Daily Average Wind Research Articles

Machine Learning Methods Based on Limited Meteorological Data to Simulate Potential Evapotranspiration: A Case Study of Source Region of Yellow River Basin

ABSTRACTSimulation of potential evapotranspiration (PET) is an important part of drought warning and water resource planning. However, the commonly used empirical models need to input a large number of meteorological elements. Therefore, to improve the efficiency and accuracy of PET simulation in areas lacking meteorological data, this study evaluated the performance of Extreme learning machine (ELM), multi‐layer perceptron (MLP), and Random Forest (RF) three machine learning methods to simulate daily PET using limited meteorological data in the source region of the Yellow River (SYRB). Two bionic optimization algorithms, Grey Wolf Optimizer (GWO) and Sparrow Search Algorithm (SSA), were used to optimise the hyperparameters of the model to improve the accuracy of the model. In addition, the effect of months on daily PET simulations was evaluated. The results showed that the daily maximum temperature (Tmax) was the most important factor affecting the PET simulation, and the daily average relative humidity (RH) and wind speed (U10) were the secondary factors. It is recommended to use Tmax, RH, U10, and sunshine duration as the optimum combination of input (R2 > 0.95). In the case of limited meteorological data, the input combination of Tmax, RH, U10, or Tmax, RH (R2 > 0.75) was considered. Considering the accuracy and the time and space overhead of the model, the ELM‐GWO model is recommended. When month information was used as an input factor, model performance improved in all scenarios, and June to July was the most accurate month for the model to simulate daily PET. This research resultwill allow researchers to choose the appropriate meteorological factor when simulating the PET to provide the reference.

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.

Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia

Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.

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.

Ubiquitous Sea Surface Temperature Anomalies Increase Spatial Heterogeneity of Trade Wind Cloudiness on Daily Time Scale

Abstract The impact of weak submeso- to mesoscale SST anomalies on daily averaged trade cumulus cloudiness is investigated using satellite observations that have been validated against shipboard measurements from the Atlantic Tradewind Ocean–Atmosphere Mesoscale Interaction Campaign (ATOMIC). Daily spatial SST anomalies are identified from GOES–POES Blended SST analysis within a 10° × 10° region during January and February 2020. Daily averaged cloud fraction and 10-m neutral wind from satellite observations and reanalysis are composited over the identified SST features, using a common coordinate system based on the near-surface background wind directions. Composites of satellite cloud fraction show a statistically significant increase of cloudiness over the SST warm core with a reduction of cloudiness away from it. These responses are largely the same but with opposite signs over SST cold anomalies, suggesting that spatial heterogeneity in SST can locally imprint on daily cloud fraction. Composites of daily 10-m wind speed and wind convergence anomalies from both satellite and reanalysis show that surface wind speed is increased over SST warm anomalies, implying enhanced turbulence over warmer SSTs. Correspondingly, the surface convergence anomalies in these composites are located around the maximum downwind SST gradient, offset downwind from the cloudiness anomalies. These results indicate that the response of daily cloudiness to these SST anomalies is more likely generated by spatial variability of surface-driven turbulence and surface fluxes rather than that of surface or boundary layer convergence.

Sea-land breezes in the Guangdong- Hong Kong- Macau Greater Bay Area coastal zone from 2013 to 2022

Sea-land breezes (SLBs11SLB, sea-land breeze; GBA, Greater Bay Area) are mesoscale circulation phenomena that significantly affect weather and air quality in coastal regions. The Guangdong–Hong Kong–Macau Greater Bay Area (GBA), one of the most densely populated and economically developed regions in China, experiences frequent SLB intrusion. In this study, we examined the SLB features in the GBA from 2013 to 2022 based on observations, focusing particularly on the influence of synoptic conditions on SLB behaviour and their interplay. The annual average daily sea and land wind speeds ranged from 2.8 to 3.2 m/s and 1.9 to 2.2 m/s, and from 1.8 to 2.2 m/s and 1.6 to 1.9 m/s in the eastern and western GBA, respectively. The start time of sea (land) winds first appeared at 0000 (1100) UTC and finally occurred at 0900 (2000) UTC. Climatologically, SLBs were observed more frequently when southerly synoptic winds prevailed in the GBA. SLBs were stronger under synoptic northerly winds, but weaker when synoptic southerly winds prevailed. The diurnal evolution amplitude of SLB strength was much more significant under the control of synoptic northerly winds. In addition, SLBs modified synoptic winds, leading to the distortion of the observed diurnal wind patterns. A southerly increment in synoptic winds was observed from noon to early night on SLB days, whereas a northerly increment was observed from midnight to morning. Northerly synoptic winds weakened or even transformed into southerly winds in the afternoon, whereas southerly synoptic winds were found to decrease or even turn northerly at night. Our study provides an integrated understanding of SLBs in the GBA, especially regarding the behaviour of SLBs under various synoptic conditions and their modification on synoptic winds that reshape the observed wind patterns. These results provide useful information for weather simulations in coastal areas.

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.

Comparison of deterministic wind speed generation models for wind erosion modeling

The diurnal patterns of hourly wind speed data are fundamentally important in precise wind erosion modeling. Several deterministic wind speed simulation models, including the Guo (1/π), Guo (1/2), wind generator (WINDGEN), Ephrath, Debele, and Goudriaan models, have been developed to quantitatively generate hourly wind speed data. In this study, the performances of these popular deterministic wind speed generation models were investigated using observed hourly wind speed data obtained from 191 climate stations in arid and semi-arid areas of China. The performances of the Guo (1/π), Guo (1/2) and WINDGEN models were the best for hourly wind speed generation. Linear regression analysis revealed that all models tended to underestimate wind speeds. The patterns of diurnal variation and the amplitude of the measured hourly wind speed data generally governed the model performance. The performances of the Guo (1/π), WINDGEN, Guo (1/2), Goudriaan, Debele, and Ephrath models successively decreased for the daily average erosive wind power density (AWPD) estimation. To improve the Guo (1/π) and Guo (1/2) models for AWPD evaluation, the calibrated parameters (π and 2) of the second term in the Guo (1/π) and Guo (1/2) models showed different values for each station. The accuracy of the AWPD obtained by the Guo (1/π) and Guo (1/2) models with the calibrated parameters were significantly improved, which could further influence the accuracy of wind erosion modeling. More studies are required to investigate the relationship between the calibrated parameters in the Guo (1/π) and Guo (1/2) models and geographic conditions linked with wind erosion.

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.

Sap flow of sweet cherry reveals distinct effects of humidity and wind under rain covered and netted protected cropping systems

Protected cropping systems (PCS) alter the plant growing environment, though understanding of this in ventilated systems and how the new climate affects tree water uptake is limited. Sap flow sensors and weather stations were deployed in 16-year-old ‘Lapins’ on ‘Colt’ rootstock cherry trees under a ventilated Voen PCS and in an adjacent bird netted PCS. Average and maximum temperatures were consistently higher (14.7 °C and 22.9 °C) while total daily solar radiation and average wind were consistently lower (12.9 MJ/m2 and 0.2 m/s) in rain covered, in contrast to netted, PCS (13.9 °C, 21.3 °C, 13.7 MJ/m2 and 0.9 m/s). Over the season, a threefold lower daily sap flow rate was observed under rain covered PCS. Using generalised additive modelling (GAM), the influence of individual climate parameters on sap flow were predicted. Whilst sap flow was only slightly affected by relative humidity (RH) less than 60%, above this threshold sap flow rapidly declined under rain covered PCS whereas sap flow more gradually declined above 20% RH under netted PCS. Overall, our novel modelling approach led to the discovery of the 60% RH critical threshold on predicted sap flow and the indirect effect that wind speeds have on sap flow under PCS.