Due to the importance of meteorological data AND limitations of data gathering from ground stations, remote sensing can play an important role in the preparation of these data. The purpose of this study was to quantitatively evaluate the LAND Surface TEMPERATURE (LST) obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images for estimating the MAXIMUM AND MINIMUM DAILY air TEMPERATURE in the Taleghan watershed. For this purpose, the MAXIMUM AND MINIMUM DAILY air TEMPERATURE data of three existing ground stations for the period 2009 to 2015 were obtained. Day AND night LST AND Normalized Difference Vegetation Index (NDVI) values ​​of MODIS were also prepared. The relationships between each of the effective variables AND MAXIMUM AND MINIMUM DAILY air TEMPERATURE in ground stations have been extracted using multiple linear regression method. The results showed that there was a significant correlation between MAXIMUM AND MINIMUM DAILY TEMPERATURE of ground stations with day AND night LST AND NDVI from MODIS sensor. Therefore, these variables were used in regression relationships. The results of validation showed that the established relationships with all effective variables had the most accuracy. Therefore, the best model for estimating the MAXIMUM DAILY air TEMPERATURE had , NSE AND RMSE values ​​of 0.74, 0.74, AND +4.7, respectively AND for estimating the MINIMUM DAILY air TEMPERATURE had 0.71, 0.72 AND +2.9, respectively. Therefore, by converting the surface TEMPERATURE obtained from MODIS sensor images, the air TEMPERATURE can be estimated with high accuracy on a DAILY AND monthly scales for various studies.

Introduction Considering the industrial development in recent years, the need for climatological atlas AND also DAILY metrological data have increased AND has become important economically. Air TEMPERATURE is of special importance in our understANDing of various natural processes in the nature. Moreover, in order to detect the impact of greenhouse gases on climate change AND developing ecological models in various regions, much attention have been given to spatial distribution of TEMPERATURE. Hence, developing AND testing accurate interpolation methods for spatial analysis of TEMPERATURE is this clear especially over data void regions. In order to successfully transfer information from irregularly distributed observing stations to a regular grid, information about physical characteristics of the region have to be taken into account. To reflect spatially complicated climate patterns at regional scales, climatic dependence on topography must be taken into account when developing reliable climate estimates.

The global warming process during last century has not only affected on amount of atmospheric parameters but also affected on onset AND end of each atmospheric parameters. The air TEMPERATURE trend has been increasing during recent decades, especially in the regions such as Iran which is located in dry AND semi-dry world belt. In this investigation, using Mann – Kendall statistical test, which is one of the proposed methods of World Meteorological Organization (WMO) for time series analysis, the trend of seasonal MAXIMUM AND MINIMUM TEMPERATUREs in Iran will be studied. Hence, In this research, the DAILY data of MAXIMUM AND MINIMUM TEMPERATURE with spatial resolution of 0.5 *0.5 degree received from cell database in the world scale, available in data base: http: //hydro.engr.scu.edu/files/, AND converted to utilizable file (.TXT) in mat lab software by using Grads software. In continuation, Mann-Kendall script code, executed for a 21550*2058 matrix to calculating the threshold trend of 95% in Mat lab. We use from Arc GIS software for graphical representation of results. The results of this research indicate that the MAXIMUM TEMPERATURE of northwest AND southeast of Iran is without trend (trendless) in every four seasons, AND in the winter, north-eastern of Iran have the least area. In the autumn, the increasing trend of MAXIMUM TEMPERATURE seem more in the eastern area AND Fars, whereas, the MAXIMUM TEMPERATURE of autumn in without trend (trendless) in central AND northwest area of Iran. The MINIMUM TEMPERATURE has increasing trend in the most regions of Iran. In this relation, the increasing trend of MINIMUM TEMPERATURE in winter is completely obvious in northwest, Zagros AND southeast of Iran.

Long time series are needed for analysis of time variation, trend of extreme events, risk estimation AND possible events. One of the most important time series in geographical AND climatic science is DAILY MAXIMUM AND MINIMUM TEMPERATURE. These two parameters use DAILY evapotranspiration estimation, determination of water balance AND climate change study. MAXIMUM AND MINIMUM TEMPERATURE are measured in meteorological stations. However, different statistical years, deficiency in statistical data AND error of measurement cause variation in time series. Therefore, reconstruction of time series is very important. This research evaluates reconstruction of DAILY extreme TEMPERATUREs to nearest neighbor AND artificial neural network methods for five stations in the west of Tehran Province. In the nearest neighborhood method correlation between respective MAXIMUM or MINIMUM TEMPERATURE is used. Whilst in the artificial neural network using meteorological stations network the MINIMUM AND MAXIMUM DAILY TEMPERATURE are reconstituted. Neural network used in this research is a multilayer feed forward network with back propagation algorithm AND hidden layer.Results show that artificial neural network method had least mean absolute error for all stations compared to the nearest neighbor method. With increasing distance of the station the estimated error increases in the nearest neighbor method. Accuracy of the two methods in estimating DAILY MAXIMUM is more than the DAILY MINIMUM TEMPERATURE.

Direct numerical weather prediction model forecasts of near surface parameters often suffer from systematic errors mainly due to the low resolution of the model topography AND inaccuracies in the physical parameterization schemes incorporated in the model. In this paper a simple objective algorithm based on Kalman filtering have been implemented to correct the MAXIMUM AND MINIMUM TEMPERATURE model forecasts.In the last couple of years different methods for a postprocessing the model outputs have been developed. Kalman filter is one of them which provides a practical tool that combines the observed data AND predictions of the model using a simple algorithm to reduce the systematic errors of the direct model outputs without the need for long historical data archives.This paper is organized as follows. In Section 2, we introduce a simple Kalman filter. In Section 3 AND 4, we show that how the filter is applied on the model outputs for 2 meter MINIMUM AND MAXIMUM TEMPERATURE for 117 meteorological stations. In Section 5, statistical results are presented AND finally the paper is concluded in section 6. Simple Kalman Filter: The Kalman filter theory provides equations for recursively updating estimates of an unknown process through combining observations related to the process AND time evolution of the process. Let xt be a vector describing the state of the unknown process at time t that, in this paper, is considered to be the systematic deviation between the observed AND predicted TEMPERATUREs. The state vector at time t is related to the state at time t−1 through the system equation: xt=ft.xt-1+wt where Ft describe the systematic change in xt AND wt denotes the rANDom part of the evolution of t x from time t−1 to time t AND is known as the noise vector of the process. The state xt is related to the observation(s) yt through the observation equation: yt=ht.xt+vtwhere Ht is the observation matrix AND vt is the noise vector of the observed data. wt AND vt are assumed to be Gaussian white noise with zero mean processes AND to have covariance matrixes Q AND R respectively. Kalman Filter has two main steps; first step includes predictor equations which preestimate the state AND its corresponding error covariance matrix:xt/t-1 = Ft . Xt-1 Pt/t-1 = Ft. Pt-1 .FTt +Q   xt/t-1 is the pre-state AND P is its error covariance matrix. The next step includes the corrector equations which update the pre-state using recent observation: xt = xt/t-1 + Kt (yt-Ht.xt/t-1) Kt = pt/t-1 .Ht/Ht .pt/t-1. HTt+R Pt= (I-H .Kt) Pt/t-1 where kt is Kalman gain. Procedure: Since there is not sufficient information about the dynamics of the system, a number of simplifying assumptions have been made; we consider F AND H as constant unit matrixes.Estimates of initial state x0 AND P0 AND also wt AND vt are required before running the filter. Initial values of x0 AND P0 are not effective in the filter performance after some iterations AND their consequence is lost. But the main problem in applying the filter is determination of noise vectors, wt AND vt. The method proposed by Galanis AND Anadranistakis for calculating wt AND vt is used here AND a training period of seven days was selected to train the filter using outputs of the MM5 modeling system. The Kalman filter was applied on model outputs for MINIMUM AND MAXIMUM TEMPERATURE forecasts for 117 meteorological stations over Iran during 120 days AND some statistical scores were calculated.

Air conditioning systems are used in industries AND residential environments with the aim of improving environmental conditions AND creating a comfortable TEMPERATURE for users. Considering the production of sound AND vibration in most of its components, the lack of control of the production sound level always exposes the users to unwanted sound, which in addition to hearing complications, also causes fatigue AND dissatisfaction with the environmental conditions. . Compressors are one of the most important sources of sound production in air conditioning systems, AND screw compressors are one of the most widely used in air conditioning industries. Therefore, the compressor shell should be designed to minimize the transmission of sound from inside to outside. Since the MAXIMUM working TEMPERATURE of compressors is up to 80 degrees Celsius, therefore, in this research, an acoustic test was performed on a sample of a screw compressor shell in two modes of ambient TEMPERATURE AND MAXIMUM working TEMPERATURE inside the acoustic room, AND the effect of TEMPERATURE increase on the sound pressure level. transferred from the shell to the external environment is discussed. Finally, based on the frequency analysis performed in two conditions of ambient TEMPERATURE AND MAXIMUM working TEMPERATURE AND comparing the amount of sound transmitted to the environment at different frequencies, practical solutions to reduce the amount of transmitted sound pressure have been presented.

Estimating air TEMPERATURE is one of the important issues in agricultural planning AND in water resources management which can be accomplished by using different methods such as empirical, semi-empirical AND intelligent methods. In the present study, Adaptive Neuro Fuzzy Inference System, Artificial Neural Networks AND Genetic Programming were used to estimate air TEMPERATURE in the synoptic station of Tabriz City, northwest of Iran. Considering the statistical indices, all three models were able to estimate accurately MINIMUM, mean AND MAXIMUM air TEMPERATURE. In spite of slight differences in the prediction accuracy AND errors by the models, Adaptive Neuro Fuzzy Inference System, Artificial Neural Networks AND Genetic Programming were in the order of priority. Also explicit solutions that show the relation between input AND output variables are presented based on Genetic Programming. This adds to the superiority of Genetic Programming over the other two models.

A B S T R A C T TEMPERATURE is one of the climate elements that has fluctuated a lot over time. When these fluctuations increase AND decrease more than normal AND are placed in the upper AND lower regions of the statistical distribution, if continued, it can lead to the creation of heating AND cooling waves. The purpose of this study is to analyze the temporal AND spatial changes in heating AND cooling waves in Iran during a period of 50 years. For this purpose, the TEMPERATURE of 663 synoptic stations from 1962 to 2004 was obtained from the Esfazari database. Then, in order to complete this database, the DAILY TEMPERATURE from 2004 to 2011 was obtained from the Meteorological Organization of the country AND added to the aforementioned database. In order to perform calculations AND draw maps, Matlab, grads AND Surfer software have been used. The results of this study showed that the index of cooling waves AND heating waves, while having a direct effect on each other, had an increasing trend in most of the area of Iran. The statistical distribution of the index of cooling waves is more heterogeneous than that of the index of heating waves. So that the spatial variation coefficient for cold waves is 84.22%. Also, the index of cooling waves has more spatial variability. The highest common diffraction of the index of heating AND cooling waves has been seen in the northwest, east AND along the Zagros mountains. Analysis of the indexes trends show that heat waves have intensified in 65.8% of Iran AND the intensity of cold waves has decreased in 48.5% of Iran Extended Abstract Introduction TEMPERATURE is one of the major climatic variables, which it has a direct impact on different aspects of human life. It plays an essential role in the growth of crops AND is considered a key driver of the biological system(Reicosky et al, 1988). It is associated with several types of extremes, for example, heat AND cold waves which caused human societies MAXIMUM damage. Past occurrences of heat waves hitherto had significant impacts on several aspects of society. Have increased Mortality AND morbidity. Ecosystems can be affected, as well as increased pressure on infrastructures that support society, such as water, transportation, AND energy(Dewce, 2016). The long-term change of extreme TEMPERATUREs has a key role in climatic change. The form of statistical distribution AND the variability of mean values AND also extreme event indicate a change in the region. It can be a small relative change in the mean as a result of a large change in the probability of extreme occurrence. Also, the variation in TEMPERATURE data variance is significantly more important than the mean, for assessing the extreme occurrence of climate(Toreti AND Desiato, 2008). The average surface TEMPERATURE has increased the world between 0.56 AND 0.92 ° C over the past 100 years(IPCC, 2007). Meanwhile, it was in the Middle East, the average DAILY TEMPERATURE increased by 0.4-0.5 ° C in decades(Kostopoulou et al, 2014; Tanarhte et al, 2012). Considering that not many studies have been done in the field of spatio-temporal Variations of the heating AND cooling waves thresholds in Iran, in this study, the spatio-temporal Variations of the heating AND cooling waves thresholds in Iran during 50 years were examined AND analyzed.   Methodology The DAILY TEMPERATURE from the beginning of the year 21/03/1967 to 19/05/2005 was obtained from the Esfazari database prepared by Dr. Masoudian at the University of Isfahan. In order to increase the time resolution of the mentioned database, the DAILY TEMPERATURE of observations from 05/21/2005 to 05/12/2012 has been added to the mentioned database using the same method, AND the exact spatial resolution (15 x 15 km) is used as a database. Threshold indices of heating waves are the average numbers between the 95th AND 99th percentiles, that is, the extreme hot threshold to the limit of excessively extreme hot. For extreme cool, from the 5th percentile down to zero is used. Of course, a condition was added to these thresholds, which is that these thresholds must be repeated two days in a row. These thresholds were extracted for each day in the 50 years of the study period AND used as the original database. In order to analyze the relationship between cooling AND heating waves, Pearson's correlation coefficient was used AND regression was used to analyze the trend.   Results AND discussion The average of cold waves was 5.26 ° C AND for the heat waves is 30.20° C. Generally, if the TEMPERATURE is upper or lower than this threshold, it is considered as hot or cold TEMPERATUREs. A comparison of the median, mode, AND average of cold waves with heat waves shows that the distribution is more heterogeneous for cold waves AND its CV is 84.22%. In southern Iran, the average threshold heat waves are higher. This situation can be caused by the effects of subtropical high-pressure radiation, low latitude, AND proximity to the sea. Though the threshold is higher in these areas, fewer fluctuations AND changes are seen in the area. Heights moderate the TEMPERATURE so they pose a MINIMUM threshold for heat waves i.e. an iso-threshold of 25 ° C is consistent along the Zagros mountain chains, but in the west AND east of Zagros Mountains, the threshold of heat waves is increased. Heat waves have increased in most areas of the country. So nearly 85 percent of the Iran has been an increasing trend, of which 65.8 percent is statistically significant at the 95% confidence level. Still, more areas of the country (60 percent) have a trend between 0.00828 AND 0.00161. As can be seen, only 15% of the lAND area (including the southwest AND northwest of the Country) had decreased heat waves. Cold waves, in most parts of the country, have a Positive Trend. However, about 25 percent of the study area's cold waves have a negative trend. they are located in areas higher than Latitude 30°. The largest decline of the wave's trend along the country is highlANDs. Nowadays, most of the country, has a trend between 0.01494 AND 0.00828 ° C, respectively. Conclusion Common changes AND effects of heat AND cold waves had a direct relationship in many parts of the country. It is remarkable common variance in the East reached 55 percent, according to statistical significance. In some areas of the northwest AND southwest, which have been impressive heights, the common variance is 40 percent. This common variance in mountains area has been high values. Investigation of heat waves trend shows that 65.8% of Iran significant positive trend AND 7.1% significant negative trend. Also, the cold waves trend has indicated a 48.5% significant positive trend AND a 10.8% significant negative trend. Climate change AND global warming have changed the frequency AND severity of TEMPERATURE extremes. The present study, by examining the number of warm waves, concluded that the warm waves have increased in magnitude in 65.8% of the Iran zone. Also, the study of the cold waves trend showed that 48.5 percent of Iran had a positive trend, which means that the amount of TEMPERATURE in the cold waves increased In other words, the severity of the cold has been reduced AND only 10.8 percent of Iran had a negative cold wave trend AND it shows the intensity of these waves is reduced.   Funding There is no funding support.   Authors’ Contribution The authors contributed equally to the conceptualization AND writing of the article. All of the authors approthe contenttent of the manuscript AND agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest The authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Continuous monitoring of TEMPERATURE in extreme areas, such as mountainous AND desert regions, is hampered with insufficient or sparse distribution of meteorological stations, as well as the complexity of interpolating existing station data. For this reason, the use of satellite remote sensing data has increased significantly in recent years. In this study, the DAILY LST data (daytime AND nighttime) obtained from the MODIS Aqua satellite in the period from 2003 to 2019 at 1-km resolution, as well as the meteorological data of 11 synoptic stations, were used to estimate the air TEMPERATURE in Yazd province. The assessment of the relationship between the mean monthly LST (day/night) AND the mean monthly air TEMPERATURE (MAXIMUM/MINIMUM) indicated a strong correlation between them. The coefficient of determination (R2) values between LST daytime AND MAXIMUM air TEMPERATURE varied from 0.989 to 0.997 AND between LST nighttime AND MINIMUM air TEMPERATURE from 0.991 to 0.999. Therefore, according to the suitable spatial coverage of MODIS LST, it is possible to estimate the air TEMPERATURE for different cells of Yazd province using the linear regression equation. In addition, the results indicated that the RMSE values at night were much smaller than the values during the day. Therefore, it is possible to retrieve the lAND surface TEMPERATURE at night with a much higher accuracy than during the day. The findings of this research also showed that the estimated air TEMPERATURE AND LST have similar seasonal cycles. Although the difference between these two variables is greater during the day than at night. These differences between the air TEMPERATURE AND LST increase in the summer season (June to August).

Weather forecasting AND monitoring systems based on numerical weather forecasting models have been increasingly used to manage issues related to meteorology AND agriculture. Using more accurate MINIMUM AND MAXIMUM TEMPERATURE forecasts can be helpful in this regard. But systematic AND rANDom errors in the model affect the accuracy of forecasts. In this study, the model errors during the 5 AND 14 days training period in the same climate areas on the points of the network where the observations are available are calculated. Then the errors are generalized on all points of the network using the cokriging interpolation method. This, preserves the model forecasts for other points of the network AND only error values are applied to them. To better evaluate the model, the spatial AND temporal distribution of the MAXIMUM AND MINIMUM TEMPERATURE forecast errors are also investigated in the country. Observed DAILY MAXIMUM AND MINIMUM TEMPERATUREs data from 560 meteorological stations for the period 1/11/2019 to 1/2/2021 are used to evaluate the WRF model. The WRF model is run DAILY at 12UTC, with a forecast time of 120 hours. AND first 12 hours of each run is consider as the model spin-up AND is not used in errors calculation. In order to correct the MAXIMUM AND MINIMUM TEMPERATURE forecast errors for next three days (forecasts of 36, 60 AND 84 hours), the forecasts for each day in the period of 11/1/ 2019 to 1/2/2021, is extracted from the model outputs. In order to evaluate the error correction method, the skill score index is used. The validation results of the error correction method shows that the absolute mean error value, correlation coefficient AND RMSE are improved after the error correction compared to results that were before the error correction. This shows that the error correction method can be used for other network points that do not contain observational data. The results shows that the RMSE of the raw model MAXIMUM (MINIMUM) TEMPERATUREs forecasts for next three days is approximately 6 degrees Celsius (5 degrees Celsius), which after error correction reaches 2 degrees Celsius (4 degrees Celsius). Also the value of correlation coefficient, after correcting for the model error, has a significant increase compared to the raw model output. The average skill score for the raw MINIMUM AND MAXIMUM TEMPERATURE forecast for more than 50% of the days is more than-1 AND-1. 9, respectively, but after correction, the model skill scores become closer to one AND for more than 75 percentage of days that reach above zero. Without exception, all climatic regions after error correction have a higher skill score than before error correction, so that the model skill score for most climatic regions after error correction reaches above zero for more than 75% of the days. Before error correction, the warm semi-humid zone has the lowest average skill score for forecasting MAXIMUM AND MINIMUM TEMPERATUREs among climatic zones, but after error correction it reaches the highest value among other zones. In general, for areas with hot AND dry climates, the raw output skill score for predicting the MINIMUM TEMPERATURE in July, August, AND September is minimized. The 14-day error correction method did not improve the modeling skill score much compared to the 5-day error correction method, AND they acted almost similarly. In areas with high elevation gradient, the model error increases. In general, model underestimates the MAXIMUM AND MINIMUM TEMPERATUREs in most areas. Knowing the spatial AND temporal distribution of model forecast error can be helpful for researchers to have an overview of the areas (AND months) where the model forecast error is high.