Introduction: The Earth's atmosphere (atmosphere) is divided into concentric layers with different chemical and physical properties. To study wave propagation, two layers called the troposphere and ionosphere are considered. The troposphere is the lowest part of the Earth's atmosphere and extends from the Earth's surface to about 40 kilometers above it. In this layer, wave propagation is mainly dependent on water vapor and temperature. Unlike the ionosphere, the troposphere is not a dispersive medium for GPS signals (seeber, 2003). As a result, the propagation of waves in this layer of the atmosphere does not depend on the frequency of the signals. The delay caused by the troposphere can be divided into two parts of hydrostatic delay and wet delay. The hydrostatic component of the tropospheric delay is due to the dry gases in this layer. In contrast, the wet component of tropospheric refraction is caused by water vapor (WV) in the troposphere. The study of atmospheric water vapor is important in two ways: First, short-term climate change is highly dependent on the amount of atmospheric water vapor. Water vapor has temporal and spatial variations that affect the climate of different regions. Second, long-term climate variation is reflected in the amount of water vapor. Obtaining water vapor using direct measurements and water vapor measuring devices is a difficult task. Radiosonde and radiometers are used to directly measure atmospheric water vapor, but the use of these devices will have problems and limitations, for example, the maintenance cost of these devices is expensive and also these devices do not have a suitable station cover. The best way to get information about water vapor changes indirectly is to use GPS measurements. GPS meteorological technology can provide continuous and almost instantaneous observations of the amount of water vapor around a GPS station. Estimation of precipitable water vapor (PWV) and water vapor density using voxel-based tomography method has disadvantages. The coefficient matrix of tomography method has a rank deficiency. Initial value of water vapor must be available to eliminate it. Also, the amount of WV inside each voxel is considered constant, if this parameter has many spatial and temporal variations. In this method, the number of unknowns is very high and it is computationally difficult to estimate (Haji Aghajany et al., 2020). To overcome these limitations, this paper presents the idea of using learning-based models. To do this, in this paper, 3 models of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression model (SVR) have been used. Materials and methods: Due to the availability of a complete set of observations of GPS stations, radiosonde and meteorological stations in the north-west of Iran, the study and evaluation of the proposed models of the paper is done in this area. Observations of 23 GPS stations were prepared in 2011 for days of year 300 to 305 by the national cartographic center (NCC) of Iran. Out of 23 stations, observations of 21 stations are used to training of models and observations of the KLBR and GGSH stations are used to test the results of the models. In the first step, the observations of 21 GPS stations that are for training are processed in Bernese GPS software (Dach et al., 2007) and the total delay of the troposphere in the zenith direction (ZTD) is calculated. It should be noted that for every 15 minutes, a value for ZTD is calculated using the observations of each station. In the second step, the zenith hydrostatic delay (ZHD) is calculated. By subtracting ZHD from ZTD, zenith wet delay (ZWD) are obtained. ZWD values are converted to PWV values. The obtained PWV values are considered as the optimal output of all three models ANN, ANFIS and SVR. Also, the input observations of all three models will be the latitude and longitude values of each GPS station, day of the year and time. Results and Discussion: After the training and achievement of the minimum cost function value for all three models, the PWV value is estimated by the trained models and compared at the location of the radiosonde station as well as the test stations. The mean correlation coefficient for the three models ANN, ANFIS and SVR in 6 days was 0. 85, 0. 88 and 0. 89, respectively. Also, the average RMSE of the three models in these 6 days was to 2. 17, 1. 90 and 1. 77 mm, respectively. The results of comparing the statistical indices of correlation coefficient and RMSE of the three models at the location of the radiosonde station show that the SVR model has a higher accuracy than the other two models. The average relative error of ANN, ANFIS and SVR models in KLBR test station was 14. 52%, 11. 67% and 10. 24%, respectively. Also, the average relative error of all three models in the GGSH test station was calculated to be 13. 91%, 12. 48% and 10. 96%, respectively. The results obtained from the two test stations show that the relative error of the SVR model is less than the other two models in both test stations. Conclusion: The results of this paper showed that learning-based models have a very high capability and accuracy in estimating temporal and spatial variations in the amount of precipitable water vapor. Also, the analyzes showed that the SVR model is more accurate than the two models ANN and ANFIS. By estimating the exact amount of PWV, the amount of surface precipitation can be predicted. The results of this paper can be used to generate an instantaneous surface precipitation warning system if the GPS station data is available online.

Introduction The present study aimed to compare the acute and chronic responses of blood pressure، arterial stiffness، and peripheral arterial disease to 11 weeks of two different high-intensity interval training (HIIT) protocols in hypertensive patients. Methods: This study was conducted on 31 hypertensive patients who were randomly assigned into SDHIIT (n=10)، LDHIIT (n=11)، and control (n=10) groups. The patients in the SDHIIT group were subjected to HIIT protocol، including 27 repetitions of 30 sec at 80-100% VO2peak with 30-sec recovery intervals at VO2peak of 10-20%. On the other hand، the LDHIIT group performed four repetitions of 4 min at 75-90% VO2peak interspersed by four 4-min recovery repetitions at the VO2peak of 15-30%. Systolic and diastolic blood pressure، pulse wave velocity (PWV)، and ankle-brachial index (ABI) were measured before and after the first and last exercise sessions. Results: There was no significant differences between the two groups in terms of PWV after one session of training (P>0. 05). However، the SBP، DBP، and ABI significantly decreased in the two groups (P<0. 05). Considering the acute responses، a significant reduction was observed in the SBP (P=0. 03) and ABI (P<0. 01) in the LDHIIT and SDHIIT groups، respectively، after 11 weeks of training. Nonetheless، no significant difference was detected in PWV and blood pressure. Regarding the chronic effect of the two training protocols، the LDHIIT group showed a significant difference in all variables، except for ABI (P>0. 05). Conclusion: Based on the findings of the study، both HIIT protocols improved blood pressure and hemodynamic factors in acute forms. However، SDHIIT was more effective than LDHIIT in the improvement of blood pressure and other variables in the long run.

Precipitable Water Vapor (PWV) is one of the most important quantities in meteorology, atmospheric physics, hydrology and climate change studies, which its estimation is useful in predicting precipitation, flood occurrence and other hydrological parameters. Today, satellite imagery is widely used to estimate PWV and analyze its correlation with other meteorological Variables. The objective of this study was to estimate the amount of PWV and to investigate its relationship with six climatic variables such as; temperature, pressure, relative humidity, cloudiness ratio, precipitation and wind speed in the geographical area of Iran using satellite-based data. The proposed data with monthly time steps and 1° *1° spatial resolution were selected in the climatic range of Iran's atmosphere for the period of 2003-2019. Pearson correlation coefficient was used to investigate the relationship between PWV and the above mentioned climatic variables. Digital data extracted after qualitative control and pre-processing were used by specialized software such as ENVI, ArcGIS and Grads to build raster layers based on the geographical boundary of Iran. According to the results, the average PWV in the atmosphere of Iran is 12. 7 mm, which shows a lower amount as compared to the global average (21. 6 mm). On the other hand, the amount of PWV in the Atmosphere of Iran does not have a temporal and spatial homogeneous distribution. So that the highest amount of PWV is concentrated in the coastal area of south and north and the lowest amount is concentrated over the Zagros mountain range, parts of northeast and east of Iran and in the next priority in the desert areas of central Iran. The Pearson correlation coefficients between PWV and the meteorlogical variables were 86% for air temperaure,-89% for pressure,-88% for relative humidity,-32% for cloudiness ratio,-64% for precipitation and 67% for wind speed.

Precipitable water vapor (PWV) is a key parameter in meteorological studies and forecasting of atmospheric events such as rain and flood. Due to the spatial limitations of GPS and meteorological stations, as well as observational discontinuities in the time domain, PWV modeling is of great importance. Obtaining PWV using direct measurements and water vapor measuring devices is a difficult task. The best way to get information on PWV variations indirectly is to use GNSS measurements. The GNSS meteorological technique can provide continuous and almost instantaneous observations of the amount of PWV around a GNSS station. Research has shown that the accuracy of weather forecasts can be improved using GNSS-dependent techniques. Models based on GNSS observations for estimating PWV are known as tropospheric analytical models. The tomographic model is one of the most famous and widely used tropospheric models. There are limitations such as a large number of unknown parameters, rank deficiency of design matrix and the inevitability of using regularization methods, assuming the amount of water vapor inside each voxel is constant and also, the need for initial amounts of water vapor inside the voxels in the voxel-based tomography (VBT) method. Such limitations have led researchers to use machine learning methods to estimate the spatio-temporal variation of PWV. In this paper, the spatio-temporal modeling of PWV is suggested using the generalized regression neural network (GRNN) model. The GRNN model is a type of artificial neural network (ANN) that uses radial basis functions (RBF) as an activity function in the hidden layer. As a result, its accuracy is higher than the ANN model. Eight parameters of longitude, latitude and height of GPS station, day of year (DOY), time (min. ), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of GRNN and ANN models and the PWVs corresponding to these eight parameters are the outputs. After the training step, to evaluate the GRNN and ANN models, the observations of two GPS networks are used. In the GPS network of north-west of Iran, observations of 23 GPS stations in the period of 300 to 314 (winter season) from 2011 have been used. For the central Alborz GPS network, observations of 11 stations at the period of 162 to 176 (summer season) in 2016 are also used. Results obtained from GRNN and ANN models in two interior control stations, one exterior control station (outside the GPS network territory) and also in Tabriz and Tehran radiosonde stations are compared and evaluated with the results of VBT, ECMWF, Saastamoinen and GPT3 models. The statistical parameters of root mean square error (RMSE), relative error and correlation coefficient (R) are used to evaluate the accuracy of the models. At the north-west GPS network, the averaged RMSE values of GRNN, ANN, VBT, ECMWF, Saastamoinen and GPT3 models in the two interior control stations are calculated as 2. 14, 2. 57, 3. 32, 3. 36, 6. 31 and 4. 35 mm, respectively. For the central Alborz GPS network, the averaged RMSE of two interior control stations are computed as 2. 01, 2. 42, 3. 24, 3. 26, 6. 00 and 4. 06 mm, respectively. For the exterior control station, the GRNN model has less error than the ANN, VBT and Saastamoinen models, but more than the ECMWF and GPT3 model. The results of this paper show that the GRNN model has a very high accuracy compared to other analytical and empirical models of the troposphere. This model has the ability to show the spatio-temporal variations of precipitable water vapor with high accuracy at the GPS network territory and,it can considered as an alternative for the other analytical and empirical models.

Precipitable Water Vapor (PWV) is one of the most important quantities in meteorology and climate studies. PWV in Earth's atmosphere can be measured by Sun-photometer, the Atmospheric Infrared Sounder (AIRS), and radiosonde from surface, atmosphere and space-based systems, respectively. In this paper, we use PWV measured by Sun-photometer located in Institute for Advanced Studies in Basic Sciences (IASBS), AIRS and 29 Iranian synoptic stations data include temperature, dew-point temperature, pressure and relative humidity. For validation of AIRS data, the correlation coefficient between AIRS and Sun-photometer data calculated. The correlation is 90%. Average of PWV measured with sun-photometer and AIRS are 9.8 and 10.8 mm, respectively. Pearson's correlation coefficients between PWV of AIRS data set and temperature, dew-point temperature, pressure and relative humidity for synoptic stations are calculated. Correlation between PWV and temperature, dew-point temperature, pressure, and humidity are 73%, 74%, -40% and -30%, respectively. PWV and temperature correlation coefficient map shows a positive trend between latitude and correlation coefficient. Rising a degree in latitude lead to increasing 2.8 percent in the correlation coefficient.

Global Satellite Navigation Systems are widely used for geodetic and geodynamics purposes. However the meteorological applications, such as Precipitable Water Vapor (PWV) estimation, are increased with GNSS permanent stations deployments all over the world. The continuity of GNSS observations and the spatial resolution of the permanent GNSS stations are some of the potentials of GNSS remote sensing using permanent arrays. In this study we are demonstrated one of the real-time meteorological applications of GNSS networks. The spatial distribution of PWV was investigated during extreme rainfall. The PWV data from the SuomiNet network stations in the Texas state was implemented. Using linear interpolation, the PWV were determined for area within the stations. It was observed that the estimated water vapor from the GNSS observations progresses gradually towards the precipitation site and then, with accumulation in the area of ​​precipitation, the rainfall begins, and then the PWV decreases. Therefore, using a network of uniformly distributed GNSS stations, GNSS observations can be used to measure the accumulation of atmospheric precipitation in a region and to investigate the probability of rainfall occurring. These predictions will be effective if the network is sufficiently dense and the perceptible water vapor is estimated with a short latency. The estimation of zenith path delay from GNSS is possible using relative or absolute method. Furthermore the slant delay estimation is one of the possibility in the dense GNSS networks. Tropospheric tomography will aid the scientists in the future applications of GNSS. In this paper the precision of real time PWV estimation via GNSS data is investigated. French RGP GNSS networks data are used for PPP processing. The processing is performed by ultra-rapid IGS orbit and clock products and then it repeated using final IGS products. The precision of Zenith Total Delay (ZTD) of final ephemeris is about 3 mm. The Real time estimation of ZTD using ultra rapid data is compared by final solution and the RMSE for different stations are from 3 to 7 mm approximately that is sufficient for real time estimation of PWV and real time precipitations prediction. Investigation of raining occurrence and the PWV changes is performed in this paper. In the investigation of PWV it may be possible to follow a pattern or patterns for a region prior to intense rainfall, spatial variations and spatial distribution of PWV, which can predict extreme rainfall. Therefore, it is suggested that by studying the PWV behavior accurately, the probability of such patterns is examined. Also, in order to determine the accuracy of the PWV obtained from GNSS observations by the PPP method with the ultra-rapid orbit and clock products, it is possible to compare the PWV obtained from the above-mentioned method with those obtained from the measurement of radiosondes as a reliable source. Also the results of the ultra-rapid products are compared with the final IGS products the consistency is about 3-7 millimeters for the estimated ZTD values. It is also possible to predict the rainfall by the permanent GNSS stations in Iran. There are several permanent arrays which may provide the GNSS observation files instantaneously. The national geodynamic network, the Tehran's Instantaneous Network, The national cadastre RTK network and the Isfahan municipality RTK network, could be used for PWV estimation with high spatial and temporal resolution and instantaneous meteorological application of a unified network is possible.

The LS-SVR model uses simple linear equations in the training phase. As a result, the complexity of the computational algorithm is reduced,the speed of convergence and the accuracy of the results are increased. Seven parameters of longitude and latitude of GPS station, day of year (DOY), time to universal time (UT), relative humidity (RH), temperature (T) and pressure (P) are considered as inputs of LS-SVR model. And the PWV corresponding to these seven parameters is the output of the model. After the training step, the PWV value was estimated with the trained model and compared with the PWV values obtained from the radiosonde station, the empirical model of Saastamoinen and GPT3, the support vector regression model (SVR) and the radial basis neural network model (RBNN) in the control stations. Statistical indices of relative error, correlation coefficient and root mean square error (RMSE) have been used to evaluate the accuracy of the models. The conducted analyzes show that the average RMSE of RBNN, SVR, LS-SVR, GPT3 and Saastamoinen models in 3 control stations is to 4. 92, 4. 13, 2. 87, 4. 22 and 4. 29 mm, respectively. Also, the average relative error of the models in 3 control stations is calculated as 38. 06, 30. 77, 22. 37, 34. 63 and 32. 80% respectively. Analysis of the PPP method shows an improvement of 33 mm in the coordinate components using the LS-SVR model. The results of this thesis show that the LS-SVR model can be considered as an alternative to the empirical troposphere models in the studied area. The LS-SVR model is a local troposphere model with high accuracy.

Background: Albeit coronary artery angiography is the gold standard of the diagnosis of coronary artery disease (CAD), coronary artery calcification (CAC) is a less invasive diagnostic method. We evaluated pulsed-wave velocity (PWV) as another accessible diagnostic tool to detect early CAD in the Iranian population. Methods: From March 2016 to March 2017, we enrolled 350 patients referred for an evaluation of CAD to Rajaie Cardiovascular, Medical, and Research Center (Tehran, Iran). The patients underwent coronary artery computed tomography angiography, and their CAC scores were measured simultaneously. The PWV index was defined as the distance between the brachial and dorsalis pedis arteries divided by time, and the correlations between the PWV index and the CAC score and known CAD risk factors were assessed. Results: From 350 patients, 52. 3% were men and the rest were women. The mean PWV was 8. 7 ± 2. 2 m/s and the mean CAC score was 251 ± 99. 52. There was no significant relationship between the CAC score and the PWV index (P = 0. 16). In the women, the CAC score and the PWV index were meaningfully higher (P ≤ 0. 001 and P < 0. 04, respectively). The CAC score was significantly different between the patients with and without CAD (P <0. 001), whereas there was no difference concerning the PWV index (P = 0. 31). Among all CAD risk factors, hypertension and diabetes mellitus were significantly correlated with the CAC score (P = 0. 001 and P = 0. 015, correspondingly) and the PWV index (P = 0. 001 and P = 0. 009, respectively). Conclusions: In contrast to some recent studies that have shown a significant increase in the PWV index in relation to the CAC score, our results did not prove it. The PWV index, thus, needs further studies if it is to be fully utilized in clinical practice.

Water vapor is the dominant greenhouse gas in the Earth’s atmosphere and, at the same time, highly variable in the atmosphere. Observations of its spatial and temporal variations is a major objective of climate. It is important in several major areas in the atmospheric sciences, on scales from turbulence to synoptic-scale systems, and including cloud formation and maintenance, radiation and climate. The intent of this paper is to demonstrate the ability of MODIS PWV products for use in monthly and daily variability of climatological scales over Iran. Therefore, the results are presented in two sections. The first section compares the long term (2003-2015) Monthly mean MODIS Level 3 and ERA-Interim PWV data sets. The second section validates the level 2 MODIS PWV products by Radiosonde daily data. For a better comparison of MODIS level 2 PWV products with Radiosonde data, we used data from 10 Radiosonde stations over Iran. We consider the sky conditions (cloudiness and visibility) in our comparison. There are no microwave radiometers (MWR) and Global Positioning System (GPS) sites in Iran hence, in the absence of these data, we used the measurements of Radiosonde and ERA-Interim as reference data for the comparison of the MODIS PWV estimates. These data were obtained at monthly and daily scales. In the first section, long-term (2003-2015) spatial and temporal characteristics of monthly mean PWV are investigated over Iran. For this, Level-3 MODIS terra (MOD08_M3) products and ERA-Interim data were obtained with the 1-degree resolution for Iran. In the second section, January (as a month with low values of PWV and unstable atmosphere) of 2004 and July (as a month with high values of PWV and Stable atmosphere) of 2008 were selected for comparison of MODIS daily (MOD05-L2) PWV product with Radiosonde data for 10 Radiosonde stations in Iran. The average annual MODIS and ERA-Interim PWV data are 12. 248 and 12. 243 mm, respectively. These values are very close to each other. These values are also close to those derived by Asakereh et al. (2015) from NCEP data reanalysis (about 14. 3 mm). Also, Ferencz and Pongra (2008) concluded that the ERA-Interim and the MODIS PWV fields are very similar. The maximum and minimum values of PWV for both data sets is observed during July and January, respectively. Tuller (1968) indicated that February and July are the months of highest and lowest precipitable water at most stations. At some, August replaces July, and at a smaller number, January replaces February. Also, our result is the same with the study of Maghrabi & Dajani, (2014) over Saudi Arabia. They reported that the lowest PWV values were in December and January, whereas the highest values were in June and July. They pointed that during warm periods, increases in the temperature and height of constant pressure levels result in an increased capacity for water vapor of the air mass, keeping it away from the saturation point and consequently preserving high PWV values. In contrast, in cold periods, the decrease in the height of constant-pressure levels, reduce the capacity for water vapor of the air mass and facilitates the condensation process, resulting in a decrease in the amount of PWV. The topography is a key factor in the spatial distribution of PWV. PWV from both data sets has a significant negative relationship with the distribution of topography in all months. This means that the concentration of PWV is high in the highland regions and vice versa. During January 2004, the ranges of errors are in the best case 5. 53 mm (Tabriz) and in the worst case (Ahwaz) 16. 02 mm. In all stations, the coefficient of determinations are negligible. While in the suitable weather condition, RMSE is decreased in all stations. During July of 2008 at many stations such as Zahedan, Kerman and Esfahan cloud cover and visibility condition have been appropriate, while in Bandar Abbas in all days the visibility was poor (less than 5 KM). It seems that the cloud cover and visibility conditions result in the high coefficient of determinations in Esfahan, Kerman and Zahedan (77, 80 and 66%, respectively) and with high error in Bandar Abbas station. Annual average MODIS PWV and ERA-Interim are close to each other (12. 24), in addition, MODIS has a higher negative correlation coefficient with topography compared to ERA-Interim PWV data. This suggests that MODIS level-3 monthly PWV data are valuable for the monthly long-term climatology of PWV over Iran. In daily scale, a comparison of MODIS and radiosonde PWV data in different atmospheric conditions are significantly different. During clear days with appropriate visibility (despite the time lag between two data sets) values of R2 is higher compared to cloudy days with poor visibility. Hence, accuracy of the MODIS PWV data over Iran is strongly dependent on weather conditions.

Introduction: Water vapor, as one of the most important greenhouse gases in the atmosphere, plays a key role in hydrological cycles, climate change, and the global climate. Many parameters for the expression of water vapor in the atmosphere have been proposed by meteorologists, one of which is Precipitable Water Vapor (PWV). There are many ground-based and space-based methods to measure PWV. Meanwhile, radiosonde is considered as one of the most common and traditional tools for measuring this parameter. However, low temporal resolution, high cost, and lack of uniform coverage across the globe are some of the limitations of this technique. In the last two decades, GPS Meteorology due to unique features such as usability in any weather conditions, long-term stability, continuous observations with very high resolution, low cost, and PWV estimation with an accuracy level of about 2 millimeters has received a lot of attention. Although radiosonde and GPS are precise methods for estimating water vapor in the atmosphere, their observations are limited to the land. While satellite remote sensing methods can provide continuous observations of the distribution of water vapor on a regional and global scale. MODIS is one of the sensors capable of measuring atmospheric water vapor measurements, which is onboard the Terra and Aqua satellites. However, PWV products obtained from remote sensing data should be evaluated with respect to the reliable in situ data before application. The main purpose of this study was to use PWV estimates obtained from ground-based GPS receivers in order to statistically evaluate the accuracy of MODIS water vapor products in IR and Near-IR bands and different times of the day over Iran. Materials and Methods: The MODIS sensor, which is on board of the Terra and Aqua satellites, is able to provide water vapor products in the IR (both night and day) and Near-IR (day-only) bands. In order to evaluate MODIS PWV products over Iran, one year data of high temporal resolution GPS PWV values in 38 different stations in the country were considered as reliable values. For statistical analysis, water vapor values were extracted from the pixels with cloud-free conditions. Also, among the cloud-free pixels, that with the closest distance to the GPS station was selected. Moreover, the corresponding PWV values of GPS and MODIS with a maximum time difference of 10 minutes were selected for comparison. Results and Discussion: Initially, Near-IR PWV products were assessed separately for Terra and Aqua satellite data. The results showed a good agreement between the two sets of PWV measurements. The correlation values between the GPS PWV and the corresponding values of the MODIS Near-IR products varied in the range of 0. 90 to 0. 98. Average bias values indicated that MODIS Near-IR overestimated PWV in comparison with GPS over Iran. In addition, a comparison of Near-IR water vapor values extracted from Terra and Aqua datasets separately showed that the data quality of both satellites in this band is almost at the same level in terms of the correlation coefficient, average bias, and RMSE. In the next step, the MODIS IR PWV products were evaluated separately during the day and night with respect to the corresponding values obtained at the GPS stations. The maximum correlation between GPS and IR PWV products during the day and night was 0. 7 and 0. 64, respectively. Furthermore, the average bias of MODIS IR PWV data in the study area for day and night was found to be-0. 38 and 3. 11 mm, respectively. In other words, MODIS IR PWV products in the study area had, on average, a positive bias with a small amount during the day and a significant negative bias during the night. On the other hand, a comparison of daytime MODIS IR and Near-IR water vapor products revealed that the quality of IR PWV data was significantly lower than the Near-IR band and requires a suitable calibration method. Conclusion: The results of this study indicate that the MODIS Near-IR water vapor products had a high agreement with GPS PWV values with an average correlation coefficient of 0. 95 in the study region. The mean bias and RMSE error of (GPS-MODIS Near-IR) PWV differences were-2. 2 and 3. 3 mm, respectively. A similar analysis of MODIS Near-IR PWV data from the Terra and Aqua satellites showed that almost both sets of water vapor data had the same accuracy. The average bias values of the MODIS IR PWV data compared to the GPS PWV for day and night were also investigated. Results showed that in the study area, MODIS IR products had a small positive bias during the day and significant negative bias at night. Examining the efficiency of the daytime MODIS water vapor products during the day, we found that the accuracy and precision of these data in the Near-IR band are much better than the IR band. Therefore, proper calibration should be made before employing the IR band.