scholarly journals Nonlinear reconstruction of bioclimatic outdoor-environment dynamics for the Lower Silesia region (SW Poland)

2021 ◽  
Author(s):  
Arkadiusz Głogowski ◽  
Paolo Perona ◽  
Krystyna Bryś ◽  
Tadeusz Bryś
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Nonlinear System Identification ◽  
Climatic Conditions ◽  
Outdoor Environment ◽  
Reconstruction Method ◽  
Climatic Index ◽  
Bioclimatic Conditions

AbstractMeasured meteorological time series are frequently used to obtain information about climate dynamics. We use time series analysis and nonlinear system identification methods in order to assess outdoor-environment bioclimatic conditions starting from the analysis of long historical meteorological data records. We investigate and model the stochastic and deterministic properties of 117 years (1891–2007) of monthly measurements of air temperature, precipitation and sunshine duration by separating their slow and fast components of the dynamics. In particular, we reconstruct the trend behaviour at long terms by modelling its dynamics via a phase space dynamical systems approach. The long-term reconstruction method reveals that an underlying dynamical system would drive the trend behaviour of the meteorological variables and in turn of the calculated Universal Thermal Climatic Index (UTCI), as representative of bioclimatic conditions. At longer terms, the system would slowly be attracted to a limit cycle characterized by 50–60 years cycle fluctuations that is reminiscent of the Atlantic Multidecadal Oscillation (AMO). Because of lack of information about long historical wind speed data we performed a sensitivity analysis of the UTCI to three constant wind speed scenarios (i.e. 0.5, 1 and 5 m/s). This methodology may be transferred to model bioclimatic conditions of nearby regions lacking of measured data but experiencing similar climatic conditions.

scholarly journals Analysis of Ozone Pollution Characteristics and Influencing Factors in Northeast Economic Cooperation Region, China

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 843
Author(s):  
Jiaqi Tian ◽  
Chunsheng Fang ◽  
Jiaxin Qiu ◽  
Ju Wang
Keyword(s):  
Wind Speed ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Economic Cooperation ◽  
Ozone Pollution ◽  
Temporal And Spatial Variation ◽  
Temporal And Spatial ◽  
Pm2.5 And Pm10 ◽  
Peak Pattern ◽  
Pm2.5 Concentration

The increase in tropospheric ozone (O3) concentration has become one of the factors restricting urban development. This paper selected the important economic cooperation areas in Northeast China as the research object and collected the hourly monitoring data of pollutants and meteorological data in 11 cities from 1 January 2015 to 31 December 2019. The temporal and spatial variation trend of O3 concentration and the effects of meteorological factors and other pollutants, including CO (carbon monoxide), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), and PM2.5 and PM10 (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) on ozone concentration were analyzed. At the same time, the variation period of O3 concentration was further analyzed by Morlet wavelet analysis. The results showed that the O3 pollution in the study area had a significant spatial correlation. The spatial distribution showed that the O3 concentration was relatively high in the south and low in the northeast. Seasonally, the O3 concentration was the highest in spring, followed by summer, and the lowest in winter. The diurnal variation of O3 concentration presented a “single peak” pattern. O3 concentration had a significant positive correlation with temperature, sunshine duration, and wind speed and a significant anticorrelation with CO, NO2, SO2, and PM2.5 concentration. Under the time scale of a = 9, 23, O3 had significant periodic fluctuation, which was similar to those of wind speed and temperature.

scholarly journals Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

2018 ◽  
Vol 22 (6) ◽  
pp. 900-906 ◽  
Author(s):  
Wonjik Kim ◽  
Osamu Hasegawa ◽  
◽  
◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Atmospheric Pressure ◽  
Time Series Data ◽  
Meteorological Data ◽  
Series Data ◽  
Total Solar Radiation ◽  
Proposed Model ◽  
Self Organizing

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

scholarly journals Towards an Adaptation of Efficient Passive Design for Thermal Comfort Buildings

2021 ◽  
Vol 13 (17) ◽  
pp. 9570
Author(s):  
Ghada Elshafei ◽  
Silvia Vilcekova ◽  
Martina Zelenakova ◽  
Abdelazim M. Negm
Keyword(s):  
Wind Speed ◽  
Thermal Comfort ◽  
Natural Ventilation ◽  
Meteorological Data ◽  
Green Building ◽  
Climatic Conditions ◽  
Indoor Climate ◽  
Indoor Thermal Comfort ◽  
Passive Design ◽  
Different Shapes

This paper discusses the effect of various climatic conditions that pertain to passive design measurements and their relationships with building configurations to improve indoor thermal comfort based on the different climate zones in Egypt to support Egypt’s sustainability agenda 2030. We find the most appropriate design settings that can increase the indoor thermal comfort, such as building orientation and shape. These settings can be modeled using DesignBuilder software combined with Egyptian meteorological data. This software is used accompanied by computational fluid dynamics to numerically assess the outcomes of different changes, by simulating indoor climate condition factors such as wind speed and temperature. Natural ventilation simulations were performed for four different shapes to create comprehensive dataset scenarios covering a general range of shapes and orientations. Seven scenarios were optimized to put forward a series of building bioclimatic design approaches for the different characteristic regions. The results indicated that the temperature decreased by about 3.2%, and the air velocity increased within the study domain by 200% in the best and the worst cases, respectively, of the four different shapes. The results of the study gave evidence that the configuration of buildings, direction, and wind speed are very important factors for defining the natural ventilation within these domains to support the green building concept and the sustainable design for a better lifestyle.

scholarly journals Spatial and temporal variation of reference evapotranspiration under climate change: a case study in the Sanjiang Plain, Northeast China

2017 ◽  
Vol 49 (1) ◽  
pp. 251-265 ◽  
Author(s):  
Xinyi Song ◽  
Kui Zhu ◽  
Fan Lu ◽  
Weihua Xiao
Keyword(s):  
Sensitivity Analysis ◽  
Wind Speed ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Negative Influence ◽  
Sanjiang Plain ◽  
Spatial And Temporal Variation ◽  
The Sanjiang Plain ◽  
Mountainous Regions

Abstract It is essential to understand the changing patterns in reference evapotranspiration (ET0) and its relation to climate variables. In this study, meteorological data obtained from the Sanjiang Plain (SJP) between 1959 and 2013 are used to calculate ET0 via the Penman–Monteith method. This study analyses the spatial and temporal changes of ET0 and determines which meteorological variables have an impact on this. The Mann–Kendall test, moving t-test, sensitivity analysis and simulated results have been used to conduct these analyses. The results demonstrate the following. (1) Spatially, there is an increasing trend in the annual ET0 values in agricultural areas. However, significant decreasing trends (P < 0.05) can be found in mountainous regions. (2) Temporally, two abrupt changes can be detected in the early 1980s and the late 1990s for the entire SJP, leading to large inter-annual differences. (3) Sensitivity analysis shows that relative humidity (RH) is the most sensitive climate variable and has a negative influence on ET0, followed by temperature, sunshine duration and wind speed, all of which exert positive impacts. (4) The simulated result shows that ET0 is most sensitive to RH. However, significant reductions in wind speed can exert large influences on the ET0 values.

Variation Characteristics of PM10 Mass Concentrations and Correlations with Meteorological Factors in Xingtai City

2013 ◽  
Vol 807-809 ◽  
pp. 73-76
Author(s):  
Jian Wang ◽  
Mei Xu ◽  
Xia Ye ◽  
Wei Liu
Keyword(s):  
Wind Speed ◽  
Vapor Pressure ◽  
Negative Correlation ◽  
Mass Concentration ◽  
Meteorological Factors ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Air Pressure ◽  
Positive Correlation ◽  
Variation Characteristics

Based on monitoring data of air pollution index (API) and meteorological data from January 2009 to December 2012 in Xingtai, the variation characteristics of PM10mass concentration were analyzed and the relationships between PM10mass concentration and air pressure, wind speed, temperature, vapor pressure, relative humid and sunshine duration were investigated for four seasons using SPSS software. The results showed that the PM10mass concentration was 86.5, 83.3, 85.0 and 80.4 μg m­3, and the differences were not obvious tending to be a weak downward trend from 2009 to 2012. The percentage of the excellent and good air quality was high during the period and the mild and light pollution mainly appeared in December. The PM10mass concentration showed significant seasonal variations with a higher value in winter and fall and a lower value in spring and summer. The relationship between PM10mass concentration and meteorological factors showed some differences in different seasons. The PM10mass concentration had negative correlation with air pressure and sunshine duration and positive correlation with vapor pressure, temperature and wind speed in spring. The PM10mass concentration was negatively correlated with air pressure and sunshine duration, but positively correlated with vapor pressure and temperature in summer. The PM10mass concentration was negatively correlated with temperature, wind speed, vapor pressure and sunshine duration in fall. The PM10mass concentration had negative correlation with wind speed, sunshine duration and air pressure and positive correlation with vapor pressure and relative humidity in winter.

scholarly journals The Impact of Heat Change Intensity on the Prevalence of Work-Related Accidents in Isfahan Steel Industry; A Time-Series Approach

2020 ◽  
Author(s):  
Hadi Alimoradi ◽  
Mahsa Nazari ◽  
Mohammad Javad Zare Sakhvidi
Keyword(s):  
Time Series ◽  
Heat Stress ◽  
Wind Speed ◽  
Steel Industry ◽  
Thermal Stresses ◽  
Meteorological Data ◽  
Daily Basis ◽  
Work Related ◽  
Change Intensity ◽  
The Impact

In the steel industries, workers are exposed to heat and ambient thermal stresses on a daily basis, leading to discomfort and limited performance. In this study, the main purpose is to investigate the effect of climate heat stress on the rate of accidents in the workplace for workers for 5 consecutive years. The data of this study were received without any sampling through the HSE Center for Steel Industry and meteorological data from 2015 to 2019 from Isfahan Meteorological station. The daily number of casualties among workers in the steel industry during 2015-2019 by adjusting seasonal patterns, months, effects of the day of the week and other meteorological factors on the average daily temperature using the studied model has a decreasing effect. Eviews software (version 8) was used to model and investigate the relationship between events and meteorological variables. The mean temperature was at least 40.2-2 and at most 70.34 ° C, respectively. In the time-series study for the main model, the number of accidents shows a direct relationship with the average temperature and wind speed. Climatic indices of humidity and rainfall have the least impact on accidents compared to temperature and wind speed. A strong correlation was shown between the increase in average ambient temperature and the rate of accidents over the past 5 years. Given the fundamental differences in studies of environmental exposure and wind speed over heat stress, further analysis in workers should be considered.

scholarly journals Evaluation of FAO-56 Procedures for Estimating Reference Evapotranspiration Using Missing Climatic Data for a Brazilian Tropical Savanna

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1763
Author(s):  
Luiz Claudio Galvão do Valle Júnior ◽  
George L. Vourlitis ◽  
Leone Francisco Amorim Curado ◽  
Rafael da Silva Palácios ◽  
José de S. Nogueira ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Water Resource Management ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Climatic Conditions ◽  
Climatic Variables ◽  
Climatic Data ◽  
Wet Season ◽  
Radiation Data

The Brazilian savanna (Cerrado) has been heavily impacted by agricultural activities over the last four to five decades, and reliable estimates of reference evapotranspiration (ETo) are needed for water resource management and irrigation agriculture. The Penman–Monteith (PM) is one of the most accepted models for ETo estimation, but it requires many inputs that are not commonly available. Therefore, assessing the FAO guidelines to compute ETo when meteorological data are missing could lead to a better understanding of which variables are critically important for reliable estimates of ETo and how climatic variables are related to water requirements and atmospheric demands. In this study, ETo was computed for a grass-dominated part of the Cerrado from April 2010 to August 2019. We tested 12 different scenarios considering radiation, relative humidity, and/or wind speed as missing climatic data using guidelines given by the FAO. Our results presented that wind speed and actual vapor pressure do not affect ETo estimates as much as the other climatic variables; therefore, in the Cerrado’s conditions, wind speed and relative humidity measurements are less required than temperature and radiation data. When radiation data were missing, the computed ETo was overestimated compared to the benchmark. FAO procedures to estimate the net radiation presented good results during the wet season; however, during the dry season, their results were overestimated because the method could not estimate negative Rn. Our results indicate that radiation data have the highest impact on ETo for our study area and presumably for regions with similar climatic conditions. In addition, those FAO procedures for estimating radiation are not suitable when radiation data are missing.

scholarly journals Forest cover trend analysis using MODIS time series and its climatic responses in the Mari El Republic of Russia

2021 ◽  
Vol 932 (1) ◽  
pp. 012003
Author(s):  
E A Kurbanov ◽  
O N Vorobev ◽  
S A Lezhnin ◽  
D M Dergunov ◽  
Y Wang
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Cover ◽  
Meteorological Data ◽  
Climatic Conditions ◽  
Data Series ◽  
Modis Ndvi ◽  
Climatic Responses ◽  
Phenological Metrics

Abstract This study assesses whether MODIS NDVI satellite data time series can be used to detect changes in forest phenology over the different forest types of the Mari El Republic of Russia. Due to the severe climatic conditions, coniferous and deciduous forests of this region are especially vulnerable to climate change, which can lead to stresses from droughts and increase the frequency of wild fires in the long term. Time series analysis was applied to 16-day composite MODIS (MOD13Q1) (250 m) satellite data records (2000-2020) for the investigated territory, based on understanding that the NDVI trend vectors would enable detection of phenological changes in forest cover. There was also the determination of land cover/land use change for the area and examination of meteorological data for the investigated period. For the study, we utilized four phenological metrics: start of season (SOS), end of season (EOS), length of season (LOS), and Maximum vegetation index (MVI). The NDVI MODIS data series were smoothed in the TimeSAT software using the Savitsky-Golay filter. The results of the study show that over the 20-years period variations in phenological metrics do not have a significant impact on the productivity and growth of forest ecosystems in the Mari El Republic.

scholarly journals Comparing The Models SARIMA, ANFIS And ANFIS-DE In Forecasting Monthly Evapotranspiration Rates Under Heterogeneous Climatic Conditions

2021 ◽  
Author(s):  
Pouya Aghelpour ◽  
Vahid Varshavian ◽  
Zahra Hamedi
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Air Temperature ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Climate Type ◽  
Inference System ◽  
Semi Arid ◽  
Different Climates

Abstract Reference crop evapotranspiration (ET0) is one of the most important hydro-climatological components which directly affects agricultural productions, and its forecasting is critical for water managers and irrigation planners. In this study, adaptive neuro-fuzzy inference system (ANFIS) model has been hybridized by differential evolution (DE) optimization algorithm as a novel approach to forecast monthly ET0. Furthermore, this model has been compared with the classic stochastic time series model. For this, the ET0 rates were calculated on monthly scale during 1995-2018, based on FAO-56 Penman-Monteith equation and meteorological data including: minimum air temperature, maximum air temperature, mean air temperature, minimum relative humidity, maximum relative humidity & sunshine duration. The investigation was performed on 6 stations in different climates of Iran, including: Bandar Anzali & Ramsar (per-humid), Gharakhil (sub-humid), Shiraz (semi-arid), Ahwaz (arid) and Yazd (extra-arid). The models’ performances were evaluated by the criteria percent bias (PB), root mean squared error (RMSE), normalized RMSE (NRMSE) and Nash-Sutcliff (NS) coefficient. Surveys confirm the high capability of the hybrid ANFIS-DE model in monthly ET0 forecasting; so that the DE algorithm was able to improve the accuracy of ANFIS, by 16% on average. Seasonal autoregressive integrated moving average (SARIMA) was the most suitable pattern among the time series stochastic models, and superior compared to its other competitors. Consequently, due to the simplicity and parsimony, the SARIMA was suggested more appropriate for monthly ET0 forecasting in all the climates. Comparison between the different climates confirmed that the climate type significantly affects the forecasting accuracies: it’s revealed that all the models work better in extra-arid, arid and semi-arid climates, than the humid and per-humid areas.

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