Climatology
Hashem Rostamzadeh; Saied Jahanbakhsh asl; Mir kamel Hosseini; Mohammad Omidfar
Abstract
AbstractChanges in the incidental behaviors are among the most important aspects of global climate change with significant consequences on human society and the environment. Monitoring and measuring heavy rainfall events are important for understanding the nature of severe weather fundamentals and future ...
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AbstractChanges in the incidental behaviors are among the most important aspects of global climate change with significant consequences on human society and the environment. Monitoring and measuring heavy rainfall events are important for understanding the nature of severe weather fundamentals and future assessment. In this study, Global Precipitation Measurement (GPM) experiments with ground station data were performed at 20 synoptic stations for intense daily detection (25 mm and above) of precipitation over an 8-year period (2021-2014). Statistics such as coefficient of determination (R2), correlation coefficient (R) and root mean square error (RMSE) were used to compare and evaluate the observational and satellite data. Comparison of the maps obtained from GPM satellites and ground stations showed that the spatial distribution of precipitation from two similar bases is the same and the low and high rainfall areas correspond to the region. GPM satellite detected precipitation zones well so that the spatial correlation coefficient between GPM satellite and observed was 0.81. The results of the ANOVA test between the observational data and the GPM satellites showed that due to the low significance level of p-value of 0.000, the assumption that the average precipitation is the same between the two databases is rejected. There is a significant relationship between the average precipitation at ground and satellite stations. Also, the results of Kolmogorov-Smirnov test showed that since the obtained p-value (0.819) is a number higher than the error value of the test (0.05), so the null hypothesis based on the equality of precipitation values recorded at ground stations and modeled are the same and the null hypothesis is confirmed.
Yousef Zarei; Ali Mohammad Khorshiddoust; Majid Rezaeebanafshe; Hashem Rostamzadeh
Abstract
Climate change is one of the main problems on Earth today, so predicting these changes in the future and their impacts on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of ...
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Climate change is one of the main problems on Earth today, so predicting these changes in the future and their impacts on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of global climate change on different climatic regions of the country were studied in 12 climatic regions. In this study, NCEP data and climatic elements of precipitation, maximum and minimum temperature were used for statistical downscaling with SDSM model. And using the CanEMS2 model output under RCP scenarios for the three statistical periods of 2011-2040, 2041-2070, and 2071-2099 annual climate change data were obtained. Correlation coefficient, determination coefficient and error indexes of RMSE, MSE and MAD were used to evaluate the performance of the model. However, the results showed that the accuracy of the model was different at different stations. In this way, each model performs better than rainfall in simulating minimum and maximum temperatures. The annual long-run results also show that precipitation will decrease in all climates studied in the coming decades, with the largest decrease occurring in semi-warm (35%) and very humid and temperate (32%) desert areas. But minimum and maximum temperature variations will be different in different climatic regions so that under RCP scenarios during all statistical periods at Sabzevar and Tabas stations minimum temperature changes will decrease but in other climatic regions the trend of minimum and maximum temperatures will be incremental. The highest minimum and maximum temperature increases based on RCP scenarios under RCP8.5 scenario during the period 2071-2099 in the cold mountain climatic region will be 3.03, 4.27 ° C, respectively.
Climatology
Behrooz Sarisarraf; Hashem Rostamzadeh; Mohamad Darand; Omid Eskandari
Abstract
Precipitation is one of the most important and variable climatic elements that changes in time and place. Critical rainfall at various time scales, especially daily, causes severe damage to human communities in densely populated urban areas and natural ecosystems and affects many arid economies. Earth ...
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Precipitation is one of the most important and variable climatic elements that changes in time and place. Critical rainfall at various time scales, especially daily, causes severe damage to human communities in densely populated urban areas and natural ecosystems and affects many arid economies. Earth outgoing long-wave radiation is studied as a significant parameter to detect clouds and estimate this type of precipitation. The current study aims to examine the relationship and analysis of outgoing long-wave radiation variables and precipitation values in Arc GIS software environment for the four cold months 17 statistical years in Iran using AIRS sensor products of Aqua satellite and GPM satellite. Correlation and regression models and confidence interval estimation were used to measure the correlation of long-wave radiation output in predicting precipitation patterns and their changes. According to the results obtained in all months studied, In the whole country, except Caspian Sea basin in January, parts of the central and eastern plateau of eastern Iran, there is a negative correlation of 10 to 92%, Which indicates that the country's atmosphere is humid and prevents the release of outgoing long-wave radiation. In the western rainfall areas of the Zagros Mountains, negative correlations above 70% and outgoing long-wave radiation is less than 260 W⋅m−2 which is due to cloudy and humid atmosphere with precipitation.In December and February, the rainfall areas north of the Caspian Sea basin range have negative correlations of above 50% and OLR less than 235 W⋅m−2 of rainfall and the reason for the lower numerical value north of the Alborz mountain range to predict is the existence of high relative humidity in the region, which is the cause of less outgoing long-wave radiation output of the earth.
Climatology
Naser Jafarbegloo; Ali Mohammad khorshiddoust; majid rezaei banafsheh; Hashem Rostamzadeh
Abstract
INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital ...
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INTRODUCTION
Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital determining factor in the distribution of plant species on the planet, can be both a limiting factor and a factor in the spread and species distribution (Rodrigo, 2000: 155). Therefore, in this study, we examined the changes in minimum temperatures in the statistical period (1980-2010) and predicted these changes in the 2050s (2065-2046) in the Northwest of the country using the LARS-WG microscale method and model output. Atmospheric pairings of HadCM3 and MPEH5 were addressed. The prediction of minimum temperature variations to determine the extent of its future changes and considering the necessary measures to minimize the adverse effects of climate change on agricultural products were of great importance. In this regard, general atmospheric circulation models (GCMs) are designed that can simulate climatic parameters.
DATA AND METHODS
In the present study, the output data of two HadCM3 and MPEH5 general circulation models based on two scenarios A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in the Northwest of the country. The results were monthly and periodic on the base period (1980-1999) and the 2050s (2046-2065), thereby the minimum temperature was evaluated and analyzed. In assessing the LARS-WG model, the observational and simulation error data were evaluated using MSE, RMSE, MAE and R2, and the model was evaluated for the appropriate region. The results showed that the minimum temperature in the future period will increase compared to the base period in the study area. This increase in air temperature at the study area is based on the HadCM3 and MPEH5 models, on average, 1.9 and 1.7 degrees Celsius to 2065 horizons compared to the base period. The north-eastern part of the northwestern region of Iran will have higher temperatures than the semi-southern regions. In fact, the cooler regions of the high latitudes will face more incremental changes in the amount of minimum temperatures. The results and achievements of this research are important for long-term plans for adaptive measures in the management of fruit gardens, agricultural products and water resources management. In order to calibrate and ensure the accuracy of the LARS-WG microscale model, the model was first implemented for the basic statistical period (1980-2010); then the minimum temperature output and its standard deviation were compared with the observational data of the studied stations, which indicated a small difference between the observed and simulated values and also deviated from their criteria.
RESULTS AND DISCUSSION
The results of evaluation of observational and simulated data by LARS-WG microscale model using RMSE, MSE and MAE error measurement indices for the studied stations indicate that there is a significant difference between the simulated values and the values of the observed observations. There is no critical 0.05 significance levels, and Pearson correlation values between simulated and real data are acceptable at the significance level of 0.01. The obtained results show that the accuracy of the model varies in different stations. In general, the results of error measurement indices indicate that the LARS-WG model is of good accuracy for micro-scaling the parameters under study. In order to better represent and ensure the accuracy of the prediction as well as to investigate the uncertainties in the studied models, the simulated values were compared and observations were made on a long-term average during the base period in the studied stations using comparative graphs. As can be seen, the observed and generated values in the base period at all stations are very close to each other and the LARS-WG model has been successful in simulating the studied parameter. After evaluating the LARS-WG model and ensuring its suitability, the data predicted by the model for two scenarios A2 and B1 using HadCM3 and MPEH5 models and were examined on a monthly and long-term basis. The study of the status of minimum temperature changes of the studied stations in the future period (2065-2056) shows that the minimum temperature is based on both scenarios and in all months and stations compared to the period, the base has increased. Due to the large number of study stations, only stations located in provincial centers of this study are listed.
CONCLUSION
Cold and frost are one of the most significant climatic hazards on fruit trees. This type of climate risk affects different parts every year, especially the cold regions of the northwest of the country. Studies show that in recent years, the rate of economic damage to fruit trees in this region has increased, so in this study, the outlook for changes in minimum temperatures in this region using the LARS-WG statistical microscale model and output two HadCM3 global model and MPEH5 were introduced in the 2050s (2065-2046). For accuracy and precision of the models, error measurement indices and coefficients of determination and correlation were used. The results showed that the LARS-WG model has a good ability to simulate the studied variables in the study area. The results of long-term prediction of the studied models show that the minimum temperature values will increase in all study stations, which is based on HadCM3 and MPEH5 models on average. In the 2050s, and it will be 1.9 and 1.7 respectively, compared to the base period. The results of the studies of Kayo et al. (2016), Sharma et al. (2017), Khalil Aghdam et al. (2012), Qaderzadeh (2015), Sobhani et al. (2015) and Khalili et al. (2015) were confirmed. In general, based on the studied scenarios and models, the minimum temperatures are expected to increase in the study area in the future. By increasing it, the yield of some crops that need cold during the growing and productive period would decrease. It can also reduce snowfall, followed by frost on crops and lack of water in dry seasons. Therefore, due to the fact that following the climate changes, the conditions of the agricultural climatology are also changing, it is necessary for the relevant officials and planners in the agricultural sectors to adopt the necessary strategies to reduce the consequences and adapt to the new climate.
Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Aliakbar Rasouly; Hashem Rostamzadeh
Abstract
Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included ...
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Introduction
For the first time, Faddingham presented a geographic weight regression model. He tried to study the aspects of space heterogeneity. After that, Bronson examined the relationship between housing prices and areas. Which encountered a number of issues in relation to the model, which included the selection of variables, bandwidth and spatial correlation errors. Using the GWR, Franklin analyzed the spatial characteristics of the rainfall along with the elevation changes. Elvi also used this model to study the spatial factors that affect land prices. The GWR produces spatial information that expresses spatial variations between variables' relationships. Therefore, the maps produced from these analyzes play a key role in the spatial non-static description and interpretation of variables (Mennis 2006) and an equation Generates a separate regression for each observation instead of calibrating an equation, so it allows the parameter values to be continuously changed in the geographic space. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away.
Data and Method
The Surface Energy Balance Algorithm for Land (SEBAL) calculates the surface heat flux instantaneously as well as 24-hour. The latent heat flux shows the energy required for true evapotranspiration and is calculated as the remainder of the equilibrium energy equation (Mobasheri, 2005). In remote sensing estimates of surface Albedo, surface temperature and surface leakage in the thermal infrared region, reflectance is used to calculate spatial variations in short-wave radiation and long-wave radiation emitted from the surface of the earth. A combination of short-wave and long-wave radiation combines the ability to calculate the pure absorbed surface radiation for each image pixel. Each of the equations is calibrated using a different weight of the observations contained in the total data. And more relative weights are assigned to closer observations and less or zero weights to those who are far away. In other words, the GWR only uses geographically close observations to estimate local coefficients. This method of weighting is based on the idea that the use of geographically close observations is the best way to estimate local coefficients. The GWR method not only does not consider the effects of self-variables on the independent variable, but also the effects of neighboring situations. The values of the geographic weighting model can be used to describe the spatial correlation of the factors used. Therefore, we extend the study area to several sections We divide the values of the geographic weight coefficients in each of the sections in relation to each of the environmental parameters. Unlike regular regression models, they provide an equation for describing general relationships between variables. GWR allows the parameter values to be changed continuously in the geographic space. Each of the equations is obtained using a different weight of the observations contained in the total data.
Results and Discussion
The analysis of the relationships between selected indices by geographic weighted regression model and the classification of output values through the normalization of data in seven categories. The values obtained vary between 1 and 1, and the smaller the index, the spatial disjunction is variable, and the larger it shows the presence of spatial clusters. It was found that all three indexes of evapotranspiration, surface temperature and vegetation index have cluster spatial pattern. Therefore, the null hypothesis is based on the spatial correlation itself, and as a result, three of the above indicators can be used for spatial analysis of the actual evaporation. Based on the correlation between the factors affecting the macroeconomic factors, the factor of vegetation index has the most effect on the magnitude of the spatial distribution in the studied area (53% with an area of 471782864 square meters). However, as the results are clear, this number is an overall number and covers the overall situation in the area. And does not refer to spatial features of the area. In the results of weighted regression, the effect of elements can be observed spatially. Accordingly, according to the geographic weighted regression method, the relationship between evapotranspiration and surface temperature was negatively affected and negatively affected. The relationship between dehiscence and vegetation index was studied in different years. The highest digit on the seventh floor is 13/99 and in the area of 266611500, which shows a high positive effect. The relationship between evapotranspiration and the Albedo shows the highest value in the first and second classes. The values of 18 and 10 in the area of 490428000 and 1170753300 m 2, respectively, show a very negative impact and a significant negative effect.
Conclusion
Geographic weighted regression method is a statistical method that is adapted to study local patterns. This method is, in fact, a technical technique that analyzes the relationship between spatial variables in a hypothetical unpopular space. In this research, we tried to express the effect of several indicators on actual evaporation. These indicators are not all indicators that have had an impact on actual evapotranspiration Because actual evapotranspiration is closely related to other climatic factors. Because of the unique ability of spatial weighted regression to identify and analyze the relationships between variables, it is recommended to use it in quantitative analyzes. The Z classes resulting from the GWR analysis of the actual evapotranspiration in different years have different states that indicate the spatial effect of the surface temperature in different conditions.
Climatology
Zeynab Jawanshir; Khalil Valizadeh Kamran; Ali Akbar Rasuly; Hashem Rostamzadeh
Abstract
Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship ...
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Introduction Water management has always emphasized the need to abandon water storage in reservoirs and pursue a policy of limiting water consumption. Spatial-spatial information on evapotranspiration helps users understand the evacuation and depletion of water due to evaporation and establish the relationship between land use, water allocation, and water consumption. Evapotranspiration is the second element of the water cycle (after precipitation) and its accurate estimation on a regional scale is necessary to design appropriate management strategies. Evapotranspiration is a function of the amount of energy available for vegetation and its exchange. Because of this dependence, it can be estimated using the principle of energy conservation. Due to the limited number of meteorological stations in the country and the high cost of collecting ground data, the cost-effectiveness of the use of satellite data is one of its advantages, and the possibility of retrieving data from all levels of the region at one time is its next advantage. Having timely information makes horizontal monitoring of meteorological and environmental parameters possible. The ability of remote sensing to measure some terrestrial parameters has had an important effect on estimating actual evapotranspiration. The SEBAL model is one of the remote sensing algorithms that calculate plant evapotranspiration based on the momentary energy balance at the level of each pixel of a satellite image. The study area of the current research was the eastern cities of Lake Urmia. The reason for studying this section was the impact of recent droughts on these areas and the reduction of surface and groundwater, which has increased the need to manage water resources in these areas. Methodology In the first step of radiometric corrections, the amount of spectral radiance in the thermal band and at the next step, the reflectance in the visible bands, near-infrared, and short-wavelength infrared bands were calculated. As mentioned above, in the SEBAL model, actual evapotranspiration is calculated through satellite imagery and meteorological data is calculated using the surface energy balance. When satellite imagery provides information for its transit time, SEBAL calculates the instantaneous evapotranspiration flux for that time. Landsat 8 images for 2017-2016-2014-2013 years and meteorological data such as Minimum temperature, maximum temperature, dew point temperature, evaporation pan data, sunny hours, and wind speed were analyzed using ENVI 4.8 - Excel 2013- Arc GIS 10.3 software. Results and Discussion SEBAL is an image processing model that measures evapotranspiration and other energy conversions on the Earth's surface using digital data measured by remote sensing satellites that emit visible, near-infrared, and thermal infrared radiation. This method uses surface temperature, surface reflection, and normalized plant differential index (NDVI) and their internal relationships to estimate surface fluxes for different types of land cover. In this section, using the values obtained from latent heat flux and evaporation heat flux, first, the amount of instantaneous evapotranspiration for each pixel was calculated. Then, using Ref_ET software, the total 24-hour evapotranspiration was calculated and the daily evapotranspiration rate was obtained for the whole image. Conclusion The results showed that there was a good correlation between the values estimated by the remote sensing algorithm (SEBAL) and the FAO-Penman-Monteith method as well as the evaporation pan method. The difference between the amount of SEBAL and the FAO-Penman-Monteith method in the reference plant was less than 4.21 mm/day; the largest difference was related to the 22nd of October. In total, SEBAL and Penman-Monteith methods had an average absolute difference of 4.28 mm/day. According to the results of this study, it can be observed that using the SEBAL model, the actual evapotranspiration and water needs of crops and even orchards and rangelands can be calculated on a large scale. This case could prove the suitability of this model for estimating actual evapotranspiration at different levels of the farm and irrigation networks. Therefore, remote sensing has a very high potential to improve the management of irrigation resources in very large areas using various algorithms and providing an estimate of the amount of ET with minimal use of ground data. Using remote sensing technology and GIS, acceptable results can be obtained in estimating the actual evapotranspiration rate, especially in large areas. If the parameters of the energy balance equations and Penman-Monteith could be calculated from satellite images spatially, with a suitable plant coefficient, the two methods would have similar results in estimating the rate of evapotranspiration. Using this method, the plant coefficient, which is one of the important factors in calculating the evapotranspiration of plants, can be accurately determined.
Climatology
Hashem Rostamzadeh; majid rezaei banafsheh; Akbar hosseinnejad
Abstract
Introduction
The global warming of the Earth due greenhouse gases diffusion (GHGs) is undeniable now; over the past century, atmospheric CO2 concentrations have increased significantly and caused an increase in global temperature of 0.44 ° C compared to Pre-industrial era. The Intergovernmental ...
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Introduction
The global warming of the Earth due greenhouse gases diffusion (GHGs) is undeniable now; over the past century, atmospheric CO2 concentrations have increased significantly and caused an increase in global temperature of 0.44 ° C compared to Pre-industrial era. The Intergovernmental Panel on Climate Change’s (IPCC) Fifth Assessment Report (AR5) shows that there is a positive correlation between the amount of CO2 and global temperature rise. Today, climate change has attracted many scientists and researchers. The reason for this is the huge impact this phenomenon has on life on Earth. Potentially, climate change can endanger drinking water supplies, food production, and sustainable development in many parts of the world, For this reason, the International Committee of Climate Change (IPCC) calls for studies on climate change at the regional and local scale. Studies have shown that the mean temperature of the Earth has increased by about 0.18 ± 0.74 °C during the twentieth century And an increase in the temperature of the 21st century is estimated to be 1.8 to 4 degrees centigrade.
materials and methods
In this study, the three-hour temperature data of the synoptic station of Tabriz for the statistical period of 67 years (2017-1951) was prepared. Using Matlab's coding, seasonal and annual time series were prepared for each synoptic. Then, in order to provide the seasonal and annual time SYNOPs for the daily and night temperatures, the data are divided into two groups of nightly temperatures (including mean SYNOPs temperatures from 00:00, 03:00, 18:00 and 21:00) and daily temperature (including average SYNOPs temperatures at 06:00, 09:00, 12:00 and 15:00).
Discussion and results
Temperature is one of the most important elements in climatic zonation and classification, and it plays an important role in the distribution of other climatic elements. Accordingly, fluctuations and temperature changes are very important. In recent decades, the applied results of temperature analysis have led to a study of its long-run fluctuations, especially in the global arena. Therefore, in this study, the temperature fluctuations of three hours (SYNOPs), night temperature and daily temperature of the synoptic station of Tabriz during the statistical period of 1951-2017 and the seasonal and annual time scale were studied.
The results of the study show that SYNOPs, (3:00 pm local time), have more severe changes than other SYNOPs, which in summer increases at 0/66 °C per decade. Most annual changes are related to SYNOP 00:00 (an increase of 0.47 °C). Seasonal variations in daily and nightly temperatures also indicate that the highest changes in the night temperature were observed in summer (an increase of 0/62 °C), and the highest daily temperature changes were observed in spring and summer (an increase of 0.3 °C) Is.
the findings of this study are largely consistent with the findings of other studies in the study area. For example, Dinpajoh et al. (1394) obtained the same results by analyzing the process of weather parameters in Tabriz, indicating an increase in the minimum, maximum and average temperature in Tabriz. The results of the study, Sari Sarraf et al. (1394), also show that in the Urmia Lake basin, the minimum, maximum and average temperature has experienced an increasing trend in the annual and seasonal scale. Jahanbakhsh Asl et al. (1396) also studied the trend of variations in the average monthly cold-year average temperature in the northwest of Iran, with the result that the average minimum temperature in most parts of the northwest is increasing. Therefore, the results of this research and previous studies indicate that the temperature in the study area is increasing. The important thing about this research and its difference with previous studies is the use and application of temperature data. So, using daily temperature data (SYNOPs), the temperature changes were dealt with, while in other studies, the average temperature or minimum and maximum temperature parameters were used, so the results of this study could be information It will provide a more accurate description of the process of temperature variation in the Tabriz Synoptic Station.
Conclusion
According to the results, it can be said that the signs of climate change in Tabriz city, especially in terms of temperature, are visible. Considering the role of temperature in increasing evapotranspiration and urban energy consumption, over the next decade, there should be solutions to better manage water and energy resources, especially heat energy during the warm season.
Climatology
Hashem Rostamzadeh; Aliakbar Rasuly; Majid Wazifedoust; nasser maleki
Abstract
Introduction Floods are a natural occurrence that causes casualties, livestock losses and damage to buildings, facilities, gardens, fields and natural resources every year. Therefore, rainfall estimates have long been considered by researchers in various fields, and along with the advancement of science ...
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Introduction Floods are a natural occurrence that causes casualties, livestock losses and damage to buildings, facilities, gardens, fields and natural resources every year. Therefore, rainfall estimates have long been considered by researchers in various fields, and along with the advancement of science and the emergence of new technologies, many advances have been made in the methods of rainfall estimation and evaluation and validation to achieve the best method. In the last twenty years, there has been a lot of progress in rainfall estimation methods. This advancement is due to the possibility of using a lot of information from different parts of the world, better understanding of atmospheric phenomena, exchanges and atmospheric rotations, improving the performance of models, progress in various surveillance tools such as radar and satellite and computer power. The methods used to estimate precipitation, especially in the short term, have shortcomings and are generally based on numerical forecasting models or the use of empirical analyzes, which are usually not very accurate for multi-hour intervals, so the use of satellite data It has been recommended as a supplement to address this problem, and doing so could greatly help increase the accuracy of numerical models for rainfall estimates. Methodology The study used the physical properties of a cloud of five waves between 2011 and 2015. The data of the second generation of MSG meteorological satellite has good coverage on different regions of Iran. The satellite has 12 channels on the region and produces accurate products. Some of these products are in line with the physical properties of the cloud used in this study. These products are produced daily every 15 minutes and include cloud peak pressure (CTP), cloud peak temperature (CTT), cloud light depth (COT), thermodynamic cloud phase (CPH), and the volume of water in the cloud. Density (CWP) are the effective radius of cloud droplets (REFF) and cloud type (CT). Was obtained. The criterion for the accuracy of the calculations was the two MAE statistics Equation 1: Equation 2: Results and discussion In this study, TRMM satellite data was considered as control data. After receiving TRMM images in MATLAB software environment, programming was performed and precipitation data were extracted from NETCDF files. After extracting TRMM satellite data, Meteosat satellite products were prepared through the CMSAF database and their data were extracted using MATLAB software code. In the study of waves, the coefficient of determination in the GPR model was 0.72 in the experimental section and 0.77 in the training section. In the TD model, the determination coefficient is calculated in the experimental section 0.64 and in the training section 0.87. However, in the neural network model, the coefficient of determination is 0.68 in the experimental section and 0.72 in the training section. The results show a good relationship between the components studied. Investigating the Effects of Cloud Physical Properties: One of the methods for determining the effectiveness of each of the physical properties of the cloud in estimating rainfall is the sensitivity analysis method. After calculating the coefficient of determination and the error coefficient, the sensitivity of each of the physical properties in estimating the precipitation was performed by the method of calculating the sensitivity analysis. Sensitivity analysis was calculated for all waves. Calculations show that the cloud type is most effective, followed by the effective radius of the cloud droplets and then the optical depth of the cloud in the second and third positions, respectively. Among the physical properties studied, the lowest effect is related to the cloud phase. To investigate the relationship between the physical characteristics of the cloud and the amount of precipitation, five waves of pervasive precipitation were selected between 2011 and 2015. Rainfall data from the region's stations were extracted. In order to validate the TRMM data, a comparison was made between the precipitation data of the selected stations and the precipitation of this satellite. Metoost satellite products were used to extract the physical properties of the cloud. After extracting the data, the physical properties of the cloud were matched to the time scale of the data and evaluated using TRMM satellite rain as a control. Conclusion The selection criteria were such that the waves lasted for at least two days and covered the entire area. On the day of the operation, the precipitation information of the meteorological stations of the region was obtained and also the precipitation information of TRMM satellite was extracted. In order to validate the data of TRMM satellite, the information of meteorological stations was compared with TRMM precipitation and obtained the necessary correlation. In order to get a better result, the matching of numbers was done in terms of time scale. In the next step, using the meteosat satellite products, the physical properties of the cloud were obtained for all waves. Data were extracted at all stages for each pixel. Then the data correlation matrix was performed with three models of GPR, TD and MLPBR, the results of which are given in Table One. Due to the use of different models as well as the study of 8 physical properties of the cloud, the results show a high relationship between the components of the study, so that the coefficient of determination in the GPR model for the experimental and training sections was 0.7 and 0.77, respectively. These coefficients for the TD model in the experimental and training sections are 0.64 and 0.87, respectively. In the artificial network model (MLPBR), the coefficients obtained in the experimental and training sections are 0.68 and 0.72, respectively. The numbers obtained indicate a relatively good relationship between the components. Sensitivity analysis was performed. Sensitivity analysis results show that the cloud type feature has the greatest effect on precipitation and then the effective radius of cloud droplets and then cloud light depth are in the second and third positions, respectively. Among the physical properties studied, the lowest effect is related to the cloud phase.
Climatology
mohammad omidfar; Ali akbr Rasouli; Hashem Rostamzadeh; BEHROOZ SARISARRAF
Abstract
Introduction Considering the problem of continuous reduction of the water amount of urmian Lake, Identifying the distribution of rainfall in the basin area of Lake has a particular importance from the point of view of climate and hydrology. Doppler weather radar has an ability of the estimating of intensity ...
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Introduction Considering the problem of continuous reduction of the water amount of urmian Lake, Identifying the distribution of rainfall in the basin area of Lake has a particular importance from the point of view of climate and hydrology. Doppler weather radar has an ability of the estimating of intensity and the accumulation of daily rainfall with suitable spatial and diurnal resolutions. In current study, radar rainfall data, observed at the Sahand station, were evaluated with 10 synoptic weather stations data inside the Urmia Lake Basin exampling some of intensive rainfall events. The compared models show that among synoptic stations Tabriz, Shabestar, Sahand, Urmia, and Bostanabad have a best fit with radar daily rainfall productions, having high-quality conformity in northwest of the study area. In contrast, low level of agreements between two sets of radar has been observed in mountainous area. Due to the problem of continuous decreasing volume of Urmian lake water, accurate identification of the temporal distribution of rainfall can be very important from climatic and hydrological points of view. There are various ways to measure or estimate rainfall. Synoptic stations have a relatively low efficiency compared to radar and satellite due to their point and number limitation, relative to the area of the study area and other influential factors such as weather and human error. Tabriz Doppler Radar is one of the 12 radars of the National Radar Network of the Meteorological Organization of Iran, which works in the frequency band of Doppler C-type radars. The aim of this study was to investigate the efficacy and accuracy of radar-distance measurement tools in the study of heavy precipitation, which due to the infancy and lack of similar studies, the results can be used in future research. Methodology The accumulative precipitation data of synoptic stations in the studied area and the product of the daily accumulative precipitation of Tabriz Doppler radar, which is produced by the radar equation, by converting the echo-return intensity of precipitation, have been used. In this study, the data of accumulative precipitation of synoptic stations of the study area and the product of daily accumulative precipitation of Tabriz Doppler radar have been used. With the help of radar software, the product of surface precipitation intensity is produced in a 24-hour period and its temporal resolution is 15 minutes. Other product specifications such as start time, spatial resolution, and maximum distance, frequency of repetition of sent waves, name of the saved file, color scale of the data and the name of the radar site next to the product are listed. Radar accumulative rainfall on the most severe rainy day in Urmia Lake basin , the distance from the site of the radar site (concentric circles with a distance of 50 km from each other) and the location of the stations studied. Also, to compare the difference in estimation between radar and stations, error estimation indicators such as: mean error, absolute error mean, mean square error and Pearson correlation coefficient were used. Results and discussion The October 14 to 21, 2014 heavy rainfall in Urmia Lake basin has been studied by various radar products and among them 24-hour collective rain product, due to compliance with the cumulative rainfall data of stations, for 10 synoptic stations around Lake Urmia. Due to the collision of the waves with mountains, the topography of the area has a significant impact on the accuracy of radar estimation. They are considered invisible spots; these points causes a lot of errors (in some cases even up to 100%). Therefore, to compare radar data with the station, the accuracy of the separate precipitation estimate at different stations was examined. Conclusion The 24-hour accumulative precipitation comparison of the stations northwest of Urmia (for the cities of Tabriz, Sahand and Shabestar)with radar estimates on the days of heavy rains in October 2014, was highly consistent and the only difference in radar estimates on 20 and 21 days, was about 5 mm that less than Measured by synoptic stations. The correlation coefficient between the data is 0.996, which confirms the closeness of the measurement values of the two methods. The remarkable point in the chart is the significant difference and jump in rainfall on October 19 compared to other days. An examination of the graphs of the cities of Salmas and Urmia in the west and Bostanabad in the east of Urmia Lake shows less accurate but acceptable estimates of rainfall and differs. Conclusion: The comparative graph of rainfall in the Ajabshir city, despite its proximity to the radar site (50 km from the radar), shows a relatively large difference between the radar estimates and the stations. The most important cause of the error is the orientation of the southern Sahand Mountain. In moving to the more southern areas, the radar accuracy is lower, but the comparative rain chart of Ajabshir city, despite its proximity to the radar site, shows a significant difference. Overall, the results shows that: the southern regions, both due to the large distance from the radar and blocking effect of radar waves, almost all of the return waves are weakened from the targets and the radar estimates the amount of precipitation zero.
GIS&RS
Jafar Jafarzadeh; Hashem Rostamzadeh; Mohammadreza Nikjoo; Esmaeil Asadi
Abstract
The study of changes in water resources in each region is essential to manage water resources and using them. In this study, the goal is to evaluate the available water resources in the plain of Ardebil in terms of surface and subsurface resources based on four criteria including natural, hydrological, ...
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The study of changes in water resources in each region is essential to manage water resources and using them. In this study, the goal is to evaluate the available water resources in the plain of Ardebil in terms of surface and subsurface resources based on four criteria including natural, hydrological, agricultural and humanitarian by using fuzzy network analysis. In order to assess better fuzzy network analysis evaluation, sub-criteria of population, industry condition, rainfall situation, the status of surface water (volume taken from the river) and groundwater (wells, springs and aqueducts status), the area under cultivation and the type of products in terms of water demand, slope and elevation are used. Dependencies among sub-criteria using DEMATEL fuzzy technique and according to experts are determined. Using the fuzzy network analysis all criteria and sub- criteria are weighed, and the maps for all sub-criteria, are generated in according to the weight obtained. Finally, the result map that is based on initial layers and weighted based on the fuzzy techniques is generated in GIS. The resulting map is identified the sensitivity of the study area in terms of potential water resources. The study area (Ardebil plain) is located in the northwest of Iran and is delimited by latitudes 38°05′ N and 38° 30′N and longitudes 48°15′ E and 48° 35′E. The average height is about 1360 meters from the sea level. It covers an area of about 820 km2 and is part of Qara Soo river basin. The low risk areas 11.13 % equivalent to 9200 hectares are located on the northern and a bit in west of the plain. The average risk areas 19.36 % equivalent to 15870 hectares are located in the north and west of plain. The high risk areas 21.3 % equivalent to 17510 hectares are located mostly in the central and upper parts of the plain. The vulnerable risk areas 31.9 % equivalent to 26220 hectares are located in the southern and central parts of the plain and finally the critical areas 16.1 % equivalent to 13250 hectares are scattered mostly in the south and east of the study area.
