Extraction, processing, production and display of geographic data
Hossein Asakereh; Fatemeh Motevali Meydanshah; Leila Ahadi
Abstract
Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly ...
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Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.
Data and Methodology
The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.
Discussion
The selection of explanatory variables for downscaling was based on four statistical methods: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.
Conclusion
The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9 in winter, 0.2 to 8.1 in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4 is expected in autumn. These results are consistent with those of other researchers.
Extraction, processing, production and display of geographic data
Hossein Asakereh; Somayeh Taheri Alam; Nosrat Farhadi
Abstract
Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological ...
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Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological phenomena, primarily vegetation, which reflects an intricate pattern of changes in climatic elements, particularly temperature, and precipitation. Although the substantial role of climatic elements on the density and geographical distribution of vegetation has been confirmed, it is arduous to estimate the relationship between climate changes and vegetation due to the complexity of the mechanism of different characteristics of climatic elements (such as the amount, type, intensity, season, continuity, etc.), feedback processes, and also the response time of the vegetation to climatic changes.
Materials and Methods
In the current research, the gridded data of the Normalized Difference Vegetation Index (NDVI), a product of the MODIS terra, was used from 2001 through 2016. The data were extracted from a GIOVANNI website. In the present study, Iran's vegetation density classes were determined based on quantitative methods, and the geographical distribution of two-half parts of the understudy periods was compared.
Results and Discussion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. The finding revealed that the NDVI has a direct relationship with the precipitation. Accordingly, the northern, northwestern, and western regions, as wet regions in Iran and comprise proper soil, included high NDVI.
Dividing NDVI data into two 8-year periods revealed that in the first 8 - year, despite the high amount of precipitation, the NDVI was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Conclusion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. Our finding showed that the spatial distribution of NDVI has a direct relationship with the precipitation. Comparing two - half of understudy data showed despite the high amount of precipitation, the NDVI in the first half was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Hossein Asakereh; Mohammad Darand; Sayed Abolfazl Masoodian; Soma Zandkarimi
Abstract
Extended AbstractIntroductionThe tropopause is a thin layer separating the stratosphere from the troposphere and is often characterized by a large change in the thermal, mass and chemical structure of the atmosphere.Compared to global studies on the tropopause and its various features, studies conducted ...
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Extended AbstractIntroductionThe tropopause is a thin layer separating the stratosphere from the troposphere and is often characterized by a large change in the thermal, mass and chemical structure of the atmosphere.Compared to global studies on the tropopause and its various features, studies conducted in Iran are very few and the methods used are often less inclusive or the length of the statistical period is limited. For this reason, and considering the importance of the tropopause and its effect on exchanges between the troposphere and the stratosphere, and also due to the lack of information about it in Iran, accurate knowledge of the height of the tropopause in the country using more reliable data sources is a fundamental necessity. To calculate the tropopause, we used daily temperatures of ECMWF reanalysis datasets from January 1979 until December 2018. Gridded data witha spatial resolution of 0.25*0.25 were used. In vertical levels, we used 10 standard isobaric surfaces from 700 to 50 hPa. MethodsThe location of the tropopause thermally and dynamically was defined. According to the WMO (World Meteorological Organization), the tropopause is defined as the lowest level at which the lapse rate decreases to 2°C/km or less, provided that the average lapse rate between this level and all higher levels within 2 km does not exceed 2°C/km.In this study, this index was used to identify the tropopause.In this study, to identify the factors affecting the tropopause, the relationship between the tropopause and spatial variables (latitude and longitude) and altitude was evaluated by general and partial correlations. Results & DiscussionThe results of this study showed that in the months of cold season, the tropopause pressure level on Iran is followed by latitude, and the tropopause height decreases with increasing latitude, but in the months of the warm season (June, July, and August), the tropopause pressure level is different from the months of the winter season.In these months, the changes in the tropopause pressure levels do not follow the latitude; on the Zagros and Kerman heights, the tropopause height is at its lowest, while the highest tropopause elevation is in these months at higher latitudes than in other months.The temperature of the upper and lower levels of tropopause also showed that the temperature of the lower levels of the tropopause in all seasons was below the temperature of the upper levels of the tropopause and the temperature of the two levels changed with the changes in the levels of tropopause pressure in different months.The study of low and high levels of tropopause showed that during the cold season, the temperature of the two levels around the tropopause, following the tropopause pressure levels, follows the latitude, and with increasing latitude, temperature increases in the two levels around the tropopause.In two studied seasons, the lowest temperature of the two levels of the tropopause is consistent with the highest level of the tropopause, but the highest two-level temperature is only consistent with the lowest tropopause pressure level during the warm season months, and in other months, this observation coordination failed.Investigating the thermal difference between two levels of tropopause showed that the temperature difference between the two levels of the tropopause in the warm season is more significant than that of the cold season, while in the cold season, the temperature difference in most regions of the latitude is obeyed. Slowly, the difference in temperature decreases with increasing latitude. ConclusionExamination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.Examination of the characteristics of the tropopause and its related factors for summer and winter showed that in each season due to local conditions and changes in large-scale factors, the height of the tropopause changes, and therefore the tropopause in each season has completely different characteristics from the other season.
Hossein Asakereh; Hasan Shadman
Abstract
Abstract
Hotdays are temperature extreme states and are considered to be one of the important climatic phenomena. Long term changes (trends) of thisphenomenon arethe consequences and evidences of thermal-climatic changes. Also, these days can affect the ecosystems and human life. Therefore, recognizing ...
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Abstract
Hotdays are temperature extreme states and are considered to be one of the important climatic phenomena. Long term changes (trends) of thisphenomenon arethe consequences and evidences of thermal-climatic changes. Also, these days can affect the ecosystems and human life. Therefore, recognizing the behavior of hot days can be the source of many topics. In this research, we tried to investigate the long-term trend of Iran's hot days using the network data of the country’s average maximum temperature from 1961 to 2007 and statistical methods. For this purpose, the hot day profile was studied based on the percentile of ninety for each pixel from the network and was estimated on each day of the year. Thus, a threshold of heat occurrence was obtained for each pixel every day. Then, the days whose temperatures equaled or exceeded this threshold, were considered hot days.The average number of hot days in the country is 39 days. The cold season months, as well as April, have the highest frequency of the average hot days.The frequency of hot days is increasing. The number of hot days has made a positive trend for about half of the country. Also, the average temperature of hot days has also been checked. The trend of the average temperature of hot daysin more than half of the countryhas been positive and in around one third of the country has been negative.The hot days’ temperature-related events of Iran have a 3 to 4 year cycle. Toanalyze the trend in data, linear regression was used with least squares error method and a spectral analysis method was used to investigate the existence of significant fluctuations in the data.
Hosein Asakareh
Volume 11, Issue 41 , May 2002, , Pages 21-23
Abstract
Modeling means presenting a complex situation in a simple and hypothetical manner, with emphasis on some aspects and attributes and removing others in order to recreate the status of the past and predict the future. The first modellings were performed on the atmosphere. Then, by using other branches ...
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Modeling means presenting a complex situation in a simple and hypothetical manner, with emphasis on some aspects and attributes and removing others in order to recreate the status of the past and predict the future. The first modellings were performed on the atmosphere. Then, by using other branches of scientific knowledge, climate modeling was carried out.Climate models fall into four categories of Energy Balance Models (EBM), Radiation-Convection Models (RCMs), Statistic-Dynamic Model (SDM) and general Circulation Models (CCM).
General Circulation Models of the atmosphere determine the three-dimensional climate indices in networks and calculate dynamic and thermodynamic processes in each network and from one network to the other based on basic equations of movement and in different time and spatial periods and atmospheric levels.Finally, the content and foundation of these models are based on dynamics, physics and levels affecting the dynamics and physics of the climate.
Hosein Asakareh
Volume 9, Issue 34 , August 2000, , Pages 44-46
Abstract
One of the events of Holocene close to our time is the occurrence of a glacial period that affected all the areas of earth. This event, famous as the Little Ice Age, happened in various places and over different periods of time. However, all theories introduce the period between early-sixteenth to mid-nineteenth ...
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One of the events of Holocene close to our time is the occurrence of a glacial period that affected all the areas of earth. This event, famous as the Little Ice Age, happened in various places and over different periods of time. However, all theories introduce the period between early-sixteenth to mid-nineteenth centuries.The first theory about the cause of this glacial period ascribed this phenomenon to the Sun’s spots through Eddies, but later Robock proved that this age occurred due to volcanic activities and spread of dusts and volcanic ashes in the atmosphere.
Hosein Asakareh
Volume 9, Issue 36 , February 2000, , Pages 41-48
Abstract
Biological responses to climate have led climatologists to recognize bio-evidence as one of the most appropriate patterns of studying past climatic developments. Biostatic evidence includes plant, animal and human evidence. Plant evidence includes plant remains and annual growth rings of trees. Animal ...
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Biological responses to climate have led climatologists to recognize bio-evidence as one of the most appropriate patterns of studying past climatic developments. Biostatic evidence includes plant, animal and human evidence. Plant evidence includes plant remains and annual growth rings of trees. Animal evidence can be studied and evaluated in aqueous and terrestrial areas, as well as in dry or humid regions. The habitations of the early humans, caves, agricultural lands remaining from ancient civilizations, types of livestock and livelihood of primitive human beings in every place indicate the climatic conditions of that place in each period.
Tracing and recreating climatic conditions of distant past based on evidence in the African Continent (Sahara), Europe, the United States and Asia have shown acceptable results with regard past climate changes. The findings from the study of past biological conditions have been confirmed by other methods. Therefore, biological methods of studying past developments are among the most useful methods for examining climate change. It should be noted that the methods of studying distant-past climates utilize the results of research in other scientific fields as well, and, along with other methods of study, provide with a more satisfactory image of the past climatic conditions.
Fatemeh Tarkarani; Hosein Asakareh
Volume 8, Issue 30 , August 1999, , Pages 14-17
Abstract
Application of the “CN” curve is in calculation of the detention coefficient’s value. This index is necessary for estimation of the delay and concentration times of the basin as well as the height of runoff by the SCS method. Since common and traditional methods of preliminary studies ...
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Application of the “CN” curve is in calculation of the detention coefficient’s value. This index is necessary for estimation of the delay and concentration times of the basin as well as the height of runoff by the SCS method. Since common and traditional methods of preliminary studies are time-consuming and costly, the best methods recommended in this regard are reasoning-based methods.One of such methods for calculation of the detention coefficient and number of curve is utilization of basin’s runoff coefficient. This method was used in the Nojian basin, one of the head-branches of the Dez River in southeast of Khorramabad, Lorestan. The runoff coefficient in this basin was calculated to be 0.59, the detention coefficient 0.41 and the average CN about 19.61.Lack of direct relationship between the basin’s monthly precipitation and runoff makes exploitation of the index mentioned above for short periods impossible. Therefore, this index can be calculated monthly only in basins where precipitation is mostly liquid and also monthly precipitation justifies runoff of the same month.
Hosein Asakareh
Volume 7, Issue 28 , February 1998, , Pages 12-15
Abstract
Although the best method of studying environment is utilization of evidences available in environment itself, exploitation of historical documents and application of different theories and their combination can provide us with useful results about past environments.Using the above method, the approximate ...
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Although the best method of studying environment is utilization of evidences available in environment itself, exploitation of historical documents and application of different theories and their combination can provide us with useful results about past environments.Using the above method, the approximate age of the Shadegan Delta was determined and it became known that sedimentation and development of the Shadegan Delta began simultaneously with glacial periods in the Pleistocene. This delta did not exist until four thousand years ago and its emergence occurred after that date.
Hosein Asakareh; Saeed Movahedi
Volume 6, Issue 23 , November 1997, , Pages 6-10
Abstract
The effective temperature is the temperature of calm and saturated air that can have, in absence of radiation, the same effect that the air in question has. This standard combines the effects of temperature and humidity. The modified effective temperature, besides the two factors of temperature and humidity, ...
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The effective temperature is the temperature of calm and saturated air that can have, in absence of radiation, the same effect that the air in question has. This standard combines the effects of temperature and humidity. The modified effective temperature, besides the two factors of temperature and humidity, involves the effect of radiation and cooling quality of wind as well. Therefore, it is the most accurate standard recommended for study of air conditions in terms of human comfort.
Effective temperatures between 22o to 27o and wind flows with speeds between 0.15 to 1.5 m/s have been suggested as the area of comfort for human being in hot regions. The following issues are considered for determination of thermal comfort area in Abadan and Dezful:
Need for mechanical cooling and provision of shade, need for decreasing the speed of wind, need for mechanical heat in early hours of the day (during winter).
It should be noted that the maximum effective temperature of Abadan in absence of wind occurs during April to June as well as August and October. In Dezful, maximum effective temperature takes place with one month of delay compared to Abadan, namely from May until June and then in September and October.
