Journal of Spatial Analysis

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Classifying daily climate circulation patterns has always been considered by climatologists. Investigating climate changes such as rainfall and the temperature in a same single time and place suggests that these changes are strongly influenced by atmospheric circulation patterns.

Regarding so, climate changes, known as variables here, such as rainfall, temperature, and other related phenomena, which are exemplified as flood, drought, glacial, and etc. are associated with special types of climate circulation patterns. The continuity and alternation of the systems are classified or identified climatically, therefore weather classification system is one of the main objectives of the synoptic climatology (Huth, 1996). Since every weather type creates its own special environmental condition, lack of identification in weather type frequencies leads to a difficult environmental explanation and alternation (Alijani, 1380: 64).

Identifying atmospheric circulation patterns different things that can be expressed inductively such as frequency, intensity, and spatial distribution of climate changes in rainfall and its physical causers (VicenteSerrano and LopezMoreno, 2006).

Heavy rainfall in many watersheds, particularly in the basin and sub-basin which involve less time exposure, causes floods and it also damages human, natural resources, infrastructure utilities and equipment. Before the occurrence of this kind of rainfall, it requires a deep understanding of the synoptic systems of their creator. This understanding is only possible through the classification and identification of rainfall patterns which used to cause floods in the studied basins.

The present study also aims at identifying and classifying the synoptic patterns of rainfall during the statistical stage of the study in the basin which caused flood in Taleqhan basin.

Taleqhan basin with area of (65/1242) per square kilometers is located in "36֯, 5', 20" to "36֯, 21', 30" north latitude and "50֯, 36', 26" to eastern longitude "51֯, 10', 18".

The study area is 120 kilometers away from North West of Tehran and located in a relatively high mountainous area in Alborz Mountain. This area is ranging from 1700 meters to 4400 meters above sea level. Average rainfall in this basin ara is 515/16 mm and its annual temperature fits 10.5 centigrade.  About 79 percent of rainfalls occurs from the cold weather period in November to March. It is also know as semi-humid cold weather based on the De Martonne classification.

Circulation algorithm (CA) and pattern clustering algorithm (PCA) were determined based on the daily methods in synoptic scale by applying information from stations in Taleqhan basin (Gateh deh, Dehdar, Dizan, Snkranchal, armouth, Ange, Joostan, Zidasht). In order to classify the weather type, daily average rate of 500 HPa and the sea level pressure (SLP) were extracted and reconstructed over the period (1980-2011) at the 2.5 degree of NCEP. Selected range includes 608 points from latitude of 10 to the 60 of northern degree, and latitude of 10 to 80 of eastern degree.

Principal components method mixes the interrelated points and reduces the matrix size, so 13 main components are remained that they includes 93 percent of the total variance. This study employs S array and Varimax rotation to identify different types of weather. It also makes use of K-Means clustering method to classify daily weather types. And finally, a matrix was formed in 118×608 dimension for 118 common days of rainfall among stations. All days were divided into four groups. They offer the most common climate circulation patterns in the proposed area. At the end, and finally integrated maps of sea level pressure and 500 HPa were drawn for each weather type. 

According to the results from factor analysis, 13 main elements were selected that they included 93% of the total variance of the data. According to the above mentioned method, all days (118 days) during the statistical period (1980-2011) were divided into 4 groups which provide the most climate circulation patterns in the study area. Then, integrated maps of sea level pressure and 500 HPa range were drawn for each of the types. Clusters were numbered according to the K-Means arrangement, and they were named based on the pressure patterns and the way circulation lines were ordered.

The classification shows two different resources for rainfall in this basin.

A: Those rain systems that are entered to the country from the West and South affect this basin. These systems humidity are caused by the Red Sea, the Mediterranean sea, the Black Sea, and the Atlantic Ocean. (B) Some parts of the Caspian coast rainfalls and the northern part of the Alborz mountain that has received their humidity from the Caspian Sea and it has infiltrated northern high-land, causes the rainfalls. It enters the basin from the wide valley of Sefid Rood. According to the rainfall measuring stations data, the least rainfall area is in western, which includes low-land areas. And the most rainfall area is its northern east. Rainfall in this area, in terms of rainfall time distribution in a year, is the Mediterranean. It does not involve a complete dry climate in summer and it takes 3 to 4 percent of the total rainfall.  Rainfall in the basin, respectively, is distributed in winter, spring, fall, and summer.

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Assessing the Homogeneity of Temperature and Precipitation Data in Iran with Climatic Approach
 
Extended Abstract:
Qualitative evaluation and validation of atmospheric parameters such as precipitation and temperature are the most important condition for statistical analysis in climatic and hydrological researches. In addition, the meteorological and climatological data have a crucial role in transportation, agriculture, urbanization and health services.  Therefore, it is clear that using wrong data source for atmospheric investigations is the first hazard in natural hazards analysis. This study aimed to investigate the homogenization of minimum and maximum temperatures and precipitation data for 36 weather stations over different climatic classes in Iran. The Standard Normal Homogeneity Test (SNHT), (Alexanderson and Moberg, 1997), Pettit test (Pettit, 1979), Cumulative Deviation test (Buishand, 1982) and Worsley’s Likelihood Ratio test (Worsley, 1979) were carried out to study homogenization of minimum and maximum temperatures and precipitation data (1966-2015). The results revealed that 91.5 % and 88.5 % of minimum and maximum temperatures data, respectively, were in non-homogenized category. Although, Isfahan, Saghez and Gorgan for minimum temperature and Bandar-e Anzali, Sharekord, Kashan and Saghez for maximum temperature showed a homogenized condition with 5 % level of significance. The results showed most of the weather stations (28 out of 36 stations) had homogenized precipitation data. Even though, seven stations including Birjandd, Kerman, Kermanshah, Saghez, Sanandaj and Tabriz had homogenized precipitation data. The Urmia weather station was in doubtful class. That is precipitation data of Urmia weather station were homogenized by two tests results and were non-homogenized with other two tests of homogenization. The spatial distribution of trend variations of minimum temperature average was between -2.8 to 2.8 degree Celsius over the country. Moreover, maximum and minimum variations of minimum temperature occurred in northeast and northwest of the country, respectively. There were a significantly increasing trend (p<0.01) in most of the regions. The results also indicated that the significant variations happened for maximum temperature in most of the weather stations, mainly in northern half of the country. The minimum temperature jump was mostly found in 1985, 1994 and 1998 years during the study period (1966-2015). The maximum variations of minimum temperature were in Mashhad, Shahroud, Ahvaz, Yazd and Semnan weather stations with 2.8, 2.3, 2.2, 2 and 2 degrees Celsius, respectively, jump for above mentioned years during 1966-2015. In addition, the minimum change in minimum temperature was occurred in Birjand, Urmia and Bandar Abbas with a jump of 0.6 degrees Celsius. It should be mentioned that, unlike other stations, the Khorramabad (Lorestan Province) and Fasa (Fars Province) had a decreasing trend for minimum temperature. It changed from 10.3 to 8.3 and from 11.8 to 10.2 degrees Celsius in Khorramabad and Fasa, respectively. The results showed that the commencement of maximum temperature jump for most of the weather stations happened in 1998 with 1.1 degrees’ Celsius change. According to our study, a remarkable decrease in precipitation data was occurred in west and northwest of the country. There was a depletion of 80 to 150 millimeters from 1998 in Tabriz, Sanandaj, Saghez and Kermanshah weather stations during study period (1966-2015). Besides, 25 to 45 millimeters reduction in precipitation was found in south and southeast of the Country which has arid climate including Birjand (South Khorasan Province), Zabol (Sistan and Baluchestan Province) and Kerman. It was revealed that the variations of minimum temperature were larger than maximum temperature which was in agreement with results obtained by Rafati and Karimi, 2018. The results showed that the start of increasing maximum temperature in most of the weather stations was in 1998. It could be due to increasing the global temperature which is in accordance with results found by Steirou and Koutsoyiannis, 2012. The results revealed that about 80 % of precipitation data of weather stations were homogenized. These results were in agreement with results obtained by Hosseinzadeh Talaee et al., 2013. The results indicated that tests of homogenization for minimum and maximum temperatures and precipitation data could use in different climate over the country. Therefore, it could not allocate a single test to a particular climate type. In conclusion, it should be noted that before any analysis pertaining to environmental hazards, the calibration and maintenance of the weather instruments should be carried out periodically. In addition, the metadata and station history for relocation of the weather station should be checked. The relocation can create great changes in meteorological parameters due to elevation, latitude, longitude and land use/land cover differences between two sites.
 
Key Words: Homogeneity tests, Climate Data, Weather Station, Metadata
 
 
 

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Tehran metropolitan with its large population, daily migrant workforce and many students, needs to planning and designing watch/warning system to reduce the climatic problems for human health.for this purpose, we need to study the climate accurately and Since the factors affecting the climate of warm and cold periods in Iran are different, in this study , the meteorological variables of Tehran warm period (May to September 2002) turned into 4 components in Temporal Synoptic Index (TSI) using PCA Method and using P-Array and Varimax rotation.By the scores of components for each day, the clustering method (in ward method) were used and, the warm days of the year was divided into two cluster named favorable and oppressive airmasses. The average maximum air temperature that is more effective in mortality, was 36.13 ° C. Days with temperatures above  34 ° C, less pressure, mild winds , dryness and more sunshine resulted in more adverse weather conditions, which resulted in a 34% increasing in mortality compare with favorable weather. The total number of deaths from cardiovascular disease during the study period was 154046 that about  67%of deaths have been simultaneous with oppressive airmass.The epidemiological study of mortality also confirms the results of previous research in this area and shows that the incidence of mortality is higher in older people as well as in men. It is clear that not all mortality can be attributed to the effects of climate, but results show that change in climatic conditions will affect on mortality and also for study the effect of climatic hazards on human health, it is better that we study the effect of all variables together on humans.

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Combat readiness in military units indicates the ability of the unit to perform military missions. To conduct research and extract weather threats in the southeastern region, data from 19 synoptic stations with a statistical period of 22 years were used and to prioritize weather threats, combat readiness criteria were used. Dust storms, heat stress, heavy rainfall and sultry conditions were identified as the most important weather threats affecting combat readiness in the region. Based on the hierarchical analysis of dust storms, the most important and effective threat to the combat readiness of military units was identified, the incompatibility rate was calculated and its value was less than 0.1, so pairwise comparison, weighting and prioritization of threats were confirmed. The zoning map of each weather threats was prepared in GIS software and finally, using the weight of each weather threats, it was obtained by combining the zoning maps of the identified threats and using fuzzy hierarchical analysis, a comprehensive map of weather threats was prepared. Zabol and Zahak synoptic stations in spring, the whole region in summer, Saravan station in autumn and Iranshahr, Saravan, Minab and Kahnooj stations in winter are the worst places for military units and also in autumn the lowest frequency of hazards in the region. We witnessed and based on weather maps, this season was recognized as the best season for the deployment and operation of military units in the region.