Daily Minimum and Maximum Temperature Estimation by Regression Analysis for Karaman City

AbstractIn studies in Western Australia, temperatures in air and one- and two-litre pads of cattle dung set out weekly and ranging from one to 20 days old were measured hourly for 438 days over all seasons, producing 1437 day x dung-pad observations. Daily maximum temperatures (and hence thermal accumulation) in cattle dung pads could not be accurately predicted using meteorological data alone. An accurate predictor of daily maximum dung temperature, using multiple regression analysis, required measurement of the following factors: maximum air temperature, hours of sunshine, rainfall, a seasonal factor (the day number derived from a linear interpolation of day number from day 0 at the winter solstice to day 182 at the preceding and following summer solstices) and a dung-pad age-specific intercept term, giving an equation that explained a 91·4% of the variation in maximum dung temperature. Daily maximum temperature in two-litre dung pads was 0·6°C cooler than in one-litre pads. Daily minimum dung temperature equalled minimum air temperature, and daily minimum dung temperatures occurred at 05.00 h and maximum temperatures at 14.00 h for one-litre and 14.30 h for two-litre pads. Thus, thermal summation in a dung pad above any threshold temperature can be computed using a skewed sine curve fitted to daily minimum air temperature and the calculated maximum dung temperature.

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Emergence of adult femaleSirex nigricornisF. andSirex noctilioF. (Hymenoptera: Siricidae) coincides with a decrease in daily minimum and maximum temperature

Agricultural and Forest Entomology ◽

10.1111/afe.12153 ◽

2016 ◽

Vol 18 (3) ◽

pp. 206-213 ◽

Cited By ~ 5

Author(s):

Jessica A. Hartshorn ◽

Laurel J. Haavik ◽

Jeremy D. Allison ◽

James R. Meeker ◽

Wood Johnson ◽

...

Keyword(s):

Maximum Temperature ◽

Daily Minimum

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An empirical method for estimating probability density functions of gridded daily minimum and maximum temperature

Advances in Science and Research ◽

10.5194/asr-10-59-2013 ◽

2013 ◽

Vol 10 (1) ◽

pp. 59-64

Author(s):

C. Lussana

Keyword(s):

Probability Density ◽

Extreme Values ◽

Estimation Procedure ◽

Empirical Method ◽

Probability Density Functions ◽

Optimal Interpolation ◽

Maximum Temperature ◽

Density Functions ◽

Daily Minimum ◽

Time Period

Abstract. The presented work focuses on the investigation of gridded daily minimum (TN) and maximum (TX) temperature probability density functions (PDFs) with the intent of both characterising a region and detecting extreme values. The empirical PDFs estimation procedure has been realised using the most recent years of gridded temperature analysis fields available at ARPA Lombardia, in Northern Italy. The spatial interpolation is based on an implementation of Optimal Interpolation using observations from a dense surface network of automated weather stations. An effort has been made to identify both the time period and the spatial areas with a stable data density otherwise the elaboration could be influenced by the unsettled station distribution. The PDF used in this study is based on the Gaussian distribution, nevertheless it is designed to have an asymmetrical (skewed) shape in order to enable distinction between warming and cooling events. Once properly defined the occurrence of extreme events, it is possible to straightforwardly deliver to the users the information on a local-scale in a concise way, such as: TX extremely cold/hot or TN extremely cold/hot.

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Diverse Characteristics of U.S. Summer Heat Waves

Journal of Climate ◽

10.1175/jcli-d-17-0098.1 ◽

2017 ◽

Vol 30 (19) ◽

pp. 7827-7845 ◽

Cited By ~ 11

Author(s):

Bradfield Lyon ◽

Anthony G. Barnston

Keyword(s):

Heat Wave ◽

Land Surface ◽

Large Scale ◽

Heat Waves ◽

Primary Data ◽

Maximum Temperature ◽

Daily Maximum ◽

Regional Variations ◽

Daily Minimum ◽

Summer Heat

Abstract Heat waves are climate extremes having significant environmental and social impacts. However, there is no universally accepted definition of a heat wave. The major goal of this study is to compare characteristics of continental U.S. warm season (May–September) heat waves defined using four different variables—temperature itself and three variables incorporating atmospheric moisture—all for differing intensity and duration requirements. To normalize across different locations and climates, daily intensity is defined using percentiles computed over the 1979–2013 period. The primary data source is the U.S. Historical Climatological Network (USHCN), with humidity data from the North American Regional Reanalysis (NARR) also tested and utilized. The results indicate that heat waves defined using daily maximum temperatures are more frequent and persistent than when based on minimum temperatures, with substantial regional variations in behavior. For all four temperature variables, heat waves based on daily minimum values have greater spatial coherency than for daily maximum values. Regionally, statistically significant upward trends (1979–2013) in heat wave frequency are identified, largest when based on daily minimum values, across variables. Other notable differences in behavior include a higher frequency of heat waves based on maximum temperature itself than for variables that include humidity, while daily minimum temperatures show greater similarity across all variables in this regard. Overall, the study provides a baseline to compare with results from climate model simulations and projections, for examining differing regional and large-scale circulation patterns associated with U.S. summer heat waves and for examining the role of land surface conditions in modulating regional variations in heat wave behavior.

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Anthropogenic Influence on Long Return Period Daily Temperature Extremes at Regional Scales

Journal of Climate ◽

10.1175/2010jcli3908.1 ◽

2011 ◽

Vol 24 (3) ◽

pp. 881-892 ◽

Cited By ~ 151

Author(s):

Francis W. Zwiers ◽

Xuebin Zhang ◽

Yang Feng

Keyword(s):

Return Period ◽

Waiting Times ◽

Maximum Temperature ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Human Society ◽

Multiple Model ◽

Extreme Temperatures ◽

Annual Maximum ◽

Daily Minimum

Abstract Observed 1961–2000 annual extreme temperatures, namely annual maximum daily maximum (TXx) and minimum (TNx) temperatures and annual minimum daily maximum (TXn) and minimum (TNn) temperatures, are compared with those from climate simulations of multiple model ensembles with historical anthropogenic (ANT) forcing and with combined anthropogenic and natural external forcings (ALL) at both global and regional scales using a technique that allows changes in long return period extreme temperatures to be inferred. Generalized extreme value (GEV) distributions are fitted to the observed extreme temperatures using a time-evolving pattern of location parameters obtained from model-simulated extreme temperatures under ANT or ALL forcing. Evaluation of the parameters of the fitted GEV distributions shows that both ANT and ALL influence can be detected in TNx, TNn, TXn, and TXx at the global scale over the land areas for which there are observations, and also regionally over many large land areas, with detection in more regions in TNx. Therefore, it is concluded that the influence of anthropogenic forcing has had a detectable influence on extreme temperatures that have impacts on human society and natural systems at global and regional scales. External influence is estimated to have resulted in large changes in the likelihood of extreme annual maximum and minimum daily temperatures. Globally, waiting times for extreme annual minimum daily minimum and daily maximum temperature events that were expected to recur once every 20 yr in the 1960s are now estimated to exceed 35 and 30 yr, respectively. In contrast, waiting times for circa 1960s 20-yr extremes of annual maximum daily minimum and daily maximum temperatures are estimated to have decreased to fewer than 10 and 15 yr, respectively.

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Determination of Factors Affecting Dengue Occurrence in Representative Areas of China: A Principal Component Regression Analysis

Frontiers in Public Health ◽

10.3389/fpubh.2020.603872 ◽

2021 ◽

Vol 8 ◽

Author(s):

Xiaobo Liu ◽

Keke Liu ◽

Yujuan Yue ◽

Haixia Wu ◽

Shu Yang ◽

...

Keyword(s):

Regression Analysis ◽

Linear Regression Analysis ◽

Principal Component Regression ◽

Principal Component ◽

Maximum Temperature ◽

Factors Affecting ◽

Positive Correlation ◽

Guangzhou City ◽

Monthly Average

Background: Determination of the key factors affecting dengue occurrence is of significant importance for the successful response to its outbreak. Yunnan and Guangdong Provinces in China are hotspots of dengue outbreak during recent years. However, few studies focused on the drive of multi-dimensional factors on dengue occurrence failing to consider the possible multicollinearity of the studied factors, which may bias the results.Methods: In this study, multiple linear regression analysis was utilized to explore the effect of multicollinearity among dengue occurrences and related natural and social factors. A principal component regression (PCR) analysis was utilized to determine the key dengue-driven factors in Guangzhou city of Guangdong Province and Xishuangbanna prefecture of Yunnan Province, respectively.Results: The effect of multicollinearity existed in both Guangzhou city and Xishuangbanna prefecture, respectively. PCR model revealed that the top three contributing factors to dengue occurrence in Guangzhou were Breteau Index (BI) (positive correlation), the number of imported dengue cases lagged by 1 month (positive correlation), and monthly average of maximum temperature lagged by 1 month (negative correlation). In contrast, the top three factors contributing to dengue occurrence in Xishuangbanna included monthly average of minimum temperature lagged by 1 month (positive correlation), monthly average of maximum temperature (positive correlation), monthly average of relative humidity (positive correlation), respectively.Conclusion: Meteorological factors presented stronger impacts on dengue occurrence in Xishuangbanna, Yunnan, while BI and the number of imported cases lagged by 1 month played important roles on dengue transmission in Guangzhou, Guangdong. Our findings could help to facilitate the formulation of tailored dengue response mechanism in representative areas of China in the future.

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