NIVAR
Year:2020 | Volume: | Issue: |Papers Count:12

One of the main challenges in numerical weather prediction models is the correct rainfall forecasting, especially for springtime convective precipitation. The dynamical recognition of the "rainfall factors" leads us to select "more suitable schemes" for more accurate rainfall prediction. The WRF model has a wide range of physical parameterization schemes. The choice of each group of these schemes can change the model outputs, significantly. But sometimes, there is not enough accuracy in the prediction even using different schemes. Therefore, it is necessary to select the appropriate schemes considering the factors causing precipitation. Here, to investigate the effect of dynamical factors causing the atmospheric instability and precipitation on the appropriate physical scheme selection, two atmospheric systems for springtime precipitation with different dynamic factors have been selected. In the first case, a false forecast of a heavy rainfall leading to floods in the central regions of the Iran occurred, and in the second case, the heavy rainfall in these areas did not forecast correctly; wrong warnings of these false predictions led to management problems for the related decision makers. In this research, initially, the two systems were studied from the dynamical perspective and then, they were simulated using the WRF mesoscale model with nine different configurations. The ERA-Interim was used as the model input data for the region of interest (33° N-36° N, 48° E-54° E). The simulations were done in two nested domains with a horizontal resolution of 45 and 15 km for the first and second domain, respectively. The proper configuration was selected for each atmospheric system according to the dynamical factors and comparing the model outputs with the observations (from some stations in Tehran, Qom, Markazi, Hamedan, and the west of Isfahan provinces). The results show that the type of convection scheme has a significant impact on the prediction of rainfall amount and this scheme extremely depends on the instability factor, which initiates from "the upper and the lower atmospheric levels conditions". Based on the results, for the first case, which its main cause of precipitation is due to the potential vorticity (PV) streamer penetration to the upper level of the troposphere, the Tiedtke convective scheme and the Kessler microphysics scheme are better than the other schemes that overestimated rainfall. In the second case, the occurrence of severe rainfall with the main mechanism of daily convection was not predicted by eight configurations. But in the ninth configuration, the Grell-Freitas convection scheme could successfully predict the convective heavy precipitation due to the better capture of daytime convection over the area of study rather than other used convective schemes.

Homogeneity analysis is one of the important challenges of trend detection in the climate data series. However, the ability of homogeneity tests in detecting heterogeneity differs depending to the parameters or types of changes. Rasht Synoptic Station has long-term climate data which according to the recorded metadata and documents has undergone several location changes. In this study, homogeneous tests including cumulative deviations, Worsley, Normal Standard, Pettit and Bishand were used to test the homogeneity of daily average temperature, air pressure and rainfall data of Rasht station during the period from 1961 to 2018. The results showed that none of the tests were able to reveal any jump in precipitation data. In temperature and pressure parameters, Both Bishand and Pettit tests had more success in discovering the sharpest heterogeneities in accordance to changes in the metadata. However, simultaneous use of multiple test results will lead to more reliable results for identifying heterogeneity. Also, although these tests give a definite date as a jump date, it should be noted that the given dates represent the range of heterogeneity occurring in the least-estimated two years around the real time of the jump. Awareness of the time period of the change of metadata can be useful in choosing the appropriate test. If it is known that potential changes have occurred around the midpoints of the series, Pettit and Bisahnd methods provide a more accurate estimate of the time of the jump occurrence.

In this research, temporal trends of maximum temperature values were investigated in various regions of Iran. For this purpose, the meteorological observations collected from 33 high quality meteorological sites for a 40-year period (1977-2016) were used and statistically significant Tmax trends in the monthly, seasonal and annual time basis were detected by the non-parametric Mann-Kendall and Spearman tests at confidence level of 95%. For eliminating the effect of serial correlation on test results, the Trend Free Pre-Whitening (TFPW) approach was applied. The results indicated that the Tmax trends for some sites were increasing (positive) but for some sites showed decreasing (negative) trends. The highest positive and negative trend on the annual time scale occurred at Kermanshah and Torbat heydarieh, respectively. The positive trend of Tmax on the monthly, seasonal and annual time scale was observed at Mashhad, Tabriz, Abadan, Ahvaz, Boshehr, Shiraz, Kermanshah and Yazd sites. In general, the performances and capabilities of Mann-Kendall and Spearman tests were consistence at verified confidence level.

The importance of using clean energies in other parts of the energy has become apparent to everybody. In the meantime, nuclear energy and its use in generating electricity is part of a relatively clean energy. On the other hand, the incident at nuclear power plants, like the Chernobyl and Fukushima incidents, has long-term and adverse effects on the environment and human health. It also has a continental scale, and the transmission, transference, and transport of radioactive material caused by it is miles away from the site of the incident. Considering the importance of using meteorological models in predicting and tracking the transmission paths of pollutants and, on the other hand, integrating them with pollution diffusion models can reduce some of its consequences and impacts. The purpose of this paper is to investigate the distribution of radioactive substance such as iodine 131 due to the biological effects of this substances in the event of an accident at Bushehr Power Plant, Which due to factors like wind direction and wind speed and topographic conditions of the environment, the amount of propagation and release of this substance is discussed. For this purpose, using the WRF-chem prediction model, the meteorological data input were obtained to the AERMOD Gaussian model. Then, using AERMOD software, was calculated and modeled for dispersion and propagation of this material. For the purpose of model verification and closer examination, meteorological data were simulated in four different seasons of 2018.

Climate change affects all regions around the world and increase some extreme events. Also climate change threatens the Middle East so much. Severe droughts in this region with anthropogenic changes cause heavy sand and dust storms. In this study, dust phenomenon has been investigated in The Middle East during 10 years (from 2009 to 2018) from different perspectives. GOCART model dust flux output shows that the maximum amount of dust flux was in the southeast of Saudi Arabia, southeast of Iraq, region on the borders between Iraq and Syria, western half of Turkmenistan, parts of Kazakhstan and Uzbekistan. Also dust flux was seen in the north coast of Persian Gulf, Oman Sea and borders between Iran, Afghanistan and Pakistan. Also AI pattern is very similar to dust flux pattern and Index EVI was low in a vast areas of The Middle East that shows deficiency of vegetation cover in that areas. In the other hand, mean annual cumulative precipitation is less than 250 mm in the vast parts of study areas.

This study was conducted to inform the World Meteorological Organization and other relevant organizations to annual climate and extreme climate events of Iran during 2018. The extreme events that studied are heat waves, cold waves, heavy rainfall, sandstorms, dust and lightning that are selected according to WMO guidelines. Heat wave and cold wave were calculated by using standard deviation of maximum and minimum daily temperature and percentile index. Sandstorm, dust and lightning are studied base on the syncopated stations records. The average temperature of the country in 2018 was 18. 8 C, based on 27 synoptic stations in the country is increased 3. 1 C compare to the average long-term period (1958-2017) and 0. 4 C compare to 2017. The precipitation of the country in 2017 has increased by 15 percent compared to average long-term period. In 2018, various provinces were affected by climate events, so that the provinces with the highest ranking and most inclusive climate events including: Zanjan (heat wave), Bandar Abbas and Khorasan Razavi (cold wave), Gilan (heavy rainfall), Sistan and Baluchestan (dust and sandstorm), Lorestan (lightning).

Climate change and global warming are some of the most important environmental issues, in addition to direct socioeconomic impacts on human health and well-being and there has been a lot of research in recent years. Enso (Southern Oscillation-El Nino) is recognized as one of the most prominent Causes of Variability on the global scale of year-to-year. ENSO affects both in the tropical Pacific and around the world. In this study, the effect of ENSO on the trend of temperature changes in Tehran province is investigated in the 19 years of winter (2000-2018). To calculate the effect of ENSO on temperature, the analysis of extreme events and the elimination of the effect of other telecommunications, in particular, the Arctic Ocean and the North Atlantic Oscillation, the daily temperature anomalies are calculated relative to the seasonal long-term average, and then Butterworth filter is used. Daily air temperature data at 2 m height were used for 10 meteorological stations in Tehran province and the monthly ENSO index. The results show that the temperature variance relative to the long-term average in Lanina and El Nino conditions is about 8 and 15, respectively. Also, the effect of Lanina and El Nino on the temperature fluctuations is relatively low. In Lannina and El Nino's condition, these fluctuations are regular and irregular respectively; So that, the temperature fluctuated to about 8 in February.

This study was conducted in order to assess the probable effect of climate change on the trend in the maximum extent of temperature variations in three scales including the point (synoptic stations), zonal (included 10 geographical areas in Iran), and national average scales. This study, the trend in the maximum extent of temperature variations of the baseline climatic period (1986-2005) was investigated against three future climatic projections, and also three future climatic projections, future climatic projections (near, middle, and far) until the end of the 21st century. This study has taken advantage of the downscaled outputs of the CNRM-CM5 global model from the set of CMIP5 models under the RCP scenarios. The results showed, while under the future climatic projections (near, middle, and far), this parameter was 0. 7, 1. 2, 1. 9 oC for the RCP4. 5 scenario and 0. 7, 1. 7, and 3. 9 oC for the RCP8. 5 scenario, respectively, which probably will experience an increase compared with the baseline period. The highest rate of increase in the maximum extent of temperature of the future climatic projections compared with the baseline climatic period occurred in the Zagros region, and the lowest rate of increase was in the Khazar region.

Climate change impacts on karst aquifers have not been mainly studied due to the difficulty of modeling these aquifers in relation to alluvial aquifers. However, it is possible to make a relationship between the climatic variables as part of the hydrologic cycle and the groundwater level using the Artificial Neural Networks (ANN). In this study firstly, precipitation and temperature data has been obtained using NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP). Secondly, the groundwater level of three limestone wells, i. e. W1, W2 and W3, has been predicted using ANN in Lali region, Khouzestan Province. The correlation coefficients (R) demonstrate good ability of the groundwater model in simulating the climate change impacts on the karst aquifer. The groundwater level probably decreases for W1 and W2 during the future time period (2021-2050) in comparison with the present time period (1961-1990), while no important changes are predicted for W3.

The global climate change that is currently happening on earth is affecting the ecosystem, environment, and the health of animals, humans and other species. Today, the climate change is one of the greatest challenges that mankind and natural world are faced with. The purpose of this study is to identify the health consequences of climate change on human health such dysentery disease in different climate zone in Iran. The research is a retrospective cohort study which uses secondary data. In data analysis, tests of Poisson regression model were used for longitudinal data. According to the current study, the average monthly maximum temperature and rainy days have the highest correlation with the incidence of dysentery disease in men and women. It also shows that there is a significant correlation between climate and dysentery disease in children and adults over the age of 65 years. The results of current study indicated that climate factors in each climatic zone have different effects on the health.

Introduction Considering the industrial development in recent years, the need for climatological atlas and also daily metrological data have increased and has become important economically. Air temperature is of special importance in our understanding of various natural processes in the nature. Moreover, in order to detect the impact of greenhouse gases on climate change and developing ecological models in various regions, much attention have been given to spatial distribution of temperature. Hence, developing and testing accurate interpolation methods for spatial analysis of temperature is this clear especially over data void regions. In order to successfully transfer information from irregularly distributed observing stations to a regular grid, information about physical characteristics of the region have to be taken into account. To reflect spatially complicated climate patterns at regional scales, climatic dependence on topography must be taken into account when developing reliable climate estimates.

Snow shower is the phenomena that caused problems in mountainous roads of North Khorasan province. The goal of this paper is the analysis of three case studies of snow shower in Bojnurd during 2011-2013. We used Bojnurd synoptic data to determine three snow shower case studies during 2011-2013 and then analyzed using synoptic maps of surface, 850, 500, and 300 hpa. Dynamical analysis is done by using sodar output data, helicity, the cross section of relative humidity and vertical wind speed. The maximum value of helicity was 348 (m2/s2) on 17. 3. 2012 which show sever instability during this phenomena. The vertical wind speed from sodar data in the third case study is very similar to meso scale shear instability. The cross section of vertical wind speed and humidity shows an increasing in relative humidity that accompanying with improvement in upward and downward motions which increase instabilities in the clouds and increase snow shower. Finally we concluded that atmospheric instabilities such as temperature reduction, increase in pressure, humidity and wind speed, increasing in helicity, suddenly changes in wind speed and direction which improve instabilities are the main factors of the formation and intensification of snow showers in the region.