Characterizing the Natural Temperature and Humidity Environment Severity

The possibility of comparing the climatic data of various years with using rank distributions is considered in this paper. As a climatic data, the annual variation of temperature on the spatial areas of meteorological observations with high variability in average temperatures is considered. The results of clustering of the monthly average temperatures values by means of a recurrent neural network were used as the basis of comparing. For a given space of weather observations the rank distribution of the clusters cardinality identified for each year of observation, is being constructed. The resulting rank distributions allow you to compare the spatial temperature distributions of various years. An experimental comparison for rank distributions of the annual variation of monthly average temperatures has confirmed the presence of scatter for various years, associated with different spatio-temporal distribution of temperature. An experimental comparison of rank distributions revealed a difference in the integral annual variation of monthly average temperatures of various years for the Northern Hemisphere.

The identification method in the CurveExpert-1.40 software environment revealed asymmetric wavelets of changes in the average monthly temperature of New Delhi from 1931 to 2021. The maximum increment for 80 years of the average monthly temperature of 5.1°C was in March 2010. An analysis of the wave patterns of the dynamics of the average monthly temperature up to 2110 was carried out. For forecasting, formulas were adopted containing four components, among which the second component is the critical heat wave of India. The first component is the Mandelbrot law (in physics). It shows the natural trend of decreasing temperature. The second component increases according to the critical law. The third component with a correlation coefficient of 0.9522 has an annual fluctuation cycle. The fourth component with a semi-annual cycle shows the influence of vegetation cover. The warming level of 2010 will repeat again in 2035-2040. From 2040 the temperature will rise steadily. June is the hottest month. At the same time, the maximum temperature of 35.1°C in 2010 in June will again reach by 2076. But according to the second component of the heat wave, the temperature will rise from 0.54°C to 16.29°C. The annual and semi-annual cycles had an insignificant effect on the June temperature dynamics. Thus, the identification method on the example of meteorological observations in New Delhi made it possible to obtain summary models containing a different number of components. The temperature at a height of 2 m is insufficient. On the surface, according to space measurements, the temperature reaches 55°C. As a result, in order to identify more accurate asymmetric wavelets for forecasting, the results of satellite measurements of the surface temperature of India at various geographical locations of meteorological stations are additionally required.

PDF HTML阅读 XML下载 导出引用 引用提醒 乌兰巴托-锡林浩特样带草地植被特征与水热因子的关系 DOI: 10.5846/stxb201308092051 作者: 作者单位: 中国科学院地理科学与资源研究所;;中国人民大学;内蒙古草原勘察设计院;内蒙古草原勘察设计院,中国人民大学，中国科学院地理科学与资源研究所,内蒙古草原勘察设计院,中国科学院地理科学与资源研究所,中国科学院地理科学与资源研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 中国科学院知识创新方向性项目(KZCX2-EW-306);国家自然科学基金项目(40971223);科技部973计划(2010CB950904) The relationship between vegetation characteristics and hydro-thermic factors along the Ulanbattar-Xilinhot Grassland Transect of the Mongolian Plateau Author: Affiliation: Institute of Geographic Sciences and Natural Resources Research,CAS;China;;Graduate University of Chinese Academy of Sciences;China;;Inner Mongolia Rangeland Survey Design Institute;China,,,, Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:2012年夏季,研究人员对蒙古高原长约1100km的乌兰巴托-锡林浩特草地样带开展考察,获取了46个样地的物种数量、地上生物量等数据;基于全球GHCN(全球历史气象网络)数据集,提取了样带夏季(6-8月)月均温度和降水总量;继而根据自然地理和行政区边界,将草地样带大致分成北部(蒙古国乌兰巴托-蒙古国艾日格)、中部(蒙古国艾日格-中国苏尼特左旗)和南部(中国苏尼特左旗-中国锡林浩特),开展了分析。研究表明:(1)样带夏季平均温度的空间分布形态呈现明显的倒"U" 型分布,南北两端温度较低,中部温度较高;夏季降水量在空间上的分布形态则与之相反,呈现南北两端降水量较高,中部降水量较低的正"U"型分布;(2)样带上植物物种数量、地上生物量的空间分布形态均呈现正"U" 型分布,即在生态景观类型为典型温性草原的样带南部和北部地区,其生物多样性、地上生物量明显好于呈现为温性荒漠草原、温性荒漠景观的样带中部地区。(3)相关分析体现了大尺度(高原样带尺度)上植被特征与水热环境因子间的关系:植物物种数量、地上生物量与夏季月均温度均呈现负相关,而与夏季降水总量则呈现正相关关系。(4)偏相关分析反映了局地小尺度上植被特征与水热环境因子间的关系:温度和降水要素对于植物物种数量、地上生物量均呈现正相关。 Abstract:During August of 2012, the research team carried out a field exploration of the Ulanbattar-Xilinhot Grassland Transect on the Mongolian Plateau. The transect was split into three parts according to the physical geographical conditions and practical administrative boundaries: the northern section (from Ulanbattar to Airag), the central section (from Airag to Sunitezuoqi), and the southern section (from Sunitezuoqi to Xilinhot). Along the 1100 km transect, 46 sample areas, 136 grass yield quadrats, and about 500 species-frequency plots were surveyed in detail and a series of species number data and above ground biomass data were then measured. Using GIS software and the GHCN datasets (The Global Historical Climatology Network), the historical meteorological data along the above transects were then extracted. The monthly average temperatures and total precipitation during the summer season (i.e., June, July and August) were calculated. Based on the vegetation characteristics data and hydro-thermic factor data, the correlation and partial correlation were analysed. The results show that: (1) the distribution of monthly average temperatures in summer are presented as an inverted "U" pattern along the transect, i.e., the northern and southern sections showing lower temperatures, while the central section maintained a high temperature level. Conversely, the distribution of total precipitation in summer is shown as an upstanding "U" pattern, i.e., high precipitation in the northern and southern sections, with low precipitation in the central section; (2) the distribution of numbers of plant species and above ground biomass are both depicted as an upstanding "U" shape along the transect. In the southern and northern regions, where the ecological type is typically warm steppe, both the bio-diversity and aboveground biomass are significantly better than those in the central region, where the ecological type is usually warm temperate desert grassland or temperate desert; (3) simple correlation analysis normally reflects the relationship between vegetation characteristics and hydro-thermic environmental factors over large scales (for example, plateau transect). In this study, the numbers of plant species and above ground biomass are negatively correlated with the summer monthly average temperature and are positively correlated with the summer precipitation; (4) the partial correlation analysis is inclined to reflect the relationship among different factors over a local scale (for example, small watershed). In this study, both the temperature and precipitation factors have positive effects on the number of plant species and the ground biomass. 参考文献 相似文献 引证文献

Nowadays, to understand regional and global changes in temperature, it is essential to identify and predict that a climate change signal in the climatologically record is a crucial task. Temperature is one of the important meteorological parameters that play a significant role in climate change. The aim is to identify the seasonal patterns in temperature and fit suitable seasonal autoregressive integrated moving average (SARIMA) models to the temperature data of Maharashtra's metropolitan cities viz., Mumbai, Pune and Nagpur. To identify temporal and seasonal monthly average temperature patterns, the data with time span between the years 2013-2023 is considered. Here, variations of the monthly averages temperature are visualized through box plot and violin plot. Further, the time series plots reveal an upward trend across all metropolitan cities. Initially, the SARIMA models are identified on the basis of autocorrelation function (ACF) and partial autocorrelation functions (PACF) plots. On the geographical climatic changes three different optimal models were obtained for: (i) Mumbai city - SARIMA (3, 1, 1) (1, 1, 1)12 model, (ii) Pune-SARIMA (2, 1, 3) (1, 1, 1)12, and (iii) Nagpur-SARIMA (1, 1, 2) (1, 1, 1)12. The model adequacy of these models is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Akaike information criterion (AIC), and the Ljung' Box test statistic of residuals. Finally, the selected models are utilized to predict monthly average temperatures for the next three years.. KEYWORDS :Average temperature, Time series, SARIMA, ACF, PACF.

Qinzhou, Guangxi Zhuang Autonomous Region, China, experiences intense seasonal precipitation and relatively high temperatures, which often lead to droughts or floods. Forecasting precipitation and temperature is an essential step in taking precautions against damages caused by weather. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model is effective for forecasting time series with regular patterns. This paper uses the SARIMA model to forecast the monthly precipitation and average temperature of Qinzhou. The training set comprises data provided by the National Oceanic and Atmospheric Administration (NOAA) from 2010 to 2022, inclusive, while data from 2023 to 2024 are used as the test set. By analyzing the augmented Dickey-Fuller (ADF) test results, and comparing Akaike information criterion (AIC) values and models' accuracy, sets of reasonable model parameters are selected. Coefficients of determination (R2) suggest the SARIMA model can effectively forecast monthly average temperature and precipitation, but it shows shortcomings in capturing unexpected extreme values.

The Qinling Mountains is not only the geographical boundary between North and South China, but also the boundary between subtropical and warm temperate zones. It plays an important role in the geo-ecological pattern of China. However, there is controversy about the specific location of this geographical boundary in academic community due to the complexity, transition and heterogeneity of the transitional zone, as well as the differences in the delimitation indicators and research purposes. To further reveal the characteristics of the North-South transitional zone and clarify the specific location of the geo-ecological boundary between North and South China, combined with SRTM topographic data, temperature and precipitation data, Pinus massoniana forest and Pinus tabulaeformis forest, which represent subtropical coniferous forest in South China and temperate coniferous forest in North China respectively, were chosen to analyze their spatial distributions in the Qinling-Daba Mountains and the climatic conditions at their boundary with the climatic indexes of annual precipitation, the coldest month (January) average temperature, the warmest month (July) average temperature and the annual average temperature. The results show that: (1) Pinus massoniana and Pinus tabulaeformis forests and the climate indicators of their boundary can be used as one of the vegetation-climate indexes for the delimitation of subtropical and warm temperate zones. The boundary between the subtropical coniferous forest (Pinus massoniana forest) and temperate coniferous forest (Pinus tabulaeformis forest) is located along the south slope of Funiu Mountain to the north edge of Hanzhong Basin (the south slope of Qinling Mountains) at an altitude of 1000–1200 m, where the climatic indictors are stable: the annual precipitation is about 750–1000 mm, the annual average temperature is about 12–14°C, the coldest monthly average temperature is 0–4°C, and the warmest monthly average temperature is about 22–26°C. (2) It can be more scientifically to delimitate the boundary of subtropical and warm temperate zones in China by comprehensively considering the vegetation-climate indicators. Additionally, the boundary between subtropical and warm temperate zones in Qinling-Daba Mountains should be a transitional zone consisting of the boundaries of coniferous forests, broad-leaved forests and shrubs between subtropical and warm temperate zones. The results provide a scientific basis for the selection of delimitation index of subtropical and warm temperate zones.

Seasonal Associations Between Foodborne Campylobacter Infections and Ambient Temperature in US, 2010–2019

In this study, we model and forecast monthly average temperature and monthly average precipitation of Turkey by employing periodogram-based time series methodology. We compare autoregressive integrated moving average methodology and harmonic regression. We show that harmonic regression performs better than the classical methodology in both time series. Also, we find that the monthly average temperature and monthly average precipitation have two different periodic structures of 6 months and 12 months which coincide with the seasonal pattern of the time series.

This paper builds on previous self-healing engineered cementitious composites (ECC) research by allowing ECC to heal outdoors, in the natural environment, under random and sometimes extreme environmental conditions. Development of an ECC material that can heal itself in the natural environment could lower infrastructure maintenance costs and allow for more sustainable development in the future by increasing service life and decreasing the amount of resources and energy needed for repairs. Determining to what extent current ECC materials self-heal in the natural environment is the first step in the development of an ECC that can completely heal itself when exposed to everyday environmental conditions. This study monitored outdoor ECC specimens for one year using resonant frequency (RF) and mechanical reloading to determine the rate and extent of self-healing in the natural environment. It was found that the level of RF, stiffness, and first cracking strength recovery increased as the duration of natural environment exposure increased. For specimens that underwent multiple damage cycles, it was found that the level of recovery was highly dependent on the average temperature and amount of precipitation between each damage event. However, RF, stiffness, and first cracking strength recovery data for specimens that underwent multiple loading cycles suggest that self-healing functionality can be maintained under multiple damage events.

The past climate change impact on some natural objects is usually studied using available observations or re-analysis data. To assess future climate changes, the only source of information is simulations of global or regional climate models. A particular application depends on the problem to be studied - if the natural object needs to be considered in more detail, then a smaller spatiotemporal grid step in a model must be used. The present study focuses on the methodology for selecting representative simulations from the EURO-CORDEX ensemble by RCP8.5 scenario and assessing near-future temperature and precipitation trends in Odesa, Ukraine. The approach is to minimize bias in average monthly temperature and precipitation. The 1970–2005 average bias by 76 simulations of the EURO-CORDEX ensemble is about +0.5÷+1.0 °C for the monthly temperature and about –25÷+10 mm for the monthly precipitation sum. The preferably positive bias for precipitation is registered from October to March and a negative one – from April to September with a minimum in July-August. Some models can show almost dry summer, which is not in fact. The procedure for selecting representative simulations involves choosing the minimum bias in the reference period 1970-2005 and adjusting the results obtained for the scenario simulation period 2006-2020. Minimizing the mean bias allowed the selection of the eight simulations that satisfactorily describe changes in temperature and precipitation over the period 1970-2020. These simulations were then used to assess the near-future climate in Odesa. The mean temperature in Odesa has increased by ~1 °C over the past 30 years. A significant trend towards an increase in the average annual temperature will continue in 2021-2050, and the temperature will also increase for all months. There exists only little probability of a negative average monthly temperature and only in January. Although the average annual precipitation sum will increase slightly (~ 20 mm), it will decrease significantly in summer and increase in spring and at the end of the year. In general, it seems that the climate of Odesa is moving towards the Mediterranean climate – warm to hot, dry summers and mild, moderately wet winters.

Actual Evapotranspiration (AET) is a major component of the river basin hydrological cycle over land surface and energy balances. More than 60% of input water on land is returned to the atmosphere through evapotranspiration and it greatly influences the water availability on the land surface. Estimation of AET is an essential part in various fields. Thus, this study mainly aims at assessing the impact of urbanization-associated land use changes on actual evapotranspiration and water balance in the Kelani River Basin, Sri Lanka. Thronthwaite equation and land use conditions are mainly considered in this study to estimate AET. Average monthly temperature data has been obtained from NASA MOD1C3 and data has been validated using observed temperature data processed by the Meteorological Department in Sri Lanka. Potential Evapotranspiration (PET) was calculated using monthly average temperature and fractional vegetation cover was calculated using Landsat images (TM, ETM and OLI) to identify the land use and land cover changes from year 2000 to 2018. Both potential evapotranspiration and fractional vegetation cover are used to estimate AET. TRRM data was used to get Digital Elevation Model (DEM) and Landsat images were used to calculate Normalized Differential Vegetation Index (NDVI), Normalized Differential Building Index (NDBI). All the analysis used in this study have been carried out using raster calculator, zonal statistics and pivot tools in ArcGIS 10.1 software. The study revealed that land use and land cover is a major fact to determine AET. Upper catchment of Kelani River has obtained high AET values due to the vegetation cover and the elevation. Lower part of the catchment is associated with the low values due to build - up areas. AET has decreased by 2018 and urbanization is the main reason for it. Rainy seasons reduce the AET since high humidity and reducing AET can be highlighted when considering the temporal changes of AET in Kelani river basin. Evapotranspiration as a major component of water cycle should be considered because it can be a significant fact to reduce precipitation.

ABSTRACTVector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host–vector interactions is expressed as vectorial capacity—a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.

The climate impact on female acute pyelonephritis in Taiwan: A population-based study

Mythimna separata (Walker) moths captured in light traps were monitored in Luohe, central‐northern China, from 1980 to 2016. Annual average temperature recorded an increase of 0.298°C/10 years in this region in the period. Our results indicate that a rising April and May average temperature and earlier occurrences of days recording the highest day temperature (30°C) caused an advanced peak and increasing proportion of high ovarian development levels of first‐generation females in earlier summers. Results using Johnson's formulation of “oogenesis‐flight syndrome” indicate that increasing sexual maturity proportion has resulted in more emigrant individuals in the local first‐generation moth becoming residents, and then increased individuals rapidly in the local second‐generation moth since 2006. Consequences of this action have a boom in corn damage since 2007 in this region. Advanced peak dates of the first and second‐generation moth revealed the same response to increasing average monthly temperatures in the monitoring period. Increasing temperatures, the average May temperature exceeds or equal to 22°C, during the early 2000's may represent a physiological threshold for M. separata development. Our results suggest that climate warming may impact M. separata migratory status and cause a problem of crop production in this region.

Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.