Background: Brucellosis (Malta fever) is a major contagious zoonotic disease, with economic and public health importance.Methods To assess the effect of meteorological (temperature, rainfall, humidity, and wind) and climate PARAMETERS on incidence of brucellosis, brucellosis distribution and meteorological zoning maps of Zanjan Province were prepared using Inverse Distance Weighting (IDW) and Kriging technique in Arc GIS medium. Zoning maps of mean temperature, rainfall, humidity, and wind were compared to brucellosis distribution maps.Results: Correlation test showed no relationship between the mean number of patients with brucellosis and any of the four meteorological PARAMETERS.Conclusion: It seems that in Zanjan province there is no correlation between brucellosis and meteorological PARAMETERS.

Precipitation is one of the key elements in the climate of each region. Decrease or increase precipitation, climate and environmental phenomena that impress direct effects of climate on human life are significant. Statistical techniques are a useful tool for predicting the behavior of the climate variables. In this study, using statistical methods, precipitation behavior is analyzed in Khoi meteorological station. For this purpose, statistical data of annual average Precipitation during the period 960- 2011 have been used. Using the methods like Pearson, Spearman and Man-Kendal, we have attempted to investigate the precipitation trend. The results of application of these methods show significant decreasing trend in annual rainfall in Khoi meteorological station. By applying a spectral analysis method based on precipitation data, its full cycle, was evaluated. The results of spectral analysis showed that at 95% confidence level, the first harmony was significant. Finally the Arima model was used to predict annual precipitation in the study area. Four basic models were fitted. Goodness of fit tests, including tests of coefficients, remained independent test of the model, using Akaike and prediction model, indicates that between the four models fitted, Arima model (1, 1, 0) is the best fitted to annual precipitation. Based on this model, Khoi meteorological station annual precipitation was predicted in 95 percent level by 2016.

One of the primary stages of climatic architectural design is the knowledge of the environmental conditions and METEOROLOGY data analyzing according to human thermal comfort and usage of building material. Nowadays access to METEOROLOGY data of all Iran METEOROLOGY stations is available on the website of the Iran METEOROLOGY organization. Therefore every architect who feels himself/herself responsible for the effect of design on environment and energy demand can easily analyze the data if he/she has reliable awareness of how to do it and how to convert it to architectural principles. This article introduces the methodology of analyzing METEOROLOGY data for architectural needs. How to choose the METEOROLOGY station for a site design is the first step of this process. It is important to pay attention to the four climatic layers that are macroclimate, mesoclimate, local climate and microclimate. The feature of the topography of the site and its neighborhood help architect to find out the microclimate changes comparing to the METEOROLOGY data. Choosing the meaningful period of the yearly data for the METEOROLOGY station is the second step. The effect of the global warming on the site design will be observed on the graph of the yearly temperature changes in different years. The graph of the heating and cooling degree days in the all yearly data of the station will show the heating and cooling needs changes in recent years. If the global warming has affected the place, it is better to choose ten to fifteen recent years data for analyzing process instead of a long term period (even if the station has more than 30 to 50 years data). After choosing the station and the period of the time, it is necessary to draw the important graphs of the climatic phenomena that will affect the design decisions. Relationship between temperature and humidity is very important on the thermal sensation of human body. There are several indices prepared for this requirement. One of the best indices that will show this effect on human body and the recommended design considerations simultaneously is "Building Bioclimatic Chart" proposed by Givoni. Besides it is one of the most reliable building thermal comfort indices. Therefore this index and the method of how to use it is introduced here. Using temperature and relative humidity, thermal condition in the building is analyzed by this index. At the next stage the drawing method of "climatic needs calendar" and "wind calendar" for a period of a sample year is introduced as a conclusion of the thermal condition in the building. How to analyze these two calendars to achieve design strategies is introduced here. Being familiar with these calendars and the way of their analyzing is a simplified and fast method of extracting the design principles without being involved with difficult calculations of thermal comfort formulas. At the end the method of grasping the complimentary design strategies according to other METEOROLOGY data-such as precipitation, freezing, dew point, heating degree days and cooling degree days, sunshine hours and sky condition – would be explained. It is hopeful that this information will give a reliable method to architects for making better decisions in climatic design concepts.

Introduction: Thunderstorms are one of the most important, abundant and uncontrollable weather climates which cause human casualties all over the world, as well as perish large quantities of crops and infrastructure developments each year. Just between 1953 and 1957 financial losses caused by the hurricanes in America were over 3.6 million dollars (Changnon, 2003, p.1231). Many storms with different intensities have been identified, while their horizontal range is several tens of kilometers, their vertical range is about 10000 meters, and their lifetime is 30 minutes (Henderson, 2006). Often, these storms are formed by cumulus, rainy cumulus and tower-shaped clouds. Due to excessive warming of surface, thunderstorms are usually developed either by air mass or air front, especially cold front. Therefore, thunder is formed by air mass or has frontal origin. Thunderstorm climate adventure, as part of natural climate in northwest of Iran. compels extensive social-financial and bio-environmental losses on the people, especially farmers and stockmen every year (Khoshhal Dastjerdi, & Ghavidel Rahimi, , 2007, p.101). Sari Sarraf et al. (2007, p.123) have studied showery precipitation in the southern part of Aras river, and concluded that the most important reason for showery precipitation in this area is mainly due to two factors: local instability (in warm seasons of year) and cold front coming (in cold seasons)...

During the 30 past years using from climate information has increased in agricultural section. Although METEOROLOGY information has an important effect on agricultural production and reducing risks but the efforts to communicate this information to farmers will need to address this information are effective in different stages of agricultural production.The purpose of this study was to appoint the effectiveness of METEOROLOGY information from farmer,s viewpoint in Golestan province. The methodological approach was a descriptive and survey type.The target population in the study consisted total farmers in Golestan province, Iran (N=130000). By using multistage cluster and simple random sampling techniques 322 farmers were chosen. Data were collected through a questionnaire and were analyzed using SPSS, V.11. Content and face validity of the instrument were obtained by the faculty members of agricultural extension and METEOROLOGY specialist. The reliability analysis was conducted with completing 30 questionnaires and Cronbach, s alpha value was 0/80. The results obtained from analytic statistics indicated that there are significant and positive relationships between independent variables named rate of wheat production(95%) and charachteristics of METEOROLOGY information named correctness, completeness, reliable, opportuneness, careful, easy to understand, answer to farmer needs, attractiveness, utilizable for all farmers, increasing of previous knowledge of farmer, applicable, usefulness, applicable by farm possibilities, observability of result utilization, creditable, farmer desire for reliance to information and farmer desire for decision making based on information(99%) and dependent variable namely viewpoint of farmers about effectiveness of METEOROLOGY information. Optimizing use of agroMETEOROLOGY information, it is necessary for meteorologist and extension worker to pay attention to effectiveness of METEOROLOGY information in different stages of agricultural production.

Water resources management, forecasting, and decision making require reliable estimates of precipitation. Therefore, a normal and an inevitable part of any hydrological or water management project before starting the research is analyzing the accuracy of time series. For this purpose by using statistical tests, the time series are analyzed heterogeneous and improbable fluctuations of data and if needed are corrected or omitted. Although usually passing the statistical tests, scientifically verifies the efficiency of the data, more precise verifications on the data may lead to different results. Therefore, in this study by choosing precipitation data of Urmia basin as a case study, final results of some statistical tests on the efficiency of the data are analyzed. Careful and precise analysis of the time series especially in comparison with neighboring stations shows that full reliance on the statistical tests alone is not enough for analyzing the efficiency of the time series and the results of these tests may mislead users on the true condition and efficiency of the data. Based on the results of this study, only 2.4 percent of the data need correction or must be omitted; nevertheless, more precise analysis through data shows that about 12.6 percent of the data are completely unsuitable and must be omitted.