Dynamic thermo-mechanical responses of road-soft ground system under vehicle load and daily temperature variation

A complete road-soft ground model is established in this paper to study the dynamic responses caused by vehicle loads and/or daily temperature variation.A dynamic thermo-elastic model is applied to capturing the behavior of the rigid pavement,the base course,and the subgrade,while the soft ground is characterized using a dynamic thermo-poroelastic model.Solutions to the road-soft ground system are derived in the Laplace-Hankel transform domain.The time domain solutions are obtained using an integration approach.The temperature,thermal stress,pore water pressure,and displacement responses caused by the vehicle load and the daily temperature variation are presented.Results show that obvious temperature change mainly exists within 0.3 m of the road when subjected to the daily temperature variation,whereas the stress responses can still be found in deeper places because of the thermal swelling/shrinkage deformation within the upper road structures.Moreover,it is important to consider the coupling effects of the vehicle load and the daily temperature variation when calculating the dynamic responses inside the road-soft ground system.

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series (1960-2006)

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata （e.g., probable dates of break points） are not always available. To examine the inffuence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization （MASH） method to the Beijing （BJ） daily temperature series for 1960- 2006 in three cases with different references： （1） 13M-considering metadata at BJ and 12 nearby stations; （2） 13NOM-considering the same 13 stations without metadata; and （3） 21NOM-considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71~0C, -0.79~0C, and -0.5~0C for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365~0C （10 yr）^（-1） for the three cases, respectively, smaller than the estimate of 0.453~0C （10 yr）^（-1） in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.

Homogenized Daily Mean/Maximum/Minimum Temperature Series for China from 1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.

The efficacy of Kriging spatial interpolation for filling temporal gaps in daily air temperature data: A case study in northeast China

Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps （i.e., missing data） are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging （UK） and Kriging with an external drift （KED）, as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients （R2） between the observed and estimated daily series were 〉0.98, and root mean square errors （RMSEs） of the estimated daily mean （Tmean）, maximum （Tmax）, and minimum （Tmin） of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.

The relation between ambulance transports due to heat stroke and air temperature using daily data in Okayama prefecture, Japan

The aim of this study was to investigate the link between ambulance transports due to heat stroke and air temperature by using daily data of ambulance transports in Okayama prefecture, Japan. Daily observations for ambulance transports due to heat stroke from July to September in 2010 in Okayama prefecture, Japan were obtained from Fire and Disaster Management Agency in Japan. Data of meteorological parameters in Okayama prefecture, Japan were also obtained from Japan Meteorological Agency. Effect of meteorological parameters on ambulance transports due to heat stroke was analyzed. A total of 1133 ambulance transports due to heat stroke were observed in from July to September of 2010 in Okayama prefecture, Japan. Ambulance transports due to heat stroke was significantly correlated with air temperature. In addition, number of subjects with ambulance transports due to heat stroke over 34°C in the highest air temperature was 21.2 ± 9.8 per day. Higher air temperature was closely associated with higher ambulance transports due to heat stroke by using daily data in Okayama, prefecture, Japan.

Study on Refined Forecast Method of Daily Maximum Temperature in Wugang City from July to September

[Objective] The aim was to study the refined forecast method of daily highest temperature in Wugang City from July to September. IM[ethod] By dint of ECMWF mode product and T231 in 2009 and 2010 and daily maximum temperature in the station in corresponding period, multi-factors similar forecast method to select forecast sample, multivariate regression multi-mode integration MOS method, after dynamic corrected mode error and regression error, dynamic forecast equation was concluded to formulate the daily maximum temperature forecast in 24 -120 h in Wugang City from July to September. [ Result] Through selection, error correction, the daily maximum temperature equation in Wugang City from July to September was concluded. Through multiple random sampling, F test was made to pass test with significant test of 0.1. [ Conclusionl The method integrated domestic and foreign forecast mode, made full use of useful information of many modes, absorbed each others advantages, con- sidered local regional environment, lessen mode and regression error, and improved forecast accuracy.

Diurnal Temperature Range and Daily Emergency Room Admissions among the Elderly in Beijing,China

In recent years, more attentions have been paid to the association between climate change and human health. Increasing and more variable global surface temperature is one of the key climatic change factors which have been consistently reported about the effect on human health. So far, more researches have revealed that temperature lead not only to direct deaths and illnesses but also to aggravation of cardiovascular and respiratory diseases. Typically, the relationship between temperature and mortality or morbidity is V-, U-, or J- shaped, with optimum temperature corresponding to the lowest point in the temperature mortality curve.

The Highest and Lowest Temperature Changes in Dongling in Shenyang from 1951 to 2008

[Objective] The aim was to study the characteristics of climate changes of highest and lowest temperature in Dongling District in Shenyang Province in recent 58 years.[Method] By dint of the highest temperature and lowest temperature in the meteorological observation station in Dongling District in Shenyang from 1951 to 2008,and through statistical method such as climate tendency rate and sequence relevance,the interannual trend changes of annual and seasonal average highest and lowest temperature were expounded.[Result] In recent 58 years,the annual and seasonal average highest and lowest temperature in Dongling District in Shenyang were increasing and the changes rate of average lowest temperatures (0.262 ℃/10 a) were larger than the rate of average highest temperature (0.187 ℃/10 a).The abrupt changes period was one era earlier than average highest temperature.The annual average highest temperature increased from 1980s and it reached historical new record in late 1990s;while annual lowest temperature stared from 1970s and reached historical new high in 1980s.The average highest temperature and lowest temperature increased most distinctly in winter,followed by spring and was weakest in summer.The differences of annual and seasonal average temperatures were declining and the significance level was low.[Conclusion] The study provided theoretical basis for the development and utilization of climate resources in Shenyang.

Change in summer daily precipitation and its relation with air temperature in Northwest China during 1957–2016

On the basis of the summer daily-precipitation meteorological data collected from weather stations across Northwest China from 1957 to 2016, this study evaluated the trends in 12-daily precipitation indices in the summer season and their relations with air temperature. Precipitation-event intensity, which was averaged over the total study area, increased in recent decades although the total precipitation continuously decreased. In particular, intensity generally decreased in the northern and eastern parts and increased in the southern and western parts of the study area. None of the 12 precipitation indices was significantly correlated with temperature in Xinjiang; R95 N(number of events with precipitation greater than the long-term95 th percentile), RX1 day(greatest 1-day total precipitation), PI(simple daily intensity), and R10(number of heavy-precipitation days) were significantly and positively correlated with temperature in Qinghai–Gansu. However, low correlation coefficients were observed. In the Loess Plateau, P(total precipitation), WS(maximum number of consecutive wet days),R95 N, and WD(number of wet days) were significantly and negatively correlated with temperature, whereas Gini(gini concentration index) and DS(maximum number of consecutive dry days) were significantly and positively correlated with temperature. Results of the study suggested that climate shift was evident in terms of daily precipitation, and the study area faced new challenges involving precipitation-event intensity increasing in the southwestern part and unevenly dispersing in the northwest.

Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Homogenization of the Daily Land Surface Temperature over the Mainland of China from 1960 through 2017

Land surface temperature(LST)is one of the most important factors in the land-atmosphere interaction process.Raw measured LSTs may contain biases due to instrument replacement,changes in recording procedures,and other non-climatic factors.This study attempts to reduce the above biases in raw daily measurements and achieves a homogenized daily LST dataset over China using 2360 stations from 1960 through 2017.The high-quality land surface air temperature(LSAT)dataset is used to correct the LST warming biases especially evident during cold months in regions north of 40ºN due to the replacement of observation instruments around 2004.Subsequently,the Multiple Analysis of Series for Homogenization(MASH)method is adopted to detect and then adjust the daily observed LST records.In total,3.68×10^(3) effective breakpoints in 1.65×106 monthly records(about 20%)are detected.A large number of these effective breakpoints are located over large parts of the Sichuan Basin and southern China.After the MASH procedure,LSTs at more than 80%of the breakpoints are adjusted within+/-0.5℃,and of the remaining breakpoints,only 10%are adjusted over 1.5℃.Compared to the raw LST dataset over the whole domain,the homogenization significantly reduces the mean LST magnitude and its interannual variability as well as its linear trend at most stations.Finally,we perform preliminary analysis upon the homogenized LST and find that the annual mean LST averaged across China shows a significant warming trend[0.22℃(10 yr)^(-1)].The homogenized LST dataset can be further adapted for a variety of applications(e.g.,model evaluation and extreme event characterization).

On the Relationship between Duration of Vegetative Growth Stage of Wheat and the Average Daily Temperature

Using the modern information technology,this paper analyzes the 20 years of experimental observation data of wheat ear differentiation research team led by Professor Cui Jinmei.It reveals that in the appropriate sowing period,there is a quartic polynomial regression relationship between the sowing period and spike primordium period,namely between duration of vegetative growth stage and the average daily temperature.It is of great significance to determining the suitable sowing period of wheat.

Quantification of relationship between annual daily maximum temperature and annual daily maximum rainfall in South Australia

The empirical relationship between annual daily maximum temperature(ADMT)and annual daily maximum rainfall(ADMR)was investigated.The data were collected from four weather stations located in Adelaide,South Australia,from 1988 to 2017.Due to the influence of sea surface temperature on rainfall and temperature,the distance from the weather station to the sea was considered in the selection of weather stations.Two weather stations near the sea and two inland weather stations were selected.Three non-parametric statistical tests(Kruskal–Wallis,Mann–Whitney,and correlation)were applied to perform statistical analysis on the ADMT and ADMR data.It was revealed that the temperature and rainfall in South Australia varies according to weather station location.The distance from the sea to the weather station was found to have limited influence on temperature and rainfall.Meanwhile,with the 0.05 level of significance,the association between ADMT and ADMR near sea stations is not as significant as the association between the two inland weather stations.It is relatively unrealistic to use ADMR to predict ADMT,or vice versa,since their correlation is not statistically significant(Spearman’s rank correlation coefficient:−0.106).

Multi-Fractal Analysis of Daily Air Temperature Time Series in Coastal Areas of China

In this article,the Multi-Fractal Detrended Fluctuation Analysis(MF-DFA)method is adopted to study the temperature,i.e.,the maximum temperature(Tmax),mean temperature(Tavg)and minimum(Tmin)air temperature,multifractal characteristics and their formation mechanism,in the typical temperature zones in the coastal regions in Guangdong,Jiangsu and Liaoning Provinces.Following are some terms and concepts used in the present study.Multifractality is defined as a term that characterizes the complexity and self-similarity of objects,and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution.Fractality strength denotes the fluctuation range of the data set,and long-range correlation(LRC)measures the stability of the climate system and the trend of climate change in the future.In this research,it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences.Furthermore,the research also proves that the Tavg,Tmaxand Tminof the above three provinces are highly multifractal.The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning>temperature series multifractality of Guangdong>temperature series multifractality of Jiangsu,and the corresponding long-range correlations follow the same order.It reveals that the most stable temperature series is that of Liaoning,followed by the temperature series of Guangdong,and the most unstable one is that of Jiangsu.Liaoning has the most stable climate system,and it will thus be less responsive to the future climate warming.The stability of the climate system in Jiangsu is the weakest,and its temperature fluctuation will continue to increase in the future,which will probably result in the meteorological disasters of high temperature and heat wave there.Guangdong possesses the strongest degree of multifractal strength,which indicates that its internal temperature series fluctuation is the largest among the three regions.The Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning,while the Tavgand Tminmultifractal strength of Jiangsu is weaker than that of Liaoning,showing that Jiangsu has a larger internal Tmaxfluctuation than Liaoning does,while it has a smaller fluctuation of Tavgand Tminthan Liaoning does.Guangdong and Liaoning both show the strongest Tminmultifractal strength,followed by Tavgmultifractal strength,and the weakest Tmax multifractal strength.However,Jiangsu has the strongest Tmax,followed by Tavg,and the weakest Tmin.The research findings show that these phenomena are closely related to solar radiation,monsoon strength,topography and some other factors.In addition,the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation,in which the long-range correlation influence of temperature series itself plays the dominant role.With the backdrop of global climate change,this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes,and thus the present study will be significant for the environmental protection of coastal areas.

Interpretation of 850 hPa Temperature in Temperature Forecast of Jiamusi Area

Based on the initial field temperature data of ECMWF 850 hPa from Jan- uary 2012 to December 2015, linear interpolation method of ECMWF was employed to calculate the 850 hPa temperature values at 8：00 and 20：00 of 7 stations （Jiamusi, Tangyuan, Huachuan, Huanan, Fujin, Tongjiang, Fuyuan）. Combined with the observed daily minimum and maximum air temperatures at the same time of the 7 stations, the correlations of the 850 hPa temperature values at 8：00 and 20：00 with the daily maximum or minimum air temperature of the ground meteorological obser- vation stations were established, and the ground observation data in accordance with the relevant analysis and correlation test principle of the prediction equation for factor were primarily selected. Regression method was used to establish forecast e- quation dividing into counties, month by month. The results showed that the 850 hPa temperature values at 8：00 and 20：00 were significantly correlated with the daily maximum or minimum air temperature, and the established temperature fore- cast equation was of certain guiding significance for the forecast of daily minimum and maximum temperature, which could help to improve the forecast accuracy.

Modeling Daily Average Temperatures in a Coastal Site of Central Africa: An Analysis of Seasonal Divisions

The seasonality and day-to-day variation of near-surface temperature patterns can greatly control nearly all physical and biological processes though temperature predictions at such scales remain challenging. This paper implements a simple analytical approach in order to generate daily average temperatures which implicitly accounts for surface heating and drivers through a comprehensive representation of station-based temperature records on a universal standard calendar propagated by the earth’s dynamics features. The modeled and observed pattern of daily temperatures exhibits a close agreement with the level of strength agreement exceeding 0.56. The extreme high and low values of the observed temperature patterns are equally well captured although model underestimates the probability of temperatures around the two modal peaks (~25.6℃ and 27.5℃). Additionally, a theoretical thermal-based division led to the identification of six seasons, including two hot and cold periods along with two pairs of mixed hot-cold. The theoretical division proposed here appears to be a good approximation for the understanding of rainfall seasonality in this area.

Effects of Greenbelts in Different Variety under Solar Thermal Radiation on Temperature Reduction

[Objective] The aim was to study on effects of greenbelts in different varieties on temperature drop under solar thermal radiation. [Method] In residential regions, effects of temperature reduction by five varieties of greenbelts （megaphanerophyte, dungarunga, shrub, herbaceous plant and bare land） and changing rules with days under the same solar thermal radiation were researched. [Result] Greenbelts＇ temperature changed with intensity of solar thermal radiation, among which greenbelt of megaphanerophyte absorbed, transfered and reflected thermal radiation through crown canopy. Temperature of underlying surface was reduced accordingly, where correlation between underlying surface＇s temperature and solar thermal radiation （R） was 0.156 and the temperature declined by 1.9 ℃. In contrast, correlation of temperature of underlying surface （of lawn） with solar thermal radiation （R） was as high as 0.820, but the temperature only declined by 0.6℃. [Conclusion] The established linear relationship between crown＇s temperature and air temperature actually provides references for temperature measurement of greenbelts at scale.

Trends of Temperature Extremes in China and their Relationship with Global Temperature Anomalies

Changes of temperature extremes over China were evaluated using daily maximum and minimum temperature data from 591 stations for the period 1961-2002. A set of indices of warm extremes, cold extremes and daily temperature range （DTR） extremes was studied with a focus on trends. The results showed that the frequency of warm extremes （F WE） increased obviously in most parts of China, and the intensity of warm extremes （I WE） increased significantly in northern China. The opposite distribution was found in the frequency and intensity of cold extremes. The frequency of high DTR extremes was relatively uniform with that of intensity： an obvious increasing trend was located over western China and the east coast, while significant decreases occurred in central, southeastern and northeastern China; the opposite distribution was found for low DTR extreme days. Seasonal trends illustrated that both F WE and I WE showed signifi- cant increasing trends, especially over northeastern China and along the Yangtze Valley basin in spring and winter. A correlation technique was used to link extreme temperature anomalies over China with global temperature anomalies. Three key regions were identified, as follows： northeastern China and its coastal areas, the high-latitude regions above 40~0N, and southwestern China and the equatorial eastern Pacific.

Influence of Inhomogeneity on the Estimation of Mean and Extreme Temperature Trends in Beijing and Shanghai

Inhomogeneities in the temperature series from Beijing and Shanghai are analyzed, using the detailed histories of both sets of observations. The major corrections for different periods range from ?0.33 to 0.6°C for Beijing and ?0.33 to 0.3°C for Shanghai, Annual mean and extreme temperature series are deduced from the daily observations and trends in the adjusted and unadjusted series are compared. The adjusted yearly mean temperatures show a warming trend of 0.5°C/ century since the turn of this century and an enhanced one of 2.0°C/ century since the 1960s. In contrast, the unadjusted data show a twice this value trend for Shanghai but little trend for Beijing at the long-term scale and overestimate the recent warming by 50%–130%. Beijing experienced a decrease of frequency of the extremes together with a cooling during the 1940s–1970s and an increase of frequency of extremes together with a warming since then. The trends of frequency of extremes at Shanghai were more or less opposite. It is implied that the regional trends of strong weather variations may be different even when the regional mean temperatures coherently change. Key words Inhomogeneity - Daily temperature series - Climatic warming - Extreme temperature The study was supported by the China NKBRSF Project G 1999043400, IAP/ DF and CAS project (KZ951-A1-402).

Climatic characteristics of high temperature in East China during 1961-2005

Based on the daily maximum temperature data covering the period 1961-2005, temporal and spatial characteristics and their changing in mean annual and monthly high temperature days（HTDs）and the mean daily maximum temperature（MDMT）during annual and monthly HTDs in East China were studied.The results show that the mean annual HTDs were 15.1 and the MDMT during annual HTDs was 36.3℃in the past 45 years.Both the mean annual HTDs and the MDMT during annual HTDs were negative anomaly in the1980s and positive anomaly in the other periods of time,oscillating with a cycle of about 12-15 years.The mean annual HTDs were more in the southern part,but less in the northern part of East China.The MDMT during annual HTDs was higher in Zhejiang,Anhui and Jiangxi provinces in the central and western parts of East China.The high temperature process（HTP） was more in the southwestern part,but less in northeastern part of East China.Both the HTDs and the numbers of HTP were at most in July,and the MDMT during monthly HTDs was also the highest in July.In the first 5 years of the 21st century,the mean annual HTDs and the MDMT during annual HTDs increased at most of the stations,both the mean monthly HTDs and the MDMT during monthly HTDs were positive anomalies from April to October,the number of each type of HTP generally was at most and the MDMT in each type of HTP was also the highest.