@article {
author = {Mirkamandar, B. and Sanaei-Nejad, Seied Hosein and Rezaee-Pazhand, H. and Farzandi, M.},
title = {Regression Patterns of Diurnal Temperature in Different Climatic Regions of Iran},
journal = {Water and Soil},
volume = {34},
number = {5},
pages = {1141-1156},
year = {2020},
publisher = {Ferdowsi University of Mashhad},
issn = {2008-4757},
eissn = {2423-396X},
doi = {10.22067/jsw.v34i5.76165},
abstract = {Introduction: The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients could be used only as approximation for estimating daily average temperature. Climatic conditions of the meteorological stations were also another parameter to be considered. This research presents a new method for estimating daily average of temperature in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately. Materials and Methods: E. Eccel (2012) developed algorithms to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation and relative humidity from available daily data. During the day, air temperature was calculated by: (1) (2) where S (t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: Current air Temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea). Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating daily average temperature in arid and semiarid regions of Iran. Final pattern has interception and new coefficients for estimate daily average of temperature. (3) Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled with linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters used in the present work. First a classification which provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation and three indices of De Martonne, Ivanov and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range and arid and semi-arid zone. Several clustering methods were used and around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation testes. Finally clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating daily average of temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis. Results and Discussion: The coefficient of determination (R2) and the Fisher statistics show that the patterns have a good ability to estimate the daily average of temperature. The daily average temperature pattern confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The average values of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. The mean squared error is a measure of the applicability of patterns. The accuracy of the estimating daily average temperature by the recommended models in three climates was confirmed by calculating the mean squared error. The proposed patterns of this study had less error than common patterns. Thus, the patterns have a good ability to estimate daily average temperature. Conclusion: The maximum temperature in calculating daily average of temperature is more effective than the minimum temperature. The standardized coefficient (Beta) of the daily average temperature patterns in coastal cluster was 48.2% for the minimum temperature and 51.8% for the maximum temperature. The largest influence of the maximum temperature was 63.1% in mountainous cluster for estimating daily average temperature. Range of the interception in the equations was from -1.735 to 0.26. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The average of the residuals in each patterns was zero. According to the graphs stabilization of variance can be seen based on the residual on the each pattern in each cluster. The proposed patterns were calculated according to mathematical principles but the common patterns did not consider these mathematical principles. The mean squared error (MSE) of the proposed patterns are less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns. The largest difference between the proposed patterns and the common patterns for estimate the daily average of temperature was 24% in mountainous cluster. Climatic clustering was done for states. The monthly and annual average temperature can be reliably estimated by using the data of sample stations in each state. These findings can be used to estimate daily, monthly and annual average of relative humidity in three climates and sample stations. In addition, one can employ the method for estimating daily, monthly and annual average of relative humidity and temperature based on around climatological clustering of Iran and other stations. Annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation can also be applied to estimate daily, monthly and annual average of temperature and relative humidity more accurately.},
keywords = {Daily average of temperature,Maximum temperature,Minimum temperature,Regression model,systematic sampling},
title_fa = {الگوهای کاربردی جدید برآورد میانگین دمای روزانه در نواحی مختلف اقلیمی ایران},
abstract_fa = {تأثیر اقلیم بر الگوهای رفتاری تغییرات دما در روشهای موجود مورد توجه قرار نگرفته است. با رسم دقیق منحنی تغییرات روزانهی دما در یک روز عادی نشان میدهد که توزیع این متغیر نرمال نیست و یک منحنی چوله به راست است. بنابراین استفاده از ضرایب مساوی برای برآورد میانگین دمای روزانه خطا دارد. این مقاله الگوهای جدیدی را برای برآورد میانگین دمای روزانه درسه ناحیه مختلف اقلیمی ایران و ایستگاههای نمونه مربوطه ارائه داده است. به این منظور خوشهبندی اقلیمی که مطابق آن کشور ایران به سه خوشه ساحلی، کوهستانی و بیابانی و نیمه بیابانی تقسیم شده، انتخاب و نمونهگیری سیستماتیک برای تعیین ایستگاههای منتخب صورت گرفت. سپس الگویهای رگرسیونی خطی پس از غربال و آماده سازی بر دادهها برازش داده شد. نتایج ارائه شده وجود عرض از مبدأ را در تمام الگوها تأیید میکند. که دامنه آن برای سه خوشه و ایستگاههای نمونه از 735/1- تا 26/0 میباشد. ضریب استاندارد شده (Beta) متغیرهای پیشگو، بیانگر نابرابری وزن این متغیرها در تمامی الگوها است. این ضریب در الگوی برآورد میانگین دمای روزانه خوشه ساحلی برای متغیرهای دمای کمینه و بیشینه به ترتیب 2/48% و 8/51% میباشد. این مقادیر نشان میدهد دمای بیشینه نسبت به دمای کمینه حساستر است. بیشترین تأثیر دمای بیشینه با مقدار 1/63% در خوشه کوهستانی برای برآورد میانگین دمای روزانه دیده میشود. واسنجی و قیاس الگوهای ارائه شده در این مقاله با الگوی مرسوم برآورد میانگین دمای روزانه نشان از توانایی بالای الگوهای پیشنهادی در برآورد این میانگین دارد. میانگین مربع خطا (MSE) معیار واسنجی و مقایسه الگوهاست. بیشترین اختلاف برآورد میانگین دمای روزانه میان الگوی مرسوم با الگوی ارائه شده برای خوشه کوهستانی بوده که 24% میباشد.},
keywords_fa = {Daily average of temperature,Maximum temperature,Minimum temperature,Regression model,systematic sampling},
url = {https://jsw.um.ac.ir/article_38856.html},
eprint = {https://jsw.um.ac.ir/article_38856_4f4a44e9692ef2e92ed4fd58586cc26c.pdf}
}