IRANIAN JOURNAL OF GEOPHYSICS
Year:2019 | Volume:13 | Issue:2 |Papers Count:11

In this study, the “ MPI-ESM-LR” model output from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used to assess the response of the North Atlantic (NA) and Mediterranian storm tracks to climate change. Historical scenario is used for the past and RCP8. 5 scenario is used as the projection for the future period. The conservation of wave activity is used as a diagnostic tool to investigate the eddy activity dynamics. A pair of large centers of divergence-convergence for the horizontal wave activity flux (wave packet) in the NA region forms the signatures of the NA storm track. The NA storm track has a double-branch structure consisting of northern and southern branches. The Mediterranian storm track is identified by a pair of positive/negative centers of wave activity flux in the west/east of the Mediterranian sea. The convergence area extends from the eastern Mediterranian and north-eastern Africa to the Middle-east and western and south-western parts of Iran. The dynamical analysis of the MPI-ESM-LR results shows that the response of the uppertropospheirc wave activity, propagation and breaking in the northern and southern branches of the NA storm track to global warming, determines the changes of eddy activity in the northern and southern latitudes in its downstream sectors from Europe to Siberia and from Mediterranean Sea to Southwest Asia, respectively. In winter, intensity and number of wave packets decrease in both northern latitudes from the northern branch of the NA storm track to the Scandinavia and Siberia and southern latitudes in the southern branch of the NA storm track, the Mediterranian storm track and the Northern Africa region, while the central branch of wave activity in the middle and eastern NA and its downstream wave packets in the central Europe– Black Sea– Caspian Sea turns out to be the dominant path for the storm activity in the future. Moreover, the eastward flux of wave activity decreases in both the northern and southern latitudes, while it gets stronger in middle latitudes. These results indicate that the doublebranch structure of the storm track in the NA and its downstream region in Europe and west Asia will turn to a single-branch pattern at the end of 21st century. Furthermore, the wave breaking and wave packets maxima associated with both the NA and Mediterranian storm tracks and the central branch of wave activity in the Europe– Black Sea will also undergo an eastward shift. Corresponding to that, the tongue of high values of wave activity in the central Mediterranean will also move to the eastern Mediterranian and the tongue of low values of wave activity in the middle-east will disappear. This leads to a considerable increase in penetration of both the Mediterranian and Black Sea wave packets and wave activity to Iran which may result in higher synoptic wave activity in this country in a warming climate.

The dependence of numerical models on the selected domain, in turn, affects the accuracy of prediction and simulation of Tropical Cyclones (TCs) and is considered as a very serious challenge. In the first part of this study, the WRF model was used to determine the sensitivity of the track and intensity of TC GONU to a selective domain. In the second part, the performance of assimilation 3D in order to reduce the dependence of the sensitivity of the TC Gonu simulation to the selective domains was evaluated. The Gonu was the strongest TC occurred over the Arabian Sea. The peak intensity of TC Gonu was estimated 140 knots and 130 knots by Joint Typhoon Warning Center (JTWC) and India Meteorology Department (IMD), respectively. Four domains were separately selected. All of the simulations in this study were initialized at 00 UTC of 2 June for six days. In all simulations, authors used the data from NCEP global final analysis (FNL) on a 1. 0° ×1. 0° grid to provide initial and boundary conditions. Despite the little difference in selective domains, the results in the first section showed the simulated tracks differed compared with each other, considerably. For performing simulations in the second part, the QSCAT, BUOY, METAR, SHIPS, SONDE, and SYNOP data to number 2064, 30, 63, 18, 37, and 208, were used, respectively. The results in the second part showed that assimilation of the satellite and synoptic data at the time of the start of the model, lead to improving quality of the first guess data. Therefore, the accuracy of the simulated tracks in all selected domains was enhanced and reduced the sensitivity of TC Gonu simulation to the selected domain. Regardless of the great difference in simulated tracks, especially in the case of no use of assimilation, which in turn influences the intensification of the TC, in all of the simulations, the simulated intensity during the intensity peak of the TC is higher compared with the IMD reference data and is less compared with JTWC reference data. Since during the simulations, the sea-surface temperature has been used constantly and on the other hand, the exact values of sea-surface temperature have a significant impact on the intensity of the TC simulation, the WRF model coupled with an ocean model for accurate determination of sea-surface temperature during simulation can improve the accuracy of the results of this study. There is, of course, another way to improve the quality of the results, when results depend on the selective domains. For every domain, one simulation is performed and the average of the simulations is considered (ensemble forecast). The high amount of time spent in this method is considered as serious trouble. It should be noted that in regional models, the sensitivity of simulations to the selected domains is also highly dependent on the boundary conditions, which should be considered.

Due to the approximately spherical nature of the atmosphere, oceans and other layers of the Earth and the complex nature of atmospheric and oceanic flows, numerical solution of their governing equations requires using an appropriate coordinate on the spherical geometry. All spherical grids defined for the spherical surface or shell, generally have their own advantages and disadvantages. In general, it can be said that there is no spherical grid which has all the following characteristics: 1-The grid is orthogonal; 2-There is no singularity; 3-There is no grid convergence problem; and defined over entire spherical surface. Thus, we have to discard one of these incompatible conditions. An overset grid is a type of grid that divides a spherical surface into subregions. Yin– Yang grid belongs to the family of overset grids. This coordinate is composed of two grid components named Yin and Yang with partial overlapping at their boundaries. Some of the advantages of the Yin– Yang grids are as follows: 1-Yin and Yang grid components are both orthogonal and based on the conventional latitude– longitude grid; 2-The singular points are absent; 3-The metric factors of the both grid components are analytically known; 4-The grid lengths are uniform approximately; 5-It requires less grid points than the conventional latitude– longitude grid; and 6-We can adapt the available latitude– longitude discretization and codes for the use with the Yin– Yang grids. In addition, we have to use interpolation for setting boundary conditions for the two grid components. The interpolation scheme that has been used in this study is bilinear. In this research, a type of the Yin– Yang grid called the rectangular (basic) is applied to solve the two-dimensional advection equation for a well-known test case using the fourth-order compact MacCormack scheme. Furthere, the fourth-order Runge– Kutta method is used for time stepping. Results show that using the Yin– Ying grids to solve the advection equation is highly effective in reducing the computational cost compared to the conventional latitude– longitude grid, however the use of rectangular Yin– Yang grid entails a lower accuracy than the conventional latitude– longitude grid. In this numerical test, global errors are computed using the absolute-value, Euclidean and maximum norms. By calculating the errors using these norms, there is an order of magnitude increase in the errors in rectangular Yin-Yang grid compared to the conventional latitude– longitude grid. This increase in error can come from the inevitable interpolation process involved in the Yin-Yang grid.

In this research, the possible effects of NAO, on Tehran air quality during 2007-2016 were investigated. First, the daily indices of air quality of Tehran in the autumn and winter seasons for the study period were applied to identify polluted and unpolluted periods. The 1th, 5th and 9th deciles of the air quality indices were chosen as the indicator for good, middle and bad air qualities. Based on these indices, five polluted and eight unpolluted periods were identified. Then, the daily indices of NAO and air quality of Tehran were used to calculate the Pearson correlation coefficients and discuss the relationships between air quality and the NAO indices. In this regard, we attempted to find out the optimum time lag for each case by examining different times that there was maximum correlation between air quality index and the NAO indices. For dynamical study, the European Centre for Medium-Range Weather Forecasts (ECMWF) (ERA-Interim) reanalysis data set including mean sea level pressure, wind, temperature, geopotential height, and specific humidity in a time period from 1979 to 2017 were used. The horizontal resolution of the initial data is 0. 75° ×0. 75° in longitudinal and latitudinal directions prepared operationally every six hours at 60 levels. The statistical analysis of the NAO indices shows that in the polluted periods, the positive phase of NAO is dominant, while there is no significant statistical difference between the positive and negative phases of NAO in the unpolluted periods. In general, the unpolluted periods (five cases) are associated mostly with the negative phase of NAO. Because of this limitation, we decided to analyze the synoptic-dynamic situations of all cases using the anomaly maps of the quantities relative to their long means. Thus, it is possible to improve the reliability of the results concerning the the effects of NAO on air quality in Tehran. Synoptic-dynamic analysis of the cases with the highest correlation between Tehran air quality index and NAO daily indices indicates that in the positive phase of NAO, there was advection of warm and humid air to the study area, while the Middle East region had a cold and dry conditions. On the other hand, when NAO was in the negative phase, advection of moist and colder air from the Atlantic Ocean toward the east occurred, thereby existing better air quality in the region. Besides, when polluted air period coincided with the negative phase of NAO, in contrast to the normal situation, the deviation of the Siberian high-pressure axis into the meridian and adhering to the Azores high-pressure created a barrier against the westerly winds causing their meridional deviations. In the unpolluted air periods associated with the positive phase of NAO, in contrast to the normal situation, the positioning of the Atlantic highpressure at higher latitudes, with respect to the mean state, and joining with the Siberian highpressure make them act as a barrier in the middle latitudes, thereby existing zonal winds in the study area.

All objects whose temperature exceeds absolute zero (-273° C) can emit energy. The amount of energy emitted from the objects depends on their temperature and can be measured according to Stephan-Boltzmann's law. The maximum emission of this energy is at a certain wavelength defined by Planck's law. Regarding the surface temperature of the sun, it emits maximum energy at a wavelength of 0. 48 microns, in the middle of visible waves, while the Earth emits its maximum energy at 10 microns (infrared) wavelengths. This radiation which starts from 3 microns and continues to 100 microns (infrared), is known as Outgoing Long Radiation (OLR). Measuring this radiation is very important for understanding the energy balance and the temperature of the Earth. Because of the difficulties in measuring this radiation, the use of remote sensing data can effectively help in understanding the tempo-spatial variations of OLR. The purpose of this study is to estimate the seasonal trend of Iran’ s outgoing longwave radiation by using National Oceanic and Atmospheric Administration (NOAA) satellites. In this study, the daily mean outgoing longwave radiation data for the period 1988/3/21 to 2018/3/20, with 1° spatial coverage, was extracted on a global scale from the United States Climate Data Record (CDR) database. Then, based on nearly 700 million pixels, the seasonal mean of Iran’ s outgoing longwave radiation was calculated for each year, and a time-space matrix was obtained with dimensions of 154*30, for each season. The rows of the matrix are locations (pixels) and the columns are the time (season). For each season of the year, the nonparametric test of Mann-Kendall was calculated at a confidence level of %90 for each individual pixel. The results showed that there was no negative trend in different seasons in Iran, and only in winter, Iran's territory has an extensive positive trend. Hence, the outgoing longwave radiation does not show trends in other seasons of the year. The positive trend of the outgoing longwave radiation during winter is due to cloudiness and snow in most of Iran. Also, in this study, the long-term mean outgoing longwave radiation pattern of Iran was calculated for each season, separately. Findings of the long-term mean of the seasons showed that outgoing longwave radiation depends on latitude and topography of the earth. So, the highest outgoing longwave radiation is seen in low and flat latitudes (especially in summer) and the lowest one is seen in high and uneven latitudes(especially in winter).

Meteorological time series are used as important input for risk forecasting and related warning systems. Wind is one of the most important atmospheric parameters because of its extensive effects in many industries and fields of human life. Many researches have been carried out to improve forecasting of the wind with the aim of improving output of wind farms, issuing warning for public, detection of wind shear and turbulence in the airports and so on. Generally, there are two main groups of meteorological forecasting methods, one is based on physical relation of atmospheric parameters, and the other is based on historical data. For a long time, time series of wind have been used for forecasting the wind speed. ARMA (Auto-Regressive Moving Average) and Markov model are two important groups of time series analyzing methods. In this paper, the capability of HMM (Hidden Markov Model) is described and used for identification and classification of wind time series. Based on theoretical concept of HMM, a proper method is proposed, and utilized for simulation with real data. The proposed method is based on constructing a multinomial– HMM on wind direction time series. The whole range of possible wind direction (360 degrees) is divided into 16 groups and then categorized to different regimes. Wind forecasting is then carried out based on these separated categories. Temporal stationary test which is well known for Markov chain, is extended for the proposed method and used for its efficiency evaluation. Efficiency of the proposed model is investigated by using real data of IKIA (Imam Khomeini International Airport). A part of the collected data including wind speed and direction is used for constructing of the proposed model and another part is used for its evaluation. The achieved results show that there is improvement in temporal stationary for HMM vs simple Markov model, in 70 to 80 percent of cases. History of the observations in IKIA shows that there are two major wind directions in the area which are related to the local condition: from mountain to the desert in the day times from north-west and from the opposite direction at nights. These are the only important directions in the area in summer when there are no important meteorological phenomena, while in winter one major direction would be added from south-west because of the large scale meteorological systems. Increasing the number of regimes has also significant improvement in temporal stationary in winter times, while there is no important improvement in summer times. This has a good harmony with long term recorded data.