Climatology
Seyyed mahmoud Hosseini seddigh; masoud jalali; Hossein Asakereh
Abstract
IntroductionThe results of the study showed that the correlation headley cell and subtropical jet on the atmosphere Iran at the level 200 hPa has a positive correlation with a value of 0.4-0.7 to 35 ° latitude and also regression analysis showed that in latitudes between 15 35 degrees north of the ...
Read More
IntroductionThe results of the study showed that the correlation headley cell and subtropical jet on the atmosphere Iran at the level 200 hPa has a positive correlation with a value of 0.4-0.7 to 35 ° latitude and also regression analysis showed that in latitudes between 15 35 degrees north of the subtropical jet 1(m/s) is higher than normal, although in 2017 up to latitudes 30 degrees north showed an increase of 2(m/s), which had a negative effect on rainfall.Data and MethodThe relationship between Hadley cell and olr in the southern, southwestern and southeastern regions of Iran with a value of 0.4 and the Zagros and northwestern heights of Iran with a value of 0.7 and regression with a value of (w/m2) 0.01 more than normal.Results and DiscussionIt acts as a tangible source of heat in the middle Wordspehr and the heat is added directly to the middle Wordspehr and causes heating of the upper half of the Wordspehr.ConclusionRegression 2 to 1 is shown. Low relative humidity along with the dried air mass is located below the descending branches of the headley cell, which has ruled the drought conditions (-0/7) showed that it creates conditions for lack of rainfall and drought.
Climatology
saeid Jahanbakhshasl; Behrouz Sari Sarraf; Hossein asakereh; soheila shirmohamadi
Abstract
Introduction Climate extreme events have expanded and intensified during the 21st century. Extreme precipitation event annually leads to severe damage in agriculture, environment, infrastructures and even the human loss. Therefore, identification of the behavior of such events is one of the pivotal aspects ...
Read More
Introduction Climate extreme events have expanded and intensified during the 21st century. Extreme precipitation event annually leads to severe damage in agriculture, environment, infrastructures and even the human loss. Therefore, identification of the behavior of such events is one of the pivotal aspects of climatic change and the increase of information about extreme precipitation is tangibly necessary for the society especially with regard to those, living in the areas with high risk of flood. extreme precipitation events can be defined as significant deviations from the precipitation mean. As a result, to identify such precipitations, a criterion was needed to evaluate the rate of precipitation values’ deviation from mean. Importantly, given the different types of indicators and thresholds proposed for extracting extreme precipitation, choosing an appropriate threshold with climatology conditions of the study region which could also be capable of identifying extreme precipitation optimally in terms of amount and frequency, requires high precision. The present study aimed at identifying the extreme precipitation events in the west of Iran through introducing the appropriate threshold and spatial scale for the extraction and investigation of these events.Data and MethodsThe west of Iran with the areaof 230760 square kilometers includes about 14% of total area of Iran. Zagros Mountains, stretching from northwest to southeast, are the most important feature of the west of Iran. Two databases have been used in this study. The first database regardsthe precipitation data of 1129 synoptic stations, climatology and rain gauge in the west of Iran. The stations statistics have been checked in terms of existence of any outlier. Ultimately 823 stations out of 1129, were used for producing gridded data. The gridded data, are the results from the interpolation of daily precipitation observations since January 1st 1965 to December 31st 2016, using Kriging interpolation method and spatial separation of 6*6 kilometers. the final base, a matrix possessing the dimensions of18993*6410 (representing time on the rows and place on the columns) was developed. The second database referred to the Sea-level pressure patterns (Hectopascal).To identify such precipitations, in addition to the main threshold that includesthe mean of precipitation more than 75th percentile for each pixel per day of a year, a second threshold including the standard deviation of these precipitations (with the values of one, two, and three times more) has been also added to the mean. Accordingly, three groups of extreme precipitation were identified in the region which were separated according to the spatial zone that had been covered. Moreover, the sea-level pressure patterns were extracted with regard to these precipitations for each zone andthen classified using clustering analysis technique.Results and Discussionthree groups of precipitations with different coverage zoneswere identified: 1- 83 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus one time standard deviation which cover more than 40% of the region. 2- 144 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus two times standard deviation which cover more than 20% of the region. 3- 82 days with equal to or more precipitation than the mean of precipitations more than 75th percentile plus three times standard deviation which cover more than 20%The maps of 7 participation groups of the first type in comparison with 6 precipitation groups of the second and third type contain common and repetitive patterns. Each precipitation maps of the second and third types explains a type of patternand there is minimum overlapping in the maps. Therefore, the precipitations are obtained from the most particular and distinct atmospheric patterns. considering the three properties of 1- equality of precipitation groups of type two and three (both include 6 groups of atmospheric patterns). 2- repeating the atmospheric patterns of precipitation of type two prominently in the precipitations of type three. 3- the formation of the most optimum atmospheric modeling for the precipitations of both thresholds in the zones of 20% and higher, in the west of Iran, the extreme precipitations refer to those with higher means of recipitations more than 75th percentile plus two times standard deviations,have mostly occurred in the zone of 20% and higher of the region.
Climatology
Hossein asakereh; Seyed Abolfazl Masoodian; Fatemeh Tarkarani
Abstract
Introduction
According to previous investigation and examining climatic elements, the hypotheses of global warming and consequently, global climate change is confirmed by majority of climatologists society around the world. The global changes probably continue for the next decades. The changes in climatic ...
Read More
Introduction
According to previous investigation and examining climatic elements, the hypotheses of global warming and consequently, global climate change is confirmed by majority of climatologists society around the world. The global changes probably continue for the next decades. The changes in climatic elements, by and large, categorized into two types; trends and variation. The trends refer to long term changes, whiles variations indicate vary time interval changes including oscillation, phase, jump (sift), and persistence.
Precipitation is one of climatic elements which can properly reflect chaotic behavior of climate system, and illustrate the nature of changes in the system. Trends, Oscillation, and persistence in this element are investigated in national and international scale, whilst the decadal variations as an index of climate variation can contribute to the current literature. In current study we attempted to illustrate an objective feature of precipitation characteristics and its anomalies over four recent decades by using Asfezari National Dataset (AND).
Data and Methods
In the present study, the gridded precipitation data of the third version of AND with spatial resolution of 10×10 km during the time period of 1970/3/21 to 2016/3/19 (46 years including 16801 days) is used. This dataset adopted from 2188 synoptic, climatology, and rain gauge stations and subjected to interpolation by using Kriging interpolation method. The dataset covers an area from N and E. Accordingly, a pixels cover the area for 16203 days. Consequently, the dataset includes dimensions.
General spatial features of Iran precipitation for the whole under investigation period was studied based on climatological annual precipitation. Next, the same characteristics calculated for four decades ending up to 2016/3/19. Finally, for every decade the anomalies of precipitation in compare with the whole understudy period and its previous decades calculated in order to discover the spatial pattern of decadal fluctuation in precipitation.
Discussion
General characteristics of annual precipitation
Annual mean of precipitation over Iran is 250.5 mm. Due to decline in temperature contrast and strength of fronts in the Mediterranean cyclones, as a main source of precipitation in Iran, the annual precipitation over Iran decreases from west to east, and from north to south.
The annual precipitation in 63.2% of Iran is lower than the climatic annual mean. The annual mean of precipitation in this area which generally located in east and south of the country is approximately 150.5 mm. Thus, the total precipitation in this area is equal to the total precipitation in the rest 36.8% of the country which its annual precipitation is more than the annual precipitation in the country, 422 mm. The spatial variation of precipitation is confirm by other statistics, for instance, skewness, kurtosis, the extreme threshold indices. For instance, a large part of Iran (26.73%) includes 100-150 mm annual precipitation, whiles the precipitation in 15.8% of the country reaches to 150-200 mm. Parts of northeast of Iran, and the coast of Persian Gulf and Oman Sea in the south, in addition to southern slops of Alborz mountain chain experience a precipitation amount of lower than 100 mm. In contrast to the above-mentioned dry regions, the (approximately) wet regions include limited areas for each precipitation class. For example, only 9.1% of the country characterized with 500 mm of precipitation, while the classes of 200-300, 300-400, and 400-500 comprise 20.62, 12.64, and 6.11 percents of the country, respectively.
Decadal variation of precipitation
In current section the spatial distribution and statistical features of precipitation in each decades was illustrated. The following list includes our finding of statistical - graphical analysis of precipitation in four successive decades:
1) The difference between spatial mean and median of annual precipitation increased from the first to the last decades. The increasing in this characteristic refers to increase in spatial asymmetrical distribution of precipitation over the country.
2) A comparison between spatial distribution of precipitation maps showed that generally, the areas experienced precipitation above the decadal and whole period average are decreased from the first and last decades.
3) The increase in spatial skewness from the first decade to the last decade is another evidence of increasing in precipitation spatial differences.
4) The last but not the least finding is the changes in the extreme threshold indices. From the first to the last decade, the range of 10th and 90th percentiles have increased.
Conclusion
Previous studies depicted that the amount of Iran precipitation has decreased over recent decades. In order to investigate the role of each decade in the decreasing values, the gridded precipitation data of the third version of AND with spatial resolution of 10×10 km during the time period of 1970/3/21 to 2016/3/19 (16801 days) is used. General spatial features of Iran precipitation for the whole under investigation period was investigated based on climatological annual precipitation. Next, the same characteristics calculated for four decades ending up to 2016/3/19. Finally, anomalies of precipitation in compare with the whole understudy period and previous decades calculated in order to discover the spatial pattern of decadal fluctuation in precipitation.
Our finding showed that by and large, precipitation has decreased over recent decades. The changes has been more pronounced in southern and northern coastal area, western slopes of Zagros mountain chain, and northern slopes of Alborz mountain chains. Previous researchers attribute these changes to changes in humidity advections in recent years.
Climatology
Hossein asakereh; Farieba Sayadi
Abstract
Artificial neural networks as a nonlinear techniques in climate and hydrology studies are important to have. Climate change and the global warming of the climate phenomenon known as persistence of drought followed Number of dry days. In this study, the data of daily rainfall during the period (1976-2008) ...
Read More
Artificial neural networks as a nonlinear techniques in climate and hydrology studies are important to have. Climate change and the global warming of the climate phenomenon known as persistence of drought followed Number of dry days. In this study, the data of daily rainfall during the period (1976-2008) and artificial neural network in MATLAB software is used to predict the number of dry days Tehran station. Feed-forward type of network used by the algorithm reduces the gradient and Levenberg Marquardt is in the process of teaching and learning. Various structures in the input and hidden layers were tested during the training phase. Finally, a network with 4 inputs and 5 neurons in the hidden layer and 1 neuron in the output layer to best structure (4-5-1) with the highest correlation to predict the optimal answer. The results showed that the aforementioned stations, dry days predicted by the network during the period under review increased compared with that by calculating the probability of dry days during the period (2018-2009) using a Markov chain, the above been approved. The correlation coefficient values predicted dry days without a genetic algorithm combined with 86 percent .After teaching network as genetic algorithm combined with 88 percent that able providing algorithm combined to network result passable showing
Hossein Asakareh; Ali Bayat
Volume 17, Issue 45 , November 2013, , Pages 121-142
Abstract
Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, ...
Read More
Principal Component Analysis (PCA) is an optimum mathematical method to decrease variables into some limited components in order to justify the highest variance of primary variables. In this study some statistical characteristics of annual precipitation of Zanjan city including sum of annual precipitation, number of rainy days, extreme daily precipitation in a year, the ratio of extreme precipitation to the sum of annual precipitation and some characteristics such as Standard Deviation (SD), Skewness (Sk), Kurtosis (Ku), Absolute Mean Deviation (AMD) and Mean Absolute Interannual Variability (MAIV) were was calculated from monthly precipitation for each year, and were introduced principal component analysis technique. The results show that 95% percent of annual precipitation variations can be explained through 4 components. The first component which indicates the highest data variance (42.6%), represents annual precipitation and absolute variability indices including SD, AMD and MAIV. The second component represents the shape of frequency distribution indices (Sk, Ku), the third component represents extreme precipitations and finally the fourth component represents the number of rainy days. The analysis of the trend of components scores show that first and fourth components scores have a significant decreasing and increasing trend, respectively. Round a lines show a precipitation decrease during the period under study from one hand and having uniform temporal distribution on the other hand.
Hossein asakereh
Volume 16, Issue 39 , May 2012, , Pages 73-88
Abstract
Understanding the heavy precipitation behaviors tend toward easy planning, designing, act and management of the water recourses. There are many definitions on heavy precipitation in different professional references. Two important extreme indices are maximum precipitation and five highest precipitations ...
Read More
Understanding the heavy precipitation behaviors tend toward easy planning, designing, act and management of the water recourses. There are many definitions on heavy precipitation in different professional references. Two important extreme indices are maximum precipitation and five highest precipitations in a year. One characteristic of heavy precipitation is variation in time and space. Accordingly’ it is important to study this phenomenon by high resolution in time and space. To investigate heavy rains in Zanjan, daily precipitation during 1961-2006 have been analyzed. The trends of maximum precipitation and their ratio to annual precipitation, the trends of five highest precipitations and their ratio to annual precipitation have been modeled by Non-parametric methods. The results in two scales (annual and monthly) show no trends in time series, while there are high fluctuation periods during 1961-1973 and low fluctuation periods during 1974-2006.
