Climatology
Mohammadreza Rafighi; Mehry Akbary; Mohammad Hassan Fakharnia; Mohammad Hassan Vahidnia
Abstract
IntroductionAlthough the air layer adjacent to the earth's surface - the boundary layer - is a small fraction of the entire atmosphere, the processes that take place on a small scale are very important to human life and activites. Among living organisms, plants and especially trees have undeniable effects ...
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IntroductionAlthough the air layer adjacent to the earth's surface - the boundary layer - is a small fraction of the entire atmosphere, the processes that take place on a small scale are very important to human life and activites. Among living organisms, plants and especially trees have undeniable effects on surface temperature and especially in urban environments have several balancing effects. This research was carried out using Landsat 8 satellite imagery and with Arc GIS software to compare the surface temperature of the earth in two areas with vegetation of coniferous trees (Chitgar Park) and broadleaf trees (Shahid Chamran Park). The values of Radiance, Reflectance, Brightness Temperature, Normalized Difference Vegetation Index, Proportion of Vegetation and Emissivity and then Land Surface Temperature were calculated and generated. A total of 1700 points were harvested from Chitgar Park and 800 points from Chamran Park. In SPSS software, Leven test (F) statistics was used to prove the homogeneity of variances of the samples and parametric tests (T with two independent samples) were used to prove the significant difference between the surface temperature of the earth in the mentioned areas. According to Leven test, the value was Sig = 0.409 (P_value), which confirms the homogeneity and equality of variance of the studied samples. Also, in the T test, the value was Sig = 0.000, which is less than 0.05, which means a significant difference. Therefore, the difference between the surface temperature data of the two parks was proved. Also, by comparing the graphs of LST values in the two groups, we found that Chitgar Park has a higher surface temperature than Chamran Park. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.Data and Method The data used in this study is a Landsat 8 satellite imagery with the acronym: 8 (LC08_L1TP_165035_20190706) is LANDSAT.Retrieved July 6, 2019 from the USGS website.Production of component images for Shahid Chamran Parks in Karaj and Chitgar in Tehran: The surface temperature image was generated step by step using the Landsat 8 satellite image using the Raster Calculator command in the ArcMap software environment. First, relevant and effective indicators in calculating the surface temperature of the earth, Top of atmospheric radiance, reflectance, Brightness Temperature, normalized difference vegetation index, proportion of vegetation, emission coefficient (emissivity), calculation and their images are produced and then the land surface temperature, It was calculated and produced according to the following mathematical formulas.Step 1: Produce a spectral radius image from above the atmosphere To obtain the brightness temperature, the image must first be converted to radius. Therefore, the gray DN values of bands 10 and 11 of the Landsat 8 satellite TIRS sensor should be converted to high atmospheric radius separately with the help of the MTL file, which is an extension of the Landsat image (Tables 1, 2 and 3).Formula (1) :Calculate the radius of the upper atmosphere TOA (Lλ) = ML * Qcal + ALLλ = (Watts / (m2 * srad * μm)) The radius of the atmosphere in terms ofML = Multi-band radius_ 10 bandStep 2: Produce an image of the light temperature above the atmosphere After converting the DN values of bands 10 and 11 to high atmospheric radii, we converted these two corrected bands to Brightness Temperature.BT = (K2 / (ln (K1 / L) + 1)) - 273.15 Formula (2): Calculation of Brightness Temperature BT = Atmospheric Brightness Temperature (° C)Lλ = (Watts / (m2 * srad * μm)) Radius of the atmosphere in terms ofBT = (1321.0789 / Ln ((774.8853 / “% TOA%”) + 1)) - 273.15K1 = K1 Constant Band (No.), K2 = K2 Constant Band (No.)Step 3: Produce vegetation index image formula (3): normalized difference vegetation index image was generated usingNDVI = (Band 5 - Band 4) / (Band 5 + Band 4)Step 4: Produce a proportion of vegetation imageThe proportion of vegetation image was generated using normalized difference vegetation index.formulas (4):Calculate the proportion of vegetation PV = (NDVI - NDVImin / NDVImax- NDVImin) 2PV = Square (("NDVI" - 0.216901) / (0.632267 - 0.216901))Step 5: Produce the Emissivity image Emissivity image was generated using formula (5)ε = 0.004 * PV + 0.986 Formula (5): Calculate the Emissivity coefficientStep 6: Produce an image of the earth's surface temperature Land surface temperature image was generated using formula (6).Formula (6) :Calculate ground land surface temperatureLST = (BT / (1 + (0.00115 * BT / 1.4388) * Ln (e)))Results and Discussion Text Comparison of surface temperature phenomena (LST) According to Table (6), the highest land surface temperature with 44.42 ° C belongs to Chitgar Park, which is covered with coniferous trees, and the lowest in Shahid Chamran Park, in Karaj with 28.09 ° C with broadleaf trees. Has been. According to Tables (7) and (8), the lowest temperature of Chamran Park is 28.09 ° C and the highest is 36.51 ° C and the lowest temperature of Chitgar Park is 34.74 ° C and the highest is 44.42 ° C. . According to Figure (22), Chitgar Park with an average surface temperature of 38.92 ° C is warmer than Shahid Chamran Park with an average land surface temperature of 31.39 ° C. Figure (23) shows a red graphic showing the surface temperature of the ground in Chitgar Park with coniferous species (pine) and the blue diagram shows the surface temperature of Shahid Chamran Park in Karaj with broadleaf species. It is clear that the temperature is significantly higher in Chitgarh Park. The range of temperature fluctuations in Shahid Chamran Park is between 36.51 - 28.09 ° C and in Chitgar Park is between 42 / 44-74 / 34 which is exactly shown in the diagram. The fact that the red chart is higher than the blue chart explains this correctly. This is due to the lower density of trees in Chitgarh Park as well as the predominant tree species (needle-shaped) due to less shading and more input radiation. T test with two independent samples: This test, which is a parametric test, was used to prove a significant difference between the earth's surface temperature in areas with coniferous and deciduous trees. Leven test (F) was used to prove the homogeneity of sample variances and t-test with two independent samples was used to examine the homogeneity of the means of the two statistical populations, which resulted in the following results. As can be seen in Table (12), the value = 0.409 Sig, which is the same value as P_value, is greater than 0.05, ie the variance of the communities is homogeneous and equal. 0.05 is less, which means that the difference is significant. Due to religion, the difference between the land surface temperature data of Shahid Chamran and Chitgar parks is proved.ConclusionAccording to all the findings, Chitgar Park has a higher land surface temperature than Chamran Park, which is due to the lower density of trees and also the type of dominant tree species (needle-shaped). Coniferous species that take up less space than broadleaf species and have less shading. They also make it possible for the sun to collide with the ground due to the fact that the leaves of the adjacent trees do not meet, and this is an important factor in raising the surface temperature in the mentioned park. Species compatible with the climate of the study areas are broadleaf species because they have more leaves shading and care than coniferous species and ultimately cause more climate adjustment. The difference in temperature between the two parks confirms this fact. In the current dilemma of the century, global warming, knowing these local realities and providing logical solutions to reduce surface temperature at the regional and regional scales as a whole can effectively solve the problem of global warming on a global scale.
Climatology
Naser Mansourei Derakhshan; Bohlol Alijani; Majid Azadi; Mehry Akbary
Abstract
Introduction The weather fronts are known for their large vorticity, dense, moisture, and statical Stability gradients, and their longitudinal scale is one unit greater than their width. The width of the front is known as the baroclinical zone, in which the front lines have a very large ...
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Introduction The weather fronts are known for their large vorticity, dense, moisture, and statical Stability gradients, and their longitudinal scale is one unit greater than their width. The width of the front is known as the baroclinical zone, in which the front lines have a very large temperature gradient, which is determined by the angle between pressure and temperature lines. Position of a front is located in warm side of the extreme temperature gradient, inside the heat transfer zone and intensity of the front is determined by the size of the horizontal or quasi-horizontal temperature gradient.Even the numerous expert synopticians disagree with each other in the position of the fronts, their types and intensity, in the manual drawing method of the fronts. So their drawn fronts are very different While objective front is based on numerical methods and its purpose is to avoid applying people''s tastes in their manual method. The advantages of objective front metod in comparison with subjective front method are high speed front detection, the possibility of determining front frequeny, moving, and feedback of fronts with land side effects. So far, various methods have been developed for objective front method. They performed objective front method using numerical methods and the first and second derivatives of the temperature parameter on a regular grid points with a relatively low resolution of about 100 km. Inside the country, there has been no study about automatic and numerical front methods. On the other hand more than 90 percent of heavy rainfall in the tropics is associated with the fronts. Therefore, considering Iran''s location in the middle latitudes, it is very necessary to study and identify the fronts. So the climatological study of the manual front detectin is very time consuming, expensive and practically impossible. Therefore, in this research, the, automatic and numerical front detection have been discussed for the first time in the country. Methodology In this study, grid point data from the European Center for Medium-range Weather Forecasting (ECMWF) of type (ERA - Interim) is used with gaussian grid points. In this centre, different types of data are classified into different formats and in different time intervals and different grid resolution. In order to study of the fronts, isobaric level data with 6 hour intervals and resolution of 0.75 × 0.75 degrees with grib format is used. This grid resolution is set in a regular 61×61 matrix with a grid distance of 83 km. Different quantities can be used to select the appropriate parameter to detection of fronts such as temperature, humidity, wind direction and wind speed, vorticity, thickness and thickness changes ,and temperature is on of the most important of them. On the other hand, detection of the exact location of the extreme temperature gradient, which is accompanied by the effects of heating on the warm convergence belt in the warm side of the front leads to warm weather, can be identified only by using the equivalent potential temperature. Results and Discussion The main idea for identifying frontal areas is to use a temperature parameter in two-dimensional horizontal coordinates. The line representing the front in these areas is identified using a frontal identification function. In order to identify the front, the masking conditions are applied once or several times. In other words, in this equation, the horizontal gradients of the equivalent potential temperature are used, which should not be less than the value of the K-threshold value. >K . Several indicators are considered to identify the front. The first of them is that the front must be at a turning point in the curvature of the temperature lines which is along the temperature gradient. The second indicator is the location of the maximum values of temperature gradient,and the third criterion is the point where the second derivative of the temperature gradient is zero. Various experiments have shown that the smaller the temperature derivative of the front temperature parameter, the less error there will be (J. Jenkner, 2009). Thus, the Front Termal Parameter (TFP), invented by Renard & Clarke (1965), was used as the main method of frontal reconnaissance. TFP = In this equation, second derivative of the temperature parameter has been used, which has converted the temperature gradient, which is a vector quantity, to a scalar quantity. Conclusion Examination of the results of objective fronts showed that the detection of fronts near the ground due to the interaction between the boundary layer and the fronts is very erroneous and the fronts are practically indistinguishable. On the other hand, at higher levels, shallow fronts at numerical output are not detected. Therefore, the appropriate level for automatic identification of fronts in the study area, 700 hPa level was selected. Examining the results, it is inferred that cold and warm fronts are often found at the bottom of the ridge and above the ridge of the upper surfaces, and these fronts, during the formation stage, are often discontinuous and gradually evolve during the developmental stages. Strengthening the front will take a more integrated form. Studies have shown that cold fronts produce stronger frontogenesis than warm fronts. Also, the output of objective fronts showed that TFP is a good parameter for detecting the front in this region and with the results of previous studies such as Hewson (1998: 49), Jenkener et al. (2010: 9), they show a good match. The results of this study can be used in the discussion of climatology and forecasting of fronts and can be helpful in the discussion of flood management due to heavy rainfall on the front.
Mehri Akbari; Hossein Mohammadi; Ali akbar Shamsipour
Volume 18, Issue 48 , June 2014, , Pages 17-36
Abstract
Abstract In this research large scale dynamic and thermodynamic anomalies during storm events resulted from cyclogenesis in the Mediterranean Sea and Sudan low-pressure systems, are investigated. 5 severe storms that happened in KarunBasin (1998-2008) were selected and using Japanese Re-analysis data ...
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Abstract In this research large scale dynamic and thermodynamic anomalies during storm events resulted from cyclogenesis in the Mediterranean Sea and Sudan low-pressure systems, are investigated. 5 severe storms that happened in KarunBasin (1998-2008) were selected and using Japanese Re-analysis data (JRA25), anomalies of dynamic and thermodynamic indices, 2 days prior to the start of the storm until the end of the storm were analyzed. The selected indices in this research are potential vorticity, convergence and divergence, vertical velocity, absolute vorticity advection, specific humidity, moisture flux, potential temperature and equivalent potential temperature. According to the results and comparing 6-hourly recorded rainfall amounts, it was found out that in the reference events, before the start of the storm, geopotential height values, in the under-study region decreased and at the time of maximum rainfalls, the geopotential height reached to its lowest values and by end of the storm, the values started increasing, whereas parameters related to convergence and vertical movements, such as potential vorticity, vorticity advection, moisture flux, convergence of moisture and specific humidity amounts corresponded to the same trend of rainfall from the beginning to the end. It is obvious that none of these indices can individually cause the occurrence of a storm, but by analyzing trends and regressions, it seems that there are meaningful relationships between geopotential height, moisture advection and potential vorticity and rainfall amounts which can be used in forecasting future rainfall events. To verify the results obtained from the reference events, 2 days without rainfall at least 2 days before and after the selected days, were also selected and studied. The results verify considerable changes of the selected dynamic and thermodynamic indices during stormy days compared to the days without any rainfall in the region.
