Daily Average Wind Speed Research Articles

The Impact of Meteorological Factors on Stroke Incidence in the Transdanubian Region of Hungary

Cerebrovascular diseases are the leading cause of death and disability. The epidemiological background and predisposing factors have been the basis of many studies. We aimed to assess the effect of seasonal variability and meteorological factors on stroke incidence in Hungary. National and county-level secondary data were assessed for 2018–2019. We identified stroke with ICD codes I60, I61, I62 (hemorrhagic), I63, I65, and I66 (ischemic). The data were obtained from the University of Pécs Clinical Centre (number of patients per day according to sex and disease subtype, n = 1765). Daily average and maximum wind speed [m/s], precipitation [mm], temperature [°C], and frontal effect [warm/cold/mixed/no effect]) were provided by the Hungarian Meteorological Service. We found that 89.92% of the patients were hospitalized for ischemic and 10.08% for hemorrhagic stroke. We observed a significantly higher number of cases in the other months compared to winter (spring: +35.9%; p = 0.007, summer: +59.0%; p = 0.016, autumn: +36.5%; p = 0.01). In autumn, an increase in temperature increased the incidence of stroke (r = 0.210; p = 0.004). Temperature change affected ischemic stroke incidence (r = 0.112; p = 0.003). In contrast, the number of hemorrhagic stroke cases showed a mild but significant negative association with daily temperature change (r = −0.073; p = 0.049). Overall, a 1 °C temperature change compared to the previous day increased the daily number of admissions by 2.9% (p = 0.017). Air pressure change also affected hemorrhagic stroke incidence (r = 0.083; p = 0.025). Changes in temperature and frontal effects can increase the incidence of stroke. Modern forecasting technology can help the healthcare system prepare for possible increased workloads during critical periods.

Takagi–Sugeno Fuzzy Parallel Distributed Compensation Control for Low-Frequency Oscillation Suppression in Wind Energy-Penetrated Power Systems

In this paper, a Takagi–Sugeno fuzzy parallel distributed compensation control (TS-PDCC) is proposed for low-frequency oscillation (LFO) suppression in wind energy-penetrated power systems. Firstly, the fuzzy C-mean algorithm (FCMA) is applied to cluster the daily average wind speed of the wind farm, and the obtained wind speed clustering center is used as the premise variable of TS-PDCC, which increases the freedom of parameter setting of the TS fuzzy model and is closer to the actual working environment. Secondly, based on the TS fuzzy model, the TS-PDCC is designed to adjust the active power output of the wind turbine for LFO suppression. To facilitate the computation of controller parameters, the stability conditions are transformed into a set of Linear Matrix Inequalities (LMIs) via the Schur complement. Subsequently, a Lyapunov function is designed to verify the stability of the wind energy-penetrated power system and obtain the parameter ranges. Simulation cases are conducted to verify the validity and superior performance of the proposed TS-PDCC under different operating conditions.

The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.

A Comparative Study on the Estimation of Wind Speed and Wind Power Density Using Statistical Distribution Approaches and Artificial Neural Network-Based Hybrid Techniques in Çanakkale, Türkiye

In recent years, wind energy has become remarkably popular among renewable energy sources due to its low installation costs and easy maintenance. Having high energy potential is of great importance in the selection of regions where wind energy investments will be made. In this study, the wind power potential in Çanakkale Province, located in the northwest of Türkiye, is examined, and the wind speed is estimated using hourly and daily data over a one-year period. The data, including 12 different meteorological parameters, were taken from the Turkish State Meteorological Service. The two-parameter Weibull and Rayleigh distributions, which are the most widely preferred models in wind energy studies, are employed to estimate the wind power potential using hourly wind speed data. The graphical method is implemented to calculate the shape (k) and scale (c) parameters of the Weibull distribution function. Daily average wind speed estimation is performed with artificial neural network–genetic algorithm (ANN-GA) and ANN–particle swarm optimization (ANN-PSO) hybrid approaches. The proposed hybrid ANN-GA and ANN-PSO algorithms provide correlation coefficient values of 0.94839 and 0.94042, respectively, indicating that the predicted and measured wind speed values are notably close. Statistical error indices reveal that the ANN-GA model outperforms the ANN-PSO model.

An investigation of the effects of meteorological factors on the incidence of tuberculosis

To explore the influence of meteorological factors on the incidence of tuberculosis (TB) in Yingjisha County, Kashgar Region, Xinjiang, and to provide a scientific basis for the prevention and control of TB. The Spearman correlation analysis and distribution lag nonlinear model analysis were conducted on the number of daily reported cases of TB from 2016 to 2023 to study the association effect of various meteorological factors and the daily incidence number of TB in Yingjisha County. A total of 13,288 TB cases were reported from January 2016 to June 2023, and June to October is the peak period of annual TB incidence. Spearman correlation analysis revealed that average daily temperature (AT) and average daily wind speed (WS) were positively correlated with TB incidence (rAT = 0.110, rWS = 0.090); and average daily relative humidity (RH) and TB incidence was negatively correlated (rRH = − 0.093). When AT was − 15 °C, the RR reached a maximum of 2.20 (95% CI: 0.77–6.29) at a lag of 21 days. When RH was 92%, the RR reached a maximum of 1.05 (95% CI: 0.92–1.19) at a lag of 6 days. When WS was 5.2 m/s, the RR reached a maximum of 1.30 (95% CI: 0.78–2.16) at a lag of 16 days. There is a non-linearity and a certain lag between meteorological factors and the occurrence and prevalence of TB in the population, which is mainly manifested in the fact that the risk of incidence of TB decreases with the increase of the daily AT, has a hazardous effect within a certain range of humidity as the average daily RH rises, and gradually increases with the increase of the average daily WS. Local residents are advised to pay attention to climate change so as to take appropriate preventive measures, especially women and middle and old age group should pay close attention to climate change and add more clothes in time, minimise travelling in hazy weather and windy and sandy weather, maintain good nutrition, adequate sleep and moderate exercise in daily life to enhance their immunity, wash hands frequently and ventilate the air, and try to avoid staying in humid and confined spaces in order to reduce the risk of latent TB patients developing the disease.

Discovering ecoacoustic codes in beehives: First evidence and perspectives

The sounds present inside a beehive originate from the overlap of honeybee buzzes with external sounds. They reveal patterns that support the hypothesis that the sonic context of the beehive may be utilized by honeybees as a source of ecoacoustic codes for communication and the coordination of social activity. Patterns were observed in a data series of acoustic files sampled at a frequency of 48 kHz during the period May–July 2023 in a beehive of Apis mellifera ligustica (Spinola, 1806). The acoustic information was extracted using the acoustic complexity index (ACItf) algorithm applied to a fast Fourier transform matrix. Data series, aggregated in 1368 min × 512 frequency bins × 61 days, were tentatively classified according to three temporal classes of aggregation (eight, six, and four clusters, respectively) using the hierarchical K-means clustering algorithm. The clusters obtained at these three resolutions were considered potential ecoacoustic codes (PECs) belonging to each minute of the data series.The number of discontinuities along the 24-h PEC sequence, the coefficient of variation of the number of PECs at daily and seasonal scales, and the PEC sample entropy confirmed a patterned distribution of PECs across the 24 h, modulated at a monthly scale. A significant correlation was found between these indices and the daily average wind speed, and temperature. Honeybee buzz is an informative medium used by honeybees to develop survival strategies.

Analysis of Interannual Anomalies and Causes of Compound Extreme Wind and Precipitation Events in Spring over the Jiangsu–Anhui Region

By using the daily average wind speed and precipitation data of 125 stations in Jiangsu and Anhui Provinces of China from 1961 to 2020 and the monthly NCEP/NCAR reanalysis data, the interannual variation characteristics and its possible reasons of spring compound extreme wind and precipitation events in the Jiangsu–Anhui region were discussed. Results show that the spring compound extreme wind and precipitation events generally present a lesser distribution in the south and more in the north. The events occurring in south (north) of 32° N are basically less than (above) three days, and in some areas of northern Jiangsu, it can reach more than four days. On a regional average, the spring compound extreme wind and precipitation events have presented a significant downward trend in the past 60 years. In addition, there was an interdecadal mutation from more to less in the early 1990s, with the most significant decline in the coastal areas of northern Jiangsu. Further analysis reveals that the synthetic height anomaly field at 500 hPa corresponding to the frequent occurrence of the spring compound extreme wind and precipitation events is positive in the northern region of 45° N, while it is negative in the southern region of 45° N, which enhances the high pressure in high latitudes, increases the meridional gradient of circulation, and promotes the activity of high-latitude short-wave trough ridges and cold air. Meanwhile, a strong southwest airflow exists in the corresponding middle and low latitudes at 850 hPa, so the water vapor from the Bay of Bengal can be continuously transported to the Jiangsu–Anhui region. Overall, the abundant water vapor transportation and the convergence of southward cold air in high latitudes are conducive to the occurrence of extreme wind and precipitation events.

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

Reliability Analysis and Applications of Generalized Type-II Progressively Hybrid Maxwell–Boltzmann Censored Data

Today, the reliability or quality practitioner always aims to shorten testing duration and reduce testing costs without neglecting efficient statistical inference. So, a generalized progressively Type-II hybrid censored mechanism has been developed in which the experimenter prepays for usage of the testing facility for T units of time. This paper investigates the issue of estimating the model parameter, reliability, and hazard rate functions of the Maxwell–Boltzmann distribution in the presence of generalized progressive Type-II hybrid censored data by making use of the likelihood and Bayesian inferential methods. Using an inverse gamma prior distribution, the Bayes estimators of the same unknown parameters with respect to the most commonly squared-error loss are derived. Since the joint likelihood function is produced in complex form, following the Monte-Carlo Markov-chain idea, the Bayes’ point estimators as well as the Bayes credible and highest posterior density intervals cannot be derived analytically, but they may be examined numerically. Via the normal approximation of the acquired maximum likelihood and log-maximum-likelihood estimators, the approximate confidence interval bounds of the unknown quantities are derived. Via comprehensive numerical comparisons, with regard to simulated root mean squared-error, mean relative absolute bias, average confidence length, and coverage probability, the actual behavior of the proposed estimation methodologies is examined. To illustrate how the offered methodologies may be used in real circumstances, two different applications, representing the failure time points of aircraft windscreens as well as the daily average wind speed in Cairo during 2009, are explored. Numerical evaluations recommend utilizing a Bayes model via the Metropolis-Hastings technique to produce samples from the posterior distribution to estimate any parameter of the Maxwell–Boltzmann distribution when collecting data from a generalized progressively Type-II hybrid censored mechanism.

Satellite-Based Estimation of Roughness Length over Vegetated Surfaces and Its Utilization in WRF Simulations

Based on morphological methods, MODIS satellite remote sensing data were used to establish a dataset of the local roughness length (Z0) of vegetation-covered surfaces in Guangdong Province. The local Z0 was used to update the mesoscale Weather Research and Forecasting (WRF) model in order to quantitatively evaluate its impact on the thermodynamic environment of vegetation-covered surfaces. The specific results are as follows: evergreen broad-leaved forests showed the largest average Z0 values at 1.27 m (spring), 1.15 m (summer), 1.03 m (autumn), and 1.15 m (winter); the average Z0 values of mixed forests ranged from 0.90 to 1.20 m; and those for cropland-covered surfaces ranged from 0.17 to 0.20 m. The Z0 values of individual vegetation coverage types all exhibited relatively high values in spring and low values in autumn, and the default Z0 corresponding to specific vegetation-covered surfaces was significantly underestimated in the WRF model. Modifying the default Z0 of surfaces underlying evergreen broad-leaved forests, mixed forests, and croplands in the model induced only relatively small changes (<1%) in their 2 m temperature, relative humidity, skin surface temperature, and the planetary boundary layer height. However, the average daily wind speed of surfaces covered by evergreen broad-leaved forests, mixed forests, and croplands was reduced by 0.48 m/s, 0.43 m/s, and 0.26 m/s, respectively, accounting for changes of 12.0%, 11.1%, and 6.5%, respectively.

Analysis of Wind Power Potential in Samiang Bay, Kotabaru, South Kalimantan

This research was conducted to determine the potential for wind power from the Tamiang Bay area, Kotabaru, South Kalimantan. This study uses data on the average daily wind speed in Tamiang Bay with latitude -4.058883°, longitude 116.050259° obtained from the European Center for Medium-Range Weather Forecasts (ECMWF). Based on the analysis that has been done, the average daily wind speed in Tamiang Bay is 4 m/s for a height of 10 m and 5.98 m/s for a height of 50 m. Through the assumption that using a Gamesa G114-2.5 MW wind turbine with a tower height of 80 m, in one year, the Tamiang Bay area has the potential to produce 2646.58 MWh of wind power. Thus, the Tamiang Bay area is said to be very potential for wind power development.

Influence of Meteorological Parameters on the Urban Heat Island in Moscow

The urban heat island (UHI) intensity in Moscow and the influence of various meteorological parameters are discussed using weather station data. The maximal and average in-space UHI intensities, i.e., a comparison of air temperature T either in the city centre or in the whole urban area together with rural zone have averaged 1.9 and 0.9 °C, respectively, in recent years. The UHI in Moscow has stabilized over the past decade and is not growing. Under conditions of a strong anticyclone, the maximal UHI intensity in space and time reaches 11–12 °C. Low cloudiness and amplitudes of diurnal air temperature, as well as surface temperature, demonstrate the closest relationship with the UHI intensity among other parameters with the correlation coefficient of up to −0.67 for low cloudiness and the maximal UHI intensity. The effect of wind speed, total cloudiness and relative humidity on the UHI is slightly weaker, but still significant. The relationships of all meteorological parameters with the maximal UHI intensity are closer than those with the average one. The multiple correlation coefficient between the maximal UHI intensity and both parameters (low cloudiness and average daily wind speed) is 0.76–0.82. The UHI intensity function of air temperature has a minimum in the range from −4 to 0 °C; its growth both at lower and higher T is due to the influence of anticyclonic weather. The UHI intensity function of wind speed decreases with wind strength. The threshold value at which this function asymptotically approaches its lower limit is 10 m/s in the 40–200 m air layer. The UHI intensity functions of both total and low cloudiness decrease with increasing cloudiness and the differences between them are significant if the cloud cover is more than 50%.

Exploration of influenza incidence prediction model based on meteorological factors in Lanzhou, China, 2014-2017.

Humans are susceptible to influenza. The influenza virus spreads quickly and behave seasonally. The seasonality and spread of influenza are often associated with meteorological factors and have spatio-temporal differences. Based on the influenza cases and daily average meteorological factors in Lanzhou from 2014 to 2017, this study firstly aimed to analyze the characteristics of influenza incidence in Lanzhou and the impact of meteorological factors on influenza activities. Then, SARIMA(X) models for the prediction were established. The influenza cases in Lanzhou from 2014 to 2017 was more male than female, and the younger the age, the higher the susceptibility; the epidemic characteristics showed that there is a peak in winter, a secondary peak in spring, and a trough in summer and autumn. The influenza cases in Lanzhou increased with increasing daily pressure, decreasing precipitation, average relative humidity, hours of sunshine, average daily temperature and average daily wind speed. Low temperature was a significant driving factor for the increase of transmission intensity of seasonal influenza. The SARIMAX (1,0,0)(1,0,1)[12] multivariable model with average temperature has better prediction performance than the university model. This model is helpful to establish an early warning system, and provide important evidence for the development of influenza control policies and public health interventions.

Distribution over time and space of PM2.5 in Beijing- Tianjin- Hebei region and its influence of meteorological factors

As the capital economic belt and the political center of China, Beijing-Tianjin-Hebei region has always been faced with the problem of air pollution. In this study, the emission, seasonal and geographical changes of PM2.5 in this region is analyzed with spatial-temporal distribution. It is found that the concentration of PM2.5 shows a spatial distribution of decreasing from southwest to northeast. In winter, the daily average concentration of PM2.5 is significantly higher than that in other seasons. The influence of meteorological factors on PM2.5 in each city is discussed by cluster analysis, and the correlation between these factors and PM2.5 is studied. The results indicate that the daily average precipitation and wind speed are negatively correlated with PM2.5 concentration, while the daily average relative humidity is positively correlated with it. Finally, this study also puts forward suggestions for PM2.5 control in this area according to the types of cities’ PM2.5 discharge affected by meteorological elements.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.

THE RAINFALL-RUNOFF MODEL FOR THE CARPATHIAN FLYSCH CATCHMENT USING THE SWAT+ HYDROLOGICAL SOFTWARE

Aim of the study: The goal of this study was to apply SWAT+ tools to estimate the water shortage in the Smugawka catchment. Material and methods: The use of the area was documented in the form of an orthophotomap with GIS layers superimposed, which was provided by CORINE Land Cover 2018. A soil and agricultural map, supplemented with lithological content from a detailed geological map, was used as the input layer for QGIS ver. 3.16. In the next stage, multiannual meteorological data (1991–2020) were added to the SWAT+ model, which included: minimum and maximum daily air temperature, daily rainfall, average daily radiation and average daily wind speed. Full integration of the SWAT+ model with QGIS facilitates the procedure of iterative generation and modification of the model input parameters. The calculation of continuous simulations for average long periods of time is presented in the manuscript. Results and conclusions: The application of GIS-based approaches and modeling techniques are crucial in the quest for optimal methods for calculating the water balance today. Our findings also contribute to a better understanding of the hydrological processes at work in the flysch catchment.

Epidemiological Characteristics of Parainfluenza Virus Type 3 and the Effects of Meteorological Factors in Hospitalized Children With Lower Respiratory Tract Infection.

ObjectiveTo investigate the relationship between meteorological factors and Human parainfluenza virus type 3 (HPIV-3) infection among hospitalized children.MethodsAll hospitalized children with acute lower respiratory tract infections were tested for viral pathogens and enrolled, at the second affiliated hospital of Wenzhou medical university, between 2008 and 2017. Meteorological data were directly obtained from Wenzhou Meteorology Bureau's nine weather stations and expressed as the mean exposure for each 10-day segment (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed). The correlation between meteorological factors and the incidence of HPIV-3 was analyzed, with an autoregressive integrated moving average model (ARIMA), generalized additive model (GAM), and least absolute shrinkage and selection operator (LASSO).ResultsA total of 89,898 respiratory specimens were tested with rapid antigen tests, and HPIV-3 was detected in 3,619 children. HPIV-3 was detected year-round, but peak activities occurred most frequently from March to August. The GAM and LASSO-based model had revealed that HPIV-3 activity correlated positively with temperature and rainfall day, but negatively with wind speed. The ARIMA (1,0,0)(0,1,1) model well-matched the observed data, with a steady R2 reaching 0.708 (Ljung-Box Q = 21.178, P = 0.172).ConclusionOur study suggests that temperature, rainfall days, and wind speed have significant impacts on the activity of HPIV-3. GAM, ARIMA, and LASSO-based models can well predict the seasonality of HPIV-3 infection among hospitalized children. Further understanding of its mechanism would help facilitate the monitoring and early warning of HPIV-3 infection.

Modeling landscape wind erosion processes on rangelands using the APEX model

Rangelands provide vital ecosystem services and comprise 36% of U.S. and 25% of world lands. Approximately 20 to 73% of these rangelands are already degraded. Soil erosion driven by winds is a significant cause of this degradation. Minimal work has been done to evaluate the influence of wind erosion on rangeland dominated landscapes or to assess the effect of landscape-scale soil and water conservation and management practices on reducing wind erosion due to its inherent complexity and nonpoint source nature. Our approach is to use an integrated and holistic eco-hydrologic model. The main objective of this study is to integrate process-based landscape wind erosion modeling schemes into the Agricultural Policy/Environmental eXtender (APEX) model (version 1905; APEX1905) for simulating horizontal aeolian sediment transport and vertical particles transport on rangelands. We demonstrate the performance and capability of the model using field data collected at the USDA ARS Jornada Experimental Range in New Mexico, U.S. A benchmark APEX1905 model that runs on high-resolution sub-daily wind speed data and daily average vegetation gap distribution derived from monthly field measurements. The benchmark APEX1905 model captured the variability in observed aeolian sediment transport during 2015–2017 reasonably well with an R2 = 0.58 and a RMSE = 2.60. When the highly variable sub-daily measured wind speed and vegetation gap distribution data were substituted with the daily average wind speed and APEX1905 estimated daily vegetation gap, the APEX1905 reproduced well the benchmark model estimates with the performance statistic of R2 = 0.77 and RMSE=0.54 for the horizontal aeolian sediment transport; and R2 = 0.82 and RMSE = 0.85 for vertical particle transport. Overall, provided limited aeolian watershed data sets, the APEX1905 is demonstrated to be reliable in estimating wind erosion in arid, desert rangeland landscapes using daily average wind speed and simulated vegetation characteristics. Benefiting from the long-established algorithms of simulating plant growth dynamics and topsoil moisture content in APEX, APEX1905 offers a robust and process-based estimation of wind erosion in areas where wind and vegetation data are scarce.

Preliminary Design and Dynamic Response of Multi-Purpose Floating Offshore Wind Turbine Platform: Part 1

Floating offshore wind turbine foundations are based on platforms operated by the oil and gas industry. However, they are designed and optimized to meet the wind turbines’ operating criteria. Although Malaysia is considered a low-wind-speed country, there are some locations facing the South China Sea that are found to be feasible for wind energy harnessing. The average daily wind speed may reach up to 15 m/s. Therefore, designing a cost-effective platform that can operate in Malaysian waters which has less severe environmental conditions compared to the North Sea would be a prudent undertaking. In this study, a new design of a multi-purpose floating offshore wind turbine platform (Mocha-TLP) is presented. In addition, the dynamic response of the platform to wave loads was investigated using the Navier–Stokes code STAR CCM+ developed by CD-adapco. Moreover, free-oscillation tests were performed to determine the natural periods of the platform. Three approaching wave cases and two wave conditions (WC) were considered. The results show that the natural periods of the platforms were within the recommended range for pitch, roll, yaw, heave, sway and surge motions. The platform was stable in rotational motion within the three cases. However, it experienced a noticeable surge motion which was more critical with wave condition one (WC1) since the wavelength equalled the length of the structure. The dynamic response of the platform to wave loads wase minimal and within the operational requirements for wind turbines.

Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil

The meteorological conditions may affect COVID-19 transmission. However, the roles of seasonality and macro-climate are still contentious due to the limited time series for early-stage studies. We studied meteorological factors' effects on COVID-19 transmission in Brazil from February 25 to November 15, 2020. We aimed to explore whether this impact showed seasonal characteristics and spatial variations related to the macro-climate. We applied two-way fixed-effect models to identify the effects of meteorological factors on COVID-19 transmission and used spatial analysis to explore their spatial-temporal characteristics with a relatively long-time span. The results showed that cold, dry and windless conditions aggravated COVID-19 transmission. The daily average temperature, humidity, and wind speed negatively affected the daily new cases. Humidity and temperature played a dominant role in this process. For the time series, the influences of meteorological conditions on COVID-19 had a periodic fluctuation of 3–4 months (in line with the seasons in Brazil). The turning points of this fluctuation occurred at the turn of seasons. Spatially, the negative effects of temperature and humidity on COVID-19 transmission clustered in the northeastern and central parts of Brazil. This is consistent with the range of arid climate types. Overall, the seasonality and similar climate types should be considered to estimate the spatial-temporal COVID-19 patterns. Winter is a critical time to be alert for COVID-19, especially in the northern part of Brazil.