%0 Journal Article
%T Modeling Average Relative Humidity of Sunrise to Sunset Using Values Measured at Different Hours in Iran
%J Water and Soil
%I Ferdowsi University of Mashhad
%Z 2008-4757
%A Mirkamandar, B.
%A Sanaei-Nejad, Seied Hosein
%A Rezaee- Pazhand, H.
%A Farzandi, M.
%D 2021
%\ 08/23/2021
%V 35
%N 3
%P 427-443
%! Modeling Average Relative Humidity of Sunrise to Sunset Using Values Measured at Different Hours in Iran
%K climatic regions
%K regression patterns
%K Relative humidity
%K systematic sampling
%R 10.22067/jsw.2021.15050.0
%X Introduction: The behavior of daily changes of relative humidity is quite variable. We first draw the curve of this variable on a normal day. And it can be seen that the distribution of this variable is not normal. The curve of this variable is a skewed curve to the right. Therefore, the equal coefficients could be used only as an approximation for estimating the daily average of relative humidity. Climatic conditions of the meteorological stations are also another parameter to be considered. This research presents a new method for estimating the daily average of relative humidity 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 an algorithm 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, the 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 the 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).Mirkamandar, et al. (2020) modified new patterns of diurnal temperature based on climatically clustering in Iran. The final pattern has an interception and new coefficients to estimate the daily average of temperature. Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating the daily average temperature in arid and semiarid regions of Iran. The final pattern has an interception and new coefficients to estimate the 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 by linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters. Which were used in the present work. First, a classification that provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation, and three indices of Demartonne, Ivanov, and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range, arid and semi-arid zone. Several clustering methods were used and the 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 tests. Finally, a 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 the daily average 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 showed that the patterns had a good ability to estimate the daily average of relative humidity. The daily average of relative humidity patterns confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The mean squares errors were a measure of the applicability of patterns. The accuracy of the estimating daily average of relative humidity recommended models in three climates was confirmed by calculating the mean squared errors. The proposed patterns of this study had less error than the common patterns. Conclusion: The relative humidity at 3 pm was more effective in estimating the daily average. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The averages 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. Proposed patterns were calculated according to mathematical principles. But the common patterns did not observe these mathematical principles. The mean squares errors (MSE) of proposed patterns were less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns.
%U https://jsw.um.ac.ir/article_40068_d14aba7d1838e6b4d5237f73a300d06b.pdf