IntroductionThere are significant differences in the spatial distribution of the Iran annual precipitation. This is resulted from spatial behavior of precipitation in the one hand and variation in the sources of precipitation on the other. The lack of adequate distribution of meteorological stations and the unavailability of long-term statistics of precipitation makes the analysis of precipitation more complicated. Precipitation Data are constant inputs of research and the models related to water resources (e.g., climate, agriculture, hydrology, and environment). Most of research institutions are used to record the Data and present it to different users. Different ways of interpolation of the Data will cause different results. Therefore, it is a critical step to select the appropriate Data based on research design. This study evaluates APHRODIATE, GPCC and Delaware University precipitation Data (UDel) based on precipitation stations using RMSE, R2 and Taylor diagram techniques.

Statement of the problem: Thunderstorm is a destructive atmospheric phenomenon, which annually causes a lot of damage to various parts of human activities. Due to the accompaniment of thunderstorm with rainstorm and hail and its effective role in creating sudden floods, the analysis of the behavior of this hazard has been widely studied both in terms of agriculture and in terms of financial and life damages throughout the world. The study of thunderstorm as a hazardous atmospheric phenomenon using instability indexes in Iran has been less considered due to lack of observation stations. Convective Available Potential Energy (CAPE) and Vertical Wind Shear (VWS) are two indexes that are often used to describe and detect thunderstorm environments. This study evaluates the thunderstorms in Iran with Reanalysis Data using CAPE and VWS indexes.Research Methodology: Thunderstorm Data in 7 different conditions at 8 times a day for 42 synoptic and upper air stations during a 37-year common period (1980-2016) was received from the Iranian Meteorological Organization. At first, frequency, trend and time of occurrence of thunderstorms in Iran were investigated during the statistical period. Then, the ERA-Interim Reanalysis Dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) with spatial resolution of 0.5 ° was used for the analysis of thunderstorms. To evaluate the ERA-Interim Dataset, the CAPE and VWS values for the 80 selected thunderstorm events that were calculated using the RAOB software were compared with ERA Data and their accuracy was confirmed. After confirming the accuracy of ERA Data, the average values of CAPE and VWS indexes in 42 stations of the country were calculated based on 4, 542 thunderstorm events at 00 and 12 GMT during the study period, and the maps of these two indexes were drawn up using the IDW method. Then, using an equation, the thunderstorm severity thresholds across the country were determined using ERA Data with 4, 542 thunderstorm events to distinguish between mild, severe and very severe storms. To ensure the selection of important storms, storms with CAPE values of less than 50 were removed to exclude poor environments for convection occurrence. As a result, out of 4, 542 thunderstorms, 535 events were eliminated and 4007 events remained. On this basis, a "2 x 2 contingency table" was prepared that compares thunderstorm events and forecasts. This table provides the information required to compute warning performance statistics including POD (Probability of Detection), FAR (False Alarm Ratio) and CSI (Critical Success Index). But the results of these statistics did not match well with the conditions of thunderstorm events in Iran. Therefore, the discriminant analysis was used to differentiate the intensity of thunderstorms and to discriminate mild, severe and extremely severe thunderstorms. Explain and interpret the results: The results of the study showed that thunderstorms in Iran are increasing during the statistical period with a regression slope of 0.23 events per year (8.5 events in the statistical period). The highest frequency of thunderstorms was observed in the month of May with an annual number of 111, and the lowest was observed in January with 12 events. Most thunderstorms occur around 21: 30. The highest average frequency of annual events at stations was related to the stations of Urmia, Tabriz, Khorramabad and Bushehr respectively. The proper capability of ERA Data to estimate instability indexes in Iran was proved. ERA Data provides a very near estimate for VWS, but estimates for the CAPE index are slightly more than observational values. The highest values of the CAPE index are observed in southern provinces, as well as in the southwest of the Caspian Sea coasts, and the highest values of the VWS index are found on the Persian Gulf coasts. When the storm severity breakdown equation for the 400 selected storm events was obtained and the "2 x 2 contingency table" was prepared, it was found that this equation was not satisfactory with respect to the POD, FAR, and CSI indexes. Hence, using the discriminant analysis, the storm severity breakdown relationships and their discriminant equations were obtained. These equations categorized 60% of the surveyed thunderstorms correctly. There is no significant difference between the mean values of CAPE and VWS in the three storm intensity groups. The role of the VWS index was higher in determining the type of storm.

Introduction Among climate variables, wind has a skewing role due to its high spatio-temporal variability and its role in other parameters such as air temperature. It is important to study wind changes in different ways so as wind speed decreases its energy and consequently increases urban pollution. Reducing the wind speed also reduces heat transfer, viscosity between the earth's surface and the atmosphere, and ultimately increases the temperature. Decreasing wind speeds at night, especially in winter nights, cause the Earth to radiate inversion. Increasing wind speeds will also result in high winds, tornadoes and damage to affected areas. Also, wind speed is one of the important components in combinatorial equations to estimate evapotranspiration and any trends in wind speed will also affect the water requirement of plants. As discussed, wind is a very important climatic parameter, but its study in particular is changing its course with limitations such as inaccessibility of homogeneous time series and long term Data with inadequate stations. Station Data, on the other hand, can also be affected by discontinuities associated with changes in measuring equipment, station location, or different measurement methods. To overcome these limitations, the re-analyzed global meteorological Dataset, available for a long period, is useful for meteorological studies. In recent years, these Databases have also been used for various wind energy applications. The purpose of this study was to evaluate wind speed changes and trends in central Iran and since most of the area is arid and insufficient stations, ECMWF Database Data were used. The results of this study can be useful for studies on climate change, agriculture and renewable energies. Research Methodology The study area is Central Iran. Central Iran is said to be bounded on the north by the Alborz Mountains, on the west and south by the Zagros Mountains, and on the east by the dispersed Khorasan Mountains. Much of central Iran has warm and dry climates that are milder and humid in the highlands. In this study, four provinces of central Iran were selected and evaluated for wind changes. Two groups of Data were used in this study. 1-Wind speed Data from Synoptic stations and 2-Wind speed Data from ECMWF ERA-Interim version with spatial resolution of 0. 75 × 0. 75 °, . Kolmogorov-Smirnov (K –,S) test confirmed the normality of the Data and the missing Data were reconstructed using linear interpolation method. Synoptic station Data were also used to validate the ERA-Interim ECMWF Database Data. Coefficient of determination (R2), Mean bias error (MBE) and root-mean-square error (RMSE) of open Data analysis of ECMWF Database ERA-Interim were used for wind speed trend in central Iran with nonparametric Mann-Kendall test. Was evaluated. Results and discussion Minimum wind clock speeds are only less than 2 meters in November (1. 98) and December (1. 96). In other months this fluctuates between 2. 01 and 2. 59 meters. The maximum wind speeds were also between 3. 43 and 5. 90 meters, respectively, from November to July, respectively. During the warmer months of June (Jun, July and August) the maximum wind speed is more than 5 meters. The average wind speed is also presented in this table, based on the results of the long-term minimum wind speed in central Iran with a mean of 2. 83 meters in January and its maximum with a value of 3. 95 meters in July. On this basis, it can be said that during the cold period of the year in central Iran, the wind speed is slower, as the hot months of the year ahead, the wind speed will increase. The average annual wind speed was 3. 19 meters. Among the seasons and months studied, winter showed the highest intensity of the trend of increasing wind speed (Z-score of 4. 916 Mann-Kendall test), which is significant at 99% level. The focus of the maximum wind speed increase trend is in Semnan province, and as we move from January to March, the intensity of the trend increases. The highest percentage of incremental trend zones is in February, with 92. 20% of central Iran showing an increasing trend of wind speed this month. June with 80. 52% of the upward trend zones after March accounted for most of the areas with upward wind speeds in the spring. In contrast to the upward trend zones that peaked in January but the maximum upward trend intensities in April reached the Mann-Kendall Z test score of 4. 031, which was statistically significant at 99%. Conclusion The results showed that the ECMWF Database is well suited for wind clock evaluation. The Shahroud, Yazd and Kerman stations showed maximum coefficient of determination (R2) and minimum error. Yazd and Kerman also showed less deviation from synoptic stations. Minimum wind speeds in November and December and maximum wind speeds were calculated in July and June. The mean wind speed was calculated based on the ECMWF results of 19. 1 m / s. The average wind speed in central Iran is directly related to the air temperature and season. Generally, during the cold season of the year the wind speed from south to north and during the warm season from north to south of central Iran is increased due to the location of arid regions such as Dasht-e Kavir in the north and Dasht-e Lut in the south of the study area. The trend of the wind clock in central Iran has shown that the maximum intensity of the trend of increasing wind speed is in the winter of March (Mann-Kendall Z test score of 4. 916) which is significant at 99% level. Also, the maximum decreasing trend with the Z-score of Man-Kendall test is-2. 73 in December. The upward trend of wind speeds in more than 50 percent of central Iran in 10 months of the year, while only in October and February, is the decline observed in more than 50 percent of the study area. Since the most important factor in reducing or increasing wind speed is pressure gradient changes, wind speed variations can be a sign of climate change.

General circulation models (GCMS) are only tools to predict future climate condition under climate change scenarios. An outstanding issue with the use of GCM output for regional and local application is the coarse spatial resolution. So there are various methods to predict, climate variables at regional, local and a station scale that all these methods are known downscaling. The Statistical Downscaling Model (SDSM) is one of the most used methods that identifies relationships between variable predictors (output GCM) and variable predictands (Temperature, precipitation, etc. in particular station) using multiple linear regression. Intergovernmental Panel on Climate Change (IPCC) has determined two periods (1961-1990) and (1971-2000) as baseline to compare the effects of climate change in future periods. As well as Reanalysis Data, that produced by the National Center for Environmental Prediction (NCEP) are important components for the structuring of the SDSM as they supply the predictor values for the calibration and validation of the model. Type and period of Reanalysis Data can be effective in model accuracy. In this study for downscaling temperature and precipitation variables the sensitivity of the SDSM model was examined to type and Reanalysis dada of NCEP in Kerman meteorological station. The mean absolute error (MAE) was used to determine the sensitivity of the model. Result showed that the model is sensitivity to both type and base period Reanalysis Data. The mean absolute error of the Reanalysis CGCM model Data, for the average maximum, minimum and mean temperature variables equal to 11, 4.5 and 4.7 times the case that theHadCM3 model Data is used respectively. In the case of the base period, when Data of (1961-1990) is used, MAE for the mentioned variables and daily precipitation equal to 3.5, 1.4, 3.5 and 1.4 times that the state which is used for the base period (2000-1970), respectively.

Cloudiness is one of the most important climate elements that plays a major role in the rainfall and water balance of any region. In addition, the radiation budget of each region is largely controlled by the cloudiness and cloud characteristics of each region. Knowledge of the temporal and spatial structure of clouds in each region plays an essential role in planning and checking programs based on water and energy resources. The main goal of this research is to analyze the temporal-spatial structure of cloudiness in Khuzestan province using gridded Data of ECMWF cloud Database version ERA5 in Khuzestan province during the statistical period of 1990-2020. The cloud Data for Khuzestan province in NC format was obtained monthly from the Copernicus integrated climate Database, which included the height of the base or cloud floor, the percentage of sky cloud cover by the upper, middle and lower clouds of each pixel was 0.25 degrees of arc. . By calling the mentioned Data in the ARC-GIS environment and spatial averaging by separating the cold and warm periods of the year, 30-year average maps of the warm and cold periods of the year were produced for the two factors of total cloudiness and cloud base height. On the other hand, the sky cloud percentage values were also calculated through spatial averaging in Khuzestan province for each year (1990-2020). The results showed that, firstly, in both hot and cold periods of the year, the northeastern and mountainous parts of the province had higher cloud cover than the plains and lowlands of the province. In the cold period of the year, the percentage of cloudiness of the sky varied between 25 and 60%, while in the warm period of the year, the percentage of sky cloudiness reached less than 5%. In addition, it was observed that in the province of Khuzestan, the highest share of cloudiness in the cold period of the year is related to the upper clouds (cirrus family including cirrus, cirrustratus and cirrocumulus with a height of more than 4 thousand meters). These clouds have a share of 25% in the cloud coverage of Khuzestan province during the cold period of the year. Meanwhile, middle clouds (alto family clouds including altocumulus and altostratus) have a 16% share in the cloud cover of the sky of Khuzestan province. In terms of the cloudiness time series trend during the period of 1990-2020, despite significant annual changes in the amount of cloudiness of the province's sky, no significant trend was observed in the changes of cloudiness in Khuzestan province during the last three decades.

A comparative study of quantitative mapping methods for bias correction of ERA5 Reanalysis precipitation Data Kaveh Bapirzadeh1, Hesam Seyed kaboli*2, Leila Najafi3 1 MSc student, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. *2 Associate Professor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Corresponding Author: Email: hkaboli@jsu.ac.ir 3 Instructor, Department of Civil Engineering, Jundi-Shapur University of Technology, Dezful, Iran. Abstract This study evaluates the ability of different quantitative mapping (QM) methods as a bias correction technique for ERA5 Reanalysis precipitation Data. Climate type and geographical location can affect the performance of the bias correction method due to differences in precipitation characteristics. For this purpose, ERA5 Reanalysis precipitation Data for the years 1989-2019 for 10 selected synoptic stations in climates with different topographic characteristics were received daily from the European Centre for Medium-Range Weather Forecasts (ECMWF) website. Bias correction of these Data was performed using 5 quantitative mapping methods based on observational Data in R software environment. Two-part evaluation and Taylor diagram were used to compare the performance of different methods. The results showed that the performance of the quantification mapping method depends on the performance functions, set of parameters and climatic conditions. In general, non-parametric methods of multiple mapping have better performance than parametric methods, so that the best performance is related to QUANT and RQUANT methods, among which DIST method has the weakest performance. Keywords: Quantitative mapping, Bias correction, ERA5, ECMWF

The variety of produced variables at the surface and atmospheric pressure levels, appropriate resolution, and global coverage of the ERA5 Reanalysis Data have led its consideration both in numerous climate research studies and for predicting atmospheric parameters. The initial step in using Reanalysis Data involves its verification using observational Data. Despite the scattered nature of measuring stations, observational Data remains a practical Dataset for this purpose, particularly in investigating thunderstorms. In this research, we have verified the accuracy of ERA5 Reanalysis Data in estimating of two convective parameters: the Lifted Index (LI) and Vertical Wind Shear (WVSH). To achieve this, we analyzed radiosonde Data from nine stations across Iran (including Tabriz, Mashhad, Tehran, Kermanshah, Isfahan, Ahvaz, Kerman, Shiraz and Zahedan stations) in the period of 1990-2020. Statistical indicators were employed for comparison between the Reanalysis Data and observational Data. Several constraints were applied to the Data. For instance, both temperature and dew point profiles should be measured simultaneously. Profiles that terminated below the 6 km above the ground or provided Data at fewer than 10 pressure levels were excluded. Additionally, some constrains were utilized to quality control wind and temperature gradients. Specifically: (I) Profiles were removed if the lapse rate in the mid-troposphere exceeded 9 K/km, (II) Profiles were excluded if the lapse rate in the low-troposphere exceeded 11 K/km, (III) Profiles were discarded if VWSH-1000 exceeded 35 m/s, (IV) Profiles were omitted if VWSH-3000 exceeded 45 m/s and (V) Profiles were removed if VWSH-6000 exceeded 70 m/s.The VWSH was calculated across three layers at altitudes of 1000, 3000 and 6000 meters from the surface. Investigations were conducted on daily, monthly, seasonal and long-term time scales. On a monthly scale, the minimum (maximum) root mean square errors (RMSE) for VWSH-1000, VWSH-3000, and VWSH-6000 were approximately 3 (8.5), 3.36 (9.84), and 4 (20) m/s, respectively. The results showed that the ERA5 Reanalysis Data consistently underestimated the value of VWSH-1000 across all stations (except Ahvaz station in recent years). The estimation of VWSH-3000 and VWSH-6000 parameters exhibited both overestimation and underestimation in different months. Notably, the highest error in ERA5 Data for VWSH-6000 occurred during January. Across most stations, the largest errors were observed during cold months (particularly for the VWSH-6000 parameter), while the smallest errors occurred during warm months. In conclusion, the results suggest that as the height of the investigated layer increases, the performance of ERA5 in generating the considered VWSH parameters at the stations improves, especially in recent years.A comparison between Reanalysis-LI and observational-LI indicatedes that the highest (lowest) error occurs during warm (cold) months of the year. Throughout the study period, the Reanalysis Data produced an error of at least 10 K (at Zahedan station) and up to 15 K (at Tehran station) in LI estimation. Except for Ahvaz station, LI was consistently underestimated across all stations. The monthly mean of Reanalysis-LI reflected more unstable conditions, whereas the observed values indicated a more stable atmosphere. Consequently, Reanalysis-LI may not be a suitable metric for distinguishing stability and instability in the considered stations. However, in Mashhad and Tehran stations, there were consistencies between the trend of annual average values from Reanalysis and observational Data. In other stations, this agreement becomes evident in recent years. However, in some stations, the annual average value of Reanalysis LI has overcome the observations, while in others, it is the opposite.

The monitoring and analyzing of drought conditions is one of the main requirements for water resources management. In present paper, Standard Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were compared in order to assess the drought conditions in the Esfarayen-Sabzevar region. As in the aforementioned region sufficient long-term Data is not available to assess the drought, Reanalysis Data of ERA-Interim were used to be combined with the observed Data. For this purpose, climatic Data of precipitation and temperature were extracted for each station in the statistical period of 1979-2016 using the web interfaces, Python script, ECMWF WebAPI and ArcGIS software. After correcting the bias of the Data based on observational Data, combined Data of precipitation and temperature were obtained for the aforementioned period and used as the basis for calculating the drought. Finally, drought assessment and estimating the correlation of SPI and SPEI were conducted for three stations of Sarcheshme, Ghasemabad, and Jaghtay in the time scales of 3, 6, 12, 18, and 24 months. After generating Data combination, the Root Mean Square Error (RMSE) and Bias were decreased from 0. 39 and 6. 69 to 0. 32 and 0. 24 respectively. Thus similar approach to Data can be used for drought assessment in areas with lack of observed Data. Results showed that in the short-term scales the frequency of dry and wet periods is high. By increasing the time scale, the frequency of the dry and wet period decreases but their duration increases. In most cases in the stations both indices showed coordination in the dry and wet periods. Based on the provided results regarding the correlation between SPI and SPEI indices, there is a positive and significant correlation between the above indices and the correlation is higher in the humid regions. As a result, the SPI index can be used in the regions with no temperature Data with a precession similar to the SPEI index.

The current research was conducted to evaluate the effectiveness of the Reanalysis Data of Era-interim, Agera5, and station Data to simulate runoff using the Sacramento model in the Shapur watershed. Focusing on an innovative approach, this study provides solutions for integrating Reanalysis Data into runoff modeling that can improve the flexibility and accuracy of hydrological models. The research Data were analyzed by statistically comparing the simulated discharge with the observed discharge at the watershed outlet on a daily time scale. The results showed that the discharge simulated by the station precipitation Data with a correlation coefficient of 0.93 with the observed discharge performs better than the discharge simulated by the Reanalysis Data. Also, among the Reanalysis Data, the Agera5 Data with a correlation coefficient of 0.82 performs better than the Era-interim Data. The results of Era-interim Data show an underestimation in the amount of precipitation, which is because it is located in the Shapur watershed (the coastline bordering the Oman Sea and the Persian Gulf). Also, in the review of the long-term monthly Data, the Reanalysis Data in the hot months of Dubai has not been correctly simulated, which is due to the durability and low thickness of the clouds, and this has caused a decrease in the accuracy of the amount of precipitation. Finally, due to the proper distribution of rain gauge stations in this basin, the results obtained from the station Data are better than the Reanalysis Data.

The lack of required Datasets over the catchments impose basic problems for applying hydrologic models. By increasing the satellite-based technologies over the past decades, several water balance components have been developed by using remote sensing and Data assimilation techniques. This research addresses the efficiency of evapotranspiration values which are obtained from the Reanalysis models (GLEAM, W3RA and HBV) for calibration of VIC-3L hydrologic model over the SefidRood basin (SRB). Results showed that using the evapotranspiration Dataset, which is estimated by GLEAM, is the best for calibration of VIC-3L (NS=0. 56 and CC=0. 80) and simulating the streamflow at the outlet of SRB. Also, using the HBV Reanalysis model’ s results, in both daily and monthly time scales, with the KGE=0. 64 leads to the better performance in simulating streamflow when comparing to the base scenario (calibration of VIC-3L model with the observed streamflow Data). Finally, results indicated that using W3RA and GLEAM Datasets improved the VIC-3L performance in estimation of runoff volume and the maximum relative error restricted to only 4. 0 %.