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.
Geographic Data
Hossein Asakereh; Ava Gholami
Abstract
Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, ...
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Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & MethodsData collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & DiscussionAppropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October. ConclusionSelecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.
Kamal Omidvar; Reza Ebrahimi; Ahmad Mazidi; Teymur Alizadeh
Abstract
Abstract[1]
Increasing demand for energy against the reduction of comprehensive energy resources along with the consequences of global warming, make the importance of a quantitative review of changes in the need for cooling, heating of the country in the past and in the future decades essential. First, ...
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Abstract[1]
Increasing demand for energy against the reduction of comprehensive energy resources along with the consequences of global warming, make the importance of a quantitative review of changes in the need for cooling, heating of the country in the past and in the future decades essential. First, the overall atmospheric circulation data was extracted from the EH5OM database. These data were under the A1B scenario of the International Climate Change Board and were downscaled with regional climate model data of average daily temperature of 0.27 x 0.27 degree, which covers approximately 30 x30 kilometer dimensions of Iran in the time interval of (2015-2050). The average daily temperature data of the past period were extracted from the ISFZARI databases during the statistical period of (1970-1970) on cells measuring 15 x 15 km. throughout the country. The temperature threshold of 11 degrees was used to calculate the heating degree day and the threshold of 18.3 to calculate the cooling degree day. The monthly average of these parameters was obtained on a matrix of 12 × 2140 (future) and 7187 * 12 (past), in which the rows represent the time (month of the year) and the columns represent the locations of the cells. Then the monthly average map of both periods was drawn and interpreted. The results indicate that the cooling of the air in the coming decades compared to the previous period in January and December in most parts of the country except for the coastal areas and the hinterlands, and the warming of the air in most parts of the country in the warm months of the year (June, July, August) will have significant effects on the amount of energy used for heating and cooling.
[1] - به دلیل کیفیت نامناسب ترجمه (چکیده مبسوط انگلیسیِ دریافتی) نشریه، به ناچار اقدام به ترجمه مجدد متن مختصر چکیده فارسی و انتشار آن به جای چکیده مبسوط انگلیسی نموده است.
Behrouz Nasiri; zahra yarmoradi
Abstract
Abstract[1]
The increase in greenhouse gases in the last few decades has disrupted the climatic balance of the Earth which is called the phenomenon of climate change. The main consequences of climate change will be the increase in global average temperature, the increase of climatic extreme phenomena ...
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Abstract[1]
The increase in greenhouse gases in the last few decades has disrupted the climatic balance of the Earth which is called the phenomenon of climate change. The main consequences of climate change will be the increase in global average temperature, the increase of climatic extreme phenomena such as floods, storms, hail, thermal waves, sea level rise, melting of polar ice and untimely cold. The use of Statistical Downscaling Models for estimating climatic fluctuations allows weather data to be generated at the appropriate spatial and temporal scales. Such capabilities have contributed greatly to studying local and regional climatic fluctuations. In this research, the efficiency of LARS-WG model was examined and evaluated for generating and simulating daily temperature, sunny hours and rainfall data in Lorestan province using MAE, T-STUDENT, MAE, R2 statistical parameters and their subsequent changes in the future became apparent too. The results showed that at 99% confidence level, there is no significant difference between actual data and data obtained from the model and the model has the necessary efficiency in generating daily data. After making sure of the model’s efficiency, the outputs of the HADCM3 model were used and the daily temperature, radiation and precipitation data for the base period (1961-2005) were simulated under three scenarios of A1B (mid-range scenario), A2 (maximum scenario) and B1 (scenario Minimum).Based on the HADCM3 model estimates for the scenarios under study in future periods, the average maximum temperature and precipitation of the province would increase about (0.9 to 1.3 degrees) and (12.04 percent), respectively, and average sunny hours would decrease by about 0.6.Also, despite lower changes in maximum temperature than the minimum temperature, the average temperature increase in this period is expected. According to these results, the climatic conditions of Lorestan province in the next 50 years will have a significant difference with the current situation and long-term strategic plans seem necessary to manage these conditions.
[1] - به دلیل کیفیت نامناسب متن چکیده مبسوط انگلیسیِ ارائه شده توسط نویسنده مسئول مقاله، نشریه به ناچار اقدام به ترجمه مجدد متن چکیده فارسی و انتشار آن به جای چکیده مبسوط انگلیسی نموده است.
Gholamali Mozafari; Shahab Shafiei; Zahra Taghizade
Abstract
Over the past few decades, the increase in the temperature of the Earth has caused the disruption of climatic balance of the Earth, causing widespread climatic changes in most parts of the planet, which is referred to as climate change. The aim of this study is to predict climatic changes of Sistan-va-Baluchestan ...
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Over the past few decades, the increase in the temperature of the Earth has caused the disruption of climatic balance of the Earth, causing widespread climatic changes in most parts of the planet, which is referred to as climate change. The aim of this study is to predict climatic changes of Sistan-va-Baluchestan Province using statistical downscaling in which the A2 scenario data of ECHO-G atmospheric general circulation model is implemented.To assess, the climatic changes and the drought in Sistan and Baluchestan Provincewere downscaledby the LARS-WG model during the statistical period of 2012 to 2031.In this study, the data of minimum temperature, maximum temperature, radiation, and precipitation of ECHO-G model, and the actual data of 7 stations in the province, including the Chabahar, Iranshahr, Khash, Saravan, Zabol, Zahak and Zahedan have been used. The overall results of the surveys for the aforementioned period indicate an 8 percent increase in precipitation in the province and a decrease in the number of glacial days and an annual average increase of about 0.3 degrees Celsius. The highest monthly increase in wintertemperatureis at 0.9 degrees Celsius. Moreover, the number of dry days increases in Saravan city and decreases in other cities, and in general, the droughts in this province decrease in the period of 2012- 2031.
