracking technique based on adaptive reference voltage that does not require a memory unit. Additionally, despite sudden changes in irradiation conditions, improved adaptive reference voltage can drift-free and reliably monitor the maximum power point. The new adaptive reference voltage technique uses temperature and radiation sensors to identify the region nearest to the maximum power point. This helps the system respond more quickly. The proposed system with searching space minimization based artificial bee colony and improved adaptive reference voltage schemes displays lower inter-harmonic content in grid current compared to perturb and observe scheme. The proposed scheme has been implemented in MATLAB & simulink atmosphere and OPAL-RT displayed satisfactory results.

Understanding the Spatiotemporal Variability of Tropical Orographic Rainfall Using Convective Plume Buoyancy

Abstract Mechanical forcing by orography affects precipitating convection across many tropical regions, but controls on the intensity and horizontal extent of the orographic precipitation peak and rain shadow remain poorly understood. A recent theory explains this control of precipitation as arising from modulation of lower-tropospheric temperature and moisture by orographic mechanical forcing, setting the distribution of convective rainfall by controlling parcel buoyancy. Using satellite and reanalysis data, we evaluate this theory by investigating spatiotemporal precipitation variations in six mountainous tropical regions spanning South and Southeast Asia, and the Maritime Continent. We show that a strong relationship holds in these regions between daily precipitation and a measure of convective plume buoyancy. This measure depends on boundary layer thermodynamic properties and lower-free-tropospheric moisture and temperature. Consistent with the theory, temporal variations in lower-free-tropospheric temperature are primarily modulated by orographic mechanical lifting through changes in cross-slope wind speed. However, winds directed along background horizontal moisture gradients also influence lower-tropospheric moisture variations in some regions. The buoyancy measure is also shown to explain many aspects of the spatial patterns of precipitation. Finally, we present a linear model with two horizontal dimensions that combines mountain wave dynamics with a linearized closure exploiting the relationship between precipitation and plume buoyancy. In some regions, this model skillfully captures the spatial structure and intensity of rainfall; it underestimates rainfall in regions where time-mean ascent in large-scale convergence zones shapes lower-tropospheric humidity. Overall, these results provide new understanding of fundamental processes controlling subseasonal and spatial variations in tropical orographic precipitation.

Climate Risk Management in Cultural Heritage for Inclusive Adaptation Actions in Nigeria

Different regions around the world are experiencing climate risks, including increasing temperatures, rapid changes in rainfall patterns, loss of biodiversity and extreme weather events. Within the last decade, Nigeria has experienced a series of localised and regional drought and flooding events affecting not only arable farmlands but also cultural heritage, including heritage buildings and cultural landscapes. This study assesses climate-related risks affecting cultural heritage using the ABC risk assessment method to understand the impacts of key climate drivers. The assessment method was applied to five cultural heritage sites with different values and functions. The findings revealed that changes in precipitation and wind speed and direction induce most of the sudden-onset impacts, such as bushfires, flooding and physical collapse. A sense of community connection and attachment to the built heritage remain strong but there have been limited efforts to implement actions that address climate risks to the built heritage and its surrounding spaces. The output of the assessment contributes to risk prioritisation and informs decision making for developing the needed adaptive actions. The study demonstrates the need to leverage climate information collected by different national and international organisations not to only assess climate risks to heritage but also to improve the involvement of local communities and non-heritage professionals in developing adaptation actions for built heritage.

The weakening effect of urbanization on tropical cyclone surface winds : An observational study for Shanghai

The observation-reanalysis difference method (OMR) and wind profile fitting method were used to analyze the influence urbanization has on the near-surface wind speed in Shanghai during tropical cyclone events. The data used include daily wind speed data from the Shanghai Meteorological Observatory Station collected from 1991 to 2020, hourly wind speed data from 100 m high wind towers from 2017 to 2019, and reanalysis data that covered the same period. The results indicated that during tropical cyclone occurrence, the wind speed of the OMR in the central urban region was significantly lower than that in the suburban area, and the OMR declined more obviously over the year, down to −8 m/s in 2020. Urbanization leads to the increase of average wind weakening rate to be higher than the maximum wind weakening rate, causing the maximum weakening rate of the central urban region to the average wind over 80%, while maximum wind speed was less than 80%. The weakening rate of both the average and maximum wind speeds during tropical cyclone impacts is greater than the rate that the annual average wind speed was reduced. A logarithmic variation was visible in the wind profile of the island station during tropical cyclone occurrence, with an average friction velocity U∗ of 0.9389 m/s and an average rough length Z0 of 0.4915 m. The wind speed during tropical cyclone events was higher than the three-year average wind speed within each layer. The suburban stations show a linear variation, and every hundred meters of height results in an increase of 5–6 m/s in the wind speed change rate. At 100 m of altitude, the wind speed in the suburban region is reduced by approximately 40%.

Optimal energy management system for grid-connected hybrid power plant and battery integrated into multilevel configuration

A novel optimal energy management system (EMS) using a nonlinear constrained multivariable function to optimize the operation of battery energy storages (BESs) used in a hybrid power plant with wind turbine (WT) and photovoltaic (PV) power plants is proposed in this work. The hybrid power plant uses a configuration based on a battery-stored impedance-based cascaded multilevel inverter to integrate renewable energy sources (PV power plants and WT) and BESs into the grid. The new optimal EMS seeks for satisfying the demanded power while dispatching power between BESs to optimize their efficiency. A grid-connected configuration is implemented to assess the efficiency of the suggested supervisory control under changes in renewable energy (changes in wind speed and irradiation), and in a varying active and reactive powers’ request. The BES efficiency obtained from the suggested EMS is set side by side to the BES efficiency got from a conventional EMS and a model predictive control (MPC), both working based on the state-of-charge (SOC) of the BES and balancing power EMS. The results from MATLAB simulation and the experimental results with the real-time OPAL-RT simulator (OP4510, OPAL-RT) and dSPACE MicroLabBox show the effectiveness of the suggested approach and the improvement in long-term BES efficiency.

Design of fuzzy PID control system for quad-rotor UAV based on particle swarm algorithm under wind disturbance conditions

This project proposes an intelligent control method that employs a particle swarm algorithm to optimize the rules of a fuzzy controller. The rules of the fuzzy controller are continuously adjusted based on feedback data on the attitude changes of a quadrotor unmanned aerial vehicle and the enhanced particle swarm algorithm. This enables the controller to learn autonomously, thereby enhancing its performance under various conditions. In addition to optimizing the rules of the fuzzy controller, an analysis and modeling process is conducted for the characteristics of wind speed changes under natural conditions. The resulting model is introduced into the system as environmental noise, thereby improving the controller’s performance under different conditions. Experiments are conducted in the Matlab/Simulink simulation environment to test the performance of the control algorithm. The algorithm’s anti-disturbance capability and control accuracy are compared when facing complex disturbances. This research methodology offers new possibilities for precise control of quadrotor unmanned aerial vehicles and provides valuable references for future drone technology development.

Wind- and rain-driven macroplastic mobilization and transport on land

Wind and rain are considered main drivers for mobilization and transport of macroplastics on land, yet there is a lack of empirical data that quantifies this. We present lab experiment results on land-based macroplastic mobilization and transport. We placed four types of macroplastics on terrains with varying surface roughness and slope angles, and exposed them to changing wind speeds and rain intensities. In general, we find that the mobilization probability and transport velocity of macroplastics strongly depend on the combination of the terrain characteristics and material properties. At Beaufort 3, 100% of the plastic bags were mobilized, whereas for the other plastic types less than 50% were mobilized. We found 1.4 (grass) to 5 times (paved surface) higher mobilization probabilities on land than assumed by existing plastic transport models. Macroplastic transport velocities were positively correlated with wind speed, but not with rain intensity. This suggests that macroplastics are not transported on land by rain unless surface runoff develops that can bring the macroplastics afloat. Macroplastic transport velocities were, driven by wind, 1.9 and, driven by rain, 4.9 times faster on paved surfaces than on grass. This study enhances our understanding of land-based macroplastic transport and provides an empirical basis for models.

Analysis of flux footprints in fragmented, heterogeneous croplands

An accurate quantification of fluxes from heterogeneous sites and further bifurcation into contributing homogeneous fluxes is an active field of research. Among such sites, fragmented croplands with varying surface roughness characteristics pose formidable challenges for footprint analysis. We conducted two flux monitoring experiments in fragmented croplands characterized by two dissimilar surfaces with objectives to: (i) evaluate the performance of two analytical footprint models in heterogeneous canopy considering aggregated roughness parameters and (ii) analyze the contribution of fluxes from individual surfaces under changing wind speed. A set of three eddy covariance (EC) towers (one each capturing the homogenous fluxes from individual surfaces and a third, high tower capturing the heterogeneous mixed fluxes) was used for method validation. High-quality EC fluxes that fulfill stationarity and internal turbulence tests were analyzed considering daytime, unstable conditions. In the first experiment, source area contribution from a surface is gradually reduced by progressive cut, and its effect on high-tower flux measurements is analyzed. Two footprint models (Kormann and Meixner ‘KM’; analytical solution to Lagrangian model ‘FFP’) with modified surface roughness parameters were applied under changing source area contributions. FFP model has consistently over predicted the footprints (RMSEFFP = 0.31 m−1, PBIASFFP = 19.00), whereas KM model prediction was gradually changed from over prediction to under prediction towards higher upwind distances (RMSEKM = 0.02 m−1, PBIASKM = 8.50). Sensitivity analysis revealed that the models are more sensitive to turbulent conditions than surface characteristics. This motivated to conduct the second experiment, where the fractional contribution of individual surfaces (α and β) to the heterogeneous fluxes measured by the high tower (T3) was estimated using the principle of superposition (FT3 = α FT1 + β FT2). Results showed that α and β are dynamic during daylight hours and strongly depend on mean wind speed (U) and friction velocity (u*). The contribution of fluxes from adjoining fields [1 − (α + β)] is significant beyond 80% isopleth. Our findings provide guidelines for future analysis of fluxes in heterogeneous, fragmented croplands.

An experimental study on the aerodynamic loads of a floating offshore wind turbine under imposed motions

Abstract. The rotor of a floating wind turbine is subject to complex aerodynamics due to changes in relative wind speeds at the blades and potential local interactions between blade sections and the rotor near wake. These complex interactions are not yet fully understood. Lab-scale experiments are highly relevant for capturing these phenomena and provide means for the validation of numerical design tools. This paper presents a new wind tunnel experimental setup able to study the aerodynamic response of a wind turbine rotor when subjected to prescribed motions. The present study uses a 1:148 scale model of the DTU 10 MW reference wind turbine mounted on top of a 6 degrees of freedom parallel kinematic robotic platform. Firstly, the thrust variation of the turbine is investigated when single degree of freedom harmonic motions are imposed by the platform, with surge, pitch and yaw being considered in this study. For reduced frequencies greater than 1.2, it is found that the thrust variation is amplified by up to 150 % compared to the quasi-steady value when the turbine is subject to pitch and surge motions, regardless of the amplitude of motion. A similar behaviour is also noticed under yaw motions. Secondly, realistic 6 degrees of freedom motions are imposed by the platform. The motions are derived from FAST simulations performed on the full-scale turbine coupled with the TripleSpar floater, and the tests aim at exploring the thrust force dynamics for different sea states and wind conditions, obtaining reasonable agreement with the simulations. Finally, the work shows the capabilities of an off-the-shelf hexapod to conduct hybrid testing of floating offshore wind turbines in wind tunnels, as well as its limitations in performing such tests.

Reliability ensemble averaging reduces surface wind speed projection uncertainties in the 21st century over China

Accurate prediction of future surface wind speed (SWS) changes is the basis of scientific planning for wind turbines. Most studies have projected SWS changes in the 21st century over China on the basis of the multi-model ensemble (MME) of the 6th Coupled Model Intercomparison Project (CMIP6). However, the simulation capability for SWS varies greatly in CMIP6 multi-models, so the MME results still have large uncertainties. In this study, we used the reliability ensemble averaging (REA) method to assign each model different weights according to their performances in simulating historical SWS changes and project the SWS under different shared socioeconomic pathways (SSPs) in 2015–2099. The results indicate that REA considerably improves the SWS simulation capacity of CMIP6, eliminating the overestimation of SWS by the MME and increasing the simulation capacity of spatial distribution. The spatial correlations with observations increased from 0.56 for the MME to 0.85 for REA. Generally, REA could eliminate the overestimation of the SWS by 33% in 2015–2099. Except for southeastern China, the SWS generally decreases over China in the near term (2020–2049) and later term (2070–2099), particularly under high-emission scenarios. The SWS reduction projected by REA is twice as high as that by the MME in the near term, reaching −4% to −3%. REA predicts a larger area of increased SWS in the later term, which expands from southeastern China to eastern China. This study helps to reduce the projected SWS uncertainties.

Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

Acoustic features as a tool to visualize and explore marine soundscapes: Applications illustrated using marine mammal passive acoustic monitoring datasets.

Passive Acoustic Monitoring (PAM) is emerging as a solution for monitoring species and environmental change over large spatial and temporal scales. However, drawing rigorous conclusions based on acoustic recordings is challenging, as there is no consensus over which approaches are best suited for characterizing marine acoustic environments. Here, we describe the application of multiple machine-learning techniques to the analysis of two PAM datasets. We combine pre-trained acoustic classification models (VGGish, NOAA and Google Humpback Whale Detector), dimensionality reduction (UMAP), and balanced random forest algorithms to demonstrate how machine-learned acoustic features capture different aspects of the marine acoustic environment. The UMAP dimensions derived from VGGish acoustic features exhibited good performance in separating marine mammal vocalizations according to species and locations. RF models trained on the acoustic features performed well for labeled sounds in the 8 kHz range; however, low- and high-frequency sounds could not be classified using this approach. The workflow presented here shows how acoustic feature extraction, visualization, and analysis allow establishing a link between ecologically relevant information and PAM recordings at multiple scales, ranging from large-scale changes in the environment (i.e., changes in wind speed) to the identification of marine mammal species.

ВЛИЯНИЕ ТРЕНДА СКОРОСТИ ВЕТРА НА ПРОДОЛЖИТЕЛЬНОСТЬ ПЕРИОДА ТЕРМИЧЕСКОГО КОМФОРТА НА ЕВРОПЕЙСКОЙ ЧАСТИ РОССИИ В ПОСЛЕДНИЕ ДЕСЯТИЛЕТИЯ

The combination of the parameters of temperature, humidity, and wind conditions of surface air, solar radiation fluxes, human metabolism under various modes of physical exertion and clothing properties characterize the thermal comfort. Under conditions of global warming, the climate characteristics that affect the quality of life, health, and well-being of people, in particular, the length of thermal comfort periods, are changing. Bioclimatic indices that determine the thermal comfort conditions depend not only on temperature changes, but also on humidity and wind speed trends. When comparing the results of the calculations of the thermal comfort period length with the use of air temperature observations and major bioclimatic indices, the authors have revealed a significant difference in the effect of changes in average wind speed on the length of the thermal comfort period in different regions of European Russia.

Functional interactions between coat structure and colour in the determination of solar heat load on arid living kangaroos in summer: balancing crypsis and thermoregulation

Interactions of solar radiation with mammal fur are complex. Reflection of radiation in the visible spectrum provides colour that has various roles, including sexual display and crypsis, i.e., camouflage. Radiation that is absorbed by a fur coat is converted to heat, a proportion of which impacts on the skin. Not all absorption occurs at the coat surface, and some radiation penetrates the coat before being absorbed, particularly in lighter coats. In studies on this phenomenon in kangaroos, we found that two arid zone species with the thinnest coats had similar effective heat load, despite markedly different solar reflectances. These kangaroos were Red Kangaroos (Osphranter rufus) and Western Grey Kangaroos (Macropus fuliginosus).Here we examine the connections between heat flow patterns associated with solar radiation, and the physical structure of these coats. Also noted are the impacts of changing wind speed. The modulation of solar radiation and resultant heat flows in these coats were measured at wind speeds from 1 to 10 m s−1 by mounting them on a heat flux transducer/temperature-controlled plate apparatus in a wind tunnel. A lamp with a spectrum like solar radiation was used as a proxy for the sun. The integrated reflectance across the solar spectrum was higher in the red kangaroos (40 ± 2%) than in the grey kangaroos (28 ± 1%). Fur depth and insulation were not different between the two species, but differences occurred in fibre structure, notably in fibre length, fibre density and fibre shape. Patterns of heat flux within the species’ coats occurred despite no overall difference in effective solar heat load. We consider that an overarching need for crypsis, particularly for the more open desert-adapted red kangaroo, has led to the complex adaptations that retard the penetrance of solar radiation into its more reflective fur.

Short-term wind speed forecasts through hybrid model based on improved variational mode decomposition

ABSTRACT Scientific and accurate wind predictions are the basis for exploiting and utilizing wind energy. Combining the VMD and KELM, this research proposes a new hybrid model to capture the pattern of wind speed change, the parameters of which are optimized by the Spotted Hyena Optimizer(SHO) and Seagull Optimization Algorithm(SOA), respectively. The VMD is optimized by the SHO to achieve the purpose of adaptive decomposition of historical wind speed time series. After that, the parameters of the KELM are optimized by the SOA. Then, the decomposed wind speed series is input into the optimized KELM. The output prediction Intrinsic Mode Functions(IMFs) is added up to obtain the short-term wind speed prediction results. The measured wind speeds for four seasons were selected as the case study for the proposed model. The prediction results are compared with the measured wind speed series to verify the accuracy and reliability of the model. Besides, the prediction accuracy and stability of the proposed model are better than traditional prediction models, with stronger performance and lower computational cost. The results of case studies indicate that the proposed model can satisfy the need for actual applications.