ORIGINAL_ARTICLE
Investigation of replacement and flow direction of granitic body at Golpaygan region using anisotropy of magnetic susceptibility (AMS)
The Golpaygan massive granite in the northern part of Golpaygan city is a part of Sanandaj – Sirjan zone. This granitic body has been intruded in surrounding metamorphosed schists of Paleozoic age. The Paleocene age (58 Ma) with K/Ar method has been assigned for this granite. Basalts, Porphyritic tuffs and Cataclastics volcanic rocks are the main rocks of this formation. The major minerals of the granite are Quartz, acidic to intermediate plagioclase (oligoclase, andesite) and orthoclases which occasionally show pertitic texture. The micaceous minerals include biotite, muscovite and sericite. In order to study AMS of Golpayegan granite, 171 cores with 10 cm length and 2.5 cm diameter were collected with drilling portable machine. The dip and azimuth of the cores were measured with magnetic compass. Each core was cut to 22 mm length in the paleomagnetic laboratory of geological survey of Iran. Bulk samples were also collected in order to examine rocks petrogically and mineralogically. The polished thin sections show the following metallic minerals: Rutile and Anatase, Oxides and oxyhydroxides, Hematite, Pyrite and Ilmenite. Anisotropy of magnetic susceptibility (AMS) is defined as a second order tensor. Due to symmetry of nondiagonal components, only diagonal ones K33, K22, K11 remain which are named as Kmax, Kmin and Kint. Lineation intensity values show alignment of magnetic dipole moments of the specimens. This parameter is maximum for sites 8, 12 and 17. The dip and direction of lineation parameter of the above sites are 261.5/44, 38.2/79 and 22/17 respectively. The dip value of site 12 , i.e. 79, may indicate place of the source of Golpayegan granite. The direction of lineation in sites 17 and 8 are opposite to each other which may indicate the existence of a fault in this area. The map of foliation parameter shows that in the central part of the study area, the dip of foliation has much more value than the surrounding area. The shape factor values are negative in the north east and center of the granite body which indicate prolate shape of magnetic susceptibility ellipsoid while in the other parts it is positive which means it is oblate. The AMS results also reveal that the study area can be divided in to two parts which have separate convergence directions. The diffusion directions may indicate mushroom type of the granite intrusion at two phases. The main mushroom type granitic body has intruded at the first phase and then in the second phase, another granitic body with the same pattern is injected into the main body. The AMS directions of the granite show northwest- southeast and northeast- southwest trends while at the center, they show on east-west trend. On the basis of the interpretation of total granitic body directions, we propose the existing of a probable fault with north-south trend at the center of the granite. The intensity of anisotropy of magnetic susceptibility at the western side of this fault is high in comparison to that of the eastern side. The occurrence of this fault can also be proved by petrological investigation and other studies. The susceptibility-temperature analysis of the granite rocks shows that magnetite and hematite are the main magnetic carriers which may indicate I or A type origin of this massive granite.
https://jesphys.ut.ac.ir/article_69140_acecf0c51eb21a4093cd83dc86404b15.pdf
2019-03-21
1
15
10.22059/jesphys.2019.249507.1006964
AMS
Granite
Lineation
Fault
Sude
Mirzaei Hajibaghloo
su.mirzaei@gmail.com
1
M.Sc. Graduated, Department of Geophysics, Research and Science Branch, Islamic Azad University, Tehran, Iran
AUTHOR
Habib
Alimohammadian
halimohammadian@gmail.com
2
Ph.D. in Geophysics, Geological Survey & Mineral Exploration of Iran, Tehran, Iran
AUTHOR
Mohsen
Oveisy Moakhar
moveisy9@gmail.com
3
Assistant Professor, Department of Physics, Razi University, Kermanshah, Iran
LEAD_AUTHOR
اطلس راههای ایران،1389، موسسه جغرافیایی و کارتوگرافی گیتاشناسی.
1
شیخالسلامی،م.، 1384، نقشه زمینشناسی محلات،1000000/1، انتشارات سازمان زمینشناسی کشور.
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Balsley, J. R. and Buddington, A. F., 1960, Magnetic susceptibility anisotropy and fabric of some Adirondack granites and orthogneiss, American Journal of Science, 258-A, 6-20.
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Bouchez, J. L., 1997, Granite is never isotropic: an introduction to AMS studies of granitic rocks , In J. L. Bouchez. D.
4
Edgardo, C. T., Irene, M. and Rapsoo, B., 2017, Anisotropy of magnetic susceptibility of silisic rocks from quarries in the vicinity of
5
Guimaraes, L. F., Raposo, M.I.B., Janasi, V. A., Canon-Tapia, E. and Polo, L. A., 2018, An AMS study of different silicic units from southern Parana- Etendeks magmatic province in Brazil: Implications for the identification of flow directions and local sources, Journal of volcanology and geothermal research, Vol.355, 304-318.
6
Gregoire, V., de Saint- Blanquat, M., Nedelec, A. and Bouchez, J. L., 1995, Shape anisotropy versus magnetic interactions of magnetite grains: experiments and application to AMS in granitic rocks, Geophys. Res. Letters, 22, 2765-2768.
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Hargraves, R. B., Johnson, D. and Chan, C. Y., 1991, Distribution anisotropy: the cause of AMS in igneous rocks, Geophys. Res. Lett., 18, 2193-2196.
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Horuda, F., 1982, Magnetic anisotropy of rocks and its application in geology and geophysics, Geophys. Surveys 5, 37-82.
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Jelink, V., 1981, Characterization of the magnetic fabrics of rocks , Tectonophysics, 79, 63-67.
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Karimi, S. M., Tabatabaei Manesh, H., Safaei, H., and Sharifi, M., 2012, Metamorphism and deformation of golpayegan metapelitic rocks, Sanandaj-Sirjan Zone, Iran, Petrology, Vol.20, No.7, 658-675.
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Khan, M. A., 1962, The Anisotropy of magnetic susceptibility of some igneous and metamorphic rocks , J. Geophys. Res., 67, 2873-85.
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Lanza, R. and Meloni, A., 2006, The earth magnetism: An Introduction for geologists, Springer.
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Merrill, R. T., MacElhinny, M. W., Macfadden, P. L., 1996, The Magnetic Field of the Earth, Academic Press.
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Mollier, B. and Bouchez, J. L., 1982, Structuration magmatique du complexe granitique de Brame-St Sylvestre-St Goussaud Limousin, Massif Central francais). C. R. Acad. Sci. Paris 294II, 1329-1334.
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Nagata, T., 1961, Rock magnetism , Maruzen, Tokyo, 350.
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Rashidnejad, Omran N., Emami, M. H., Sbzehei, M., Rastad, E., Bellon, H. and Piqne, A., 2002, Lithostratigraphie et historie paleozoiques a Paleocene des complexes metamorphiques de Ia reyion de muteh, Zone de Sanandaj-Sirjan (Iran mezidiornal), G.R.Geoscience, 334, 1184-1191.
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Sadeghian, M., Bouchezb, J. L., Ne´de´lecb, A., Siqueirab, R. and Valizadeha, R., 2005, The Granite Plution of Zahedan: a petrological and magnetic fabric study, Journal of Asian Sciences, 25, 301-277.
18
Skytta, P., Hermansson, T., Elming, S. A. and Bauer, T., 2010, Magnetic fabrics as constrains on the kinematic history of a Pre-tectonic granitoid intrusion, Kritineberg, northern Sweden, Journal of Structural Geology, 32, 1125-1136.
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Stephenson, A., 1994, Distribution Anisotropy: two simple models for magnetic lineation and foliation, J.Phys. Earth Planet. Inter., 82, 49-53.
20
Tarling, D. H. and Hrouda, F., 1993, The magnetic Anisotropy of rocks, Chapman & Hall, London.
21
ORIGINAL_ARTICLE
Fluid replacement modeling in Ilam Formation in one of the southwest Iranian oil reservoirs
Seismic technologies have been recently evolved into a central position in reservoir characterization and monitoring with the recent improvements and its cost efficiency. In this regards rock physics play an essential role by connecting seismic data to the presence of in-situ hydrocarbons. Modeling the effects of pore fluids on rock velocity and density is an essential part which normally is used to detect the influence of pore fluids on seismic signature. In recent years, one of the most important developments in rock physics has been the fast progress toward quantifying the relations between geologic processes and geophysical signatures. This quantification is normally done through application of different types of rock physics models: theoretical, empirical and hybrid models. However, fluid substitution methods make it possible to predict the elastic response of a rock saturated with one type of fluid from the elastic response of the same rock saturated with another fluid. This infers that seismic wave velocity could be predicted in geological formations for any possible hydrocarbon signature based on the measured velocities in the counterpart water-saturated formations. Therefore, fluid substitution is an important part of any seismic rock physics analysis (e.g., amplitude versus offset and time lapse studies), and can provides an efficient tool for fluid identification and quantification in a given reservoir. Fluid substitution commonly performed by using Gassmann’s equation which has already being discussed frequently. In general, Gassmann applicability is questionable in carbonates as it can under-predict, over- predict or even correctly predict seismic velocity changes by changing pore fluids. This is normally attributed to the violation of some of the Gassmann assumptions like their pore space connectivity in carbonates. The goal of this study is to perform fluid substitution and seismic modelling of one of the Iranian carbonate oil field to investigate validity of Xu and Payne (2009) for the carbonate field. This model generally emphasizes the behavior of rocks related to different pore types. Fluid substitution results are then compared and verified with the laboratory measurements of core sample taken from the same reservoir intervals. The final output of fluid substitution is saturated bulk modulus, shear modulus and density for either of the defined saturation scenarios. Our results show that Xu and Payne (2009) can be used on the studied reservoir. Also, the obtained results were confirmed using other source of information like ultrasonic measurements. Furthermore, this model was used to model frame bulk modulus as an input into the fluid substitution purposes. The results of the fluid substitution confirm the applicability of the introduced approach to discriminate different fluid responses in this field.
https://jesphys.ut.ac.ir/article_69147_8a685e4743c6eeefb7c941e66bb2e7f2.pdf
2019-03-21
17
30
10.22059/jesphys.2019.259047.1007012
Fluid detection
Carbonate reservoir
Rock Physics model
Fluid Replacement Modeling
Javad
Sharifi
jv_sharifi@yahoo.com
1
Ph.D. Student, Department of Geology, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Naser
Hafezi Moqaddas
nhafezi@um.ac.ir
2
Professor, Department of Geology, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Gholam Reza
Lashkaripour
lashkaripour@um.ac.ir
3
Professor, Department of Geology, Faculty of Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Abdolrahim
Javaherian
javaherian@aut.ac.ir
4
Professor, Department of Petroleum Engineering, Amirkabir University of Technology, Tehran. Iran
AUTHOR
Marzieh
Mirzakhanian
mirzakhanian@ut.ac.ir
5
Senior Reservoir Geophysicist, Exploration Directorate, National Iranian Oil Company (NIOC), Tehran
AUTHOR
شریفی، ج. و سکوتی دیارجان م. ر.، 1394، مطالعه موردی انجام آزمایشهای آلتراسونیک در فشار مخزن و ارایه یک مدل فیزیک سنگ، سیوچهارمین گردهمایی علوم زمین، سازمان زمین شناسی و اکتشافات معدنی کشور، 3 الی 5 اسفند، تهران، ایران.
1
شریفی، ج.، میرزاخانیان، م.، صابری، م.ر. و سکوتی دیارجان، م.ر.، 1395، مدلسازی جانشینی سیال در ارتباط با پاسخ AVO در یک مخزن کربناته، هفدهمین کنفرانس ژئوفیزیک ایران، تهران، انجمن ژئوفیزیک ایران.
2
مدیریت اکتشاف شرکت ملی نفت ایران، 1393، گزارش تکمیلی چاه B-01، تهران، ایران.
3
Adam, L., Batzle, M. and Brevik, I., 2006, Gassmann's Fluid Substitution and Shear Modulus Variability in Carbonates at Laboratory Seismic and Ultrasonic Frequencies, Geophysics, 71(6). F173–F183.
4
Assefa, S., McCann, C. and Sothcott J., 2003, Velocities of Compressional and shear waves in limestones. Geophysical Prospecting, 51,1-13.
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Avseth, P., Mukerji, T. and Mavko, G., 2005, Quantitative seismic interpretation: Cambridge University Press.
6
Batzle, M. and Wang, Z., 1992, Seismic properties of fluids: Geophysics, 57, 1396-1408.
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9
Dou, Q., Sun Y. and Sullivan, C., 2011, Rock-physics-based carbonate pore type characterization and reservoir permeability heterogeneity evaluation, Upper San Andres reservoir, Permian Basin, west Texas, Journal of Applied Geophysics 74, 8–18
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Feng, Q., Jiang, L., Liub, M., Wand, H., Chene, L. and Xiaod, W., 2014, Fluid substitution in carbonate rocks based on the Gassmann equation and Eshelby–Walsh theory, Journal of Applied Geophysics Volume 106, 60–66.
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20
Motiei, H., 1993, Stratigraphy of Zagros. Geological Survey of Iran. Tehran.
21
Paula, O. B., Pervukhina, M. and Gurevich, B., 2010, Testing Gassmann fluid substitution in carbonates: sonic log versus ultrasonic core measurements. SEG Expanded Abstracts.
22
Russell, B. R., Hedlin, K., Hilterman, F. J. and Lines, L. R., 2003, Fluid-property discrimination with AVO: A Biot- Gassmann perspective, Geophysics, 68, 29-39.
23
Saberi, M. R., 2014, A Rock Physics Model for Unconventional Reservoirs Characterization: Wolfcamp Shale Example, EAGE/FESM Joint Regional Conference Petrophysics Meets Geoscience 17-18 February Kuala Lumpur, Malaysia.
24
Sayers C. M., 2008, The elastic properties of carbonates. The Leading Edge, 27, 1020–1024.
25
Sharifi, J., Mirzakhanian, M., Javaherian, A., Saberi, M. R. and Hafezi Moqadas, N, 2017, An investigation on the relationship between static and dynamic bulk modulus on an Iranian oilfield, 79th EAGE Conference & Exhibition, Paris, France.
26
Sharifi, J., Mirzakhanian, M., Saberi, M. R., Moradi, M. and Sharifi, M., 2018, Quantification of Pore Type System in Carbonate Rocks for RockPhysics Modelling, 80th EAGE Conference & Exhibition, Copenhagen, Denmark.
27
Sodagar, M. and Lawton, D. C. T., 2011, Seismic modeling of CO2 fluid substitution for the Heartland Area Red water CO2 Storage Project (HARP), Alberta, Canada, Energy Procedia, 4, 3338–3345
28
Songa, Y., Hua, H. and Rudnickib, J. W., 2016, Shear properties of heterogeneous fluid-filled porous media with spherical inclusions, International Journal of Solids and Structures, 83,154–168.
29
Verwer, K., Braaksma, H. and Kenter A. M. J., 2008, Case History Acoustic properties of carbonates: Effects of rock texture and implications for fluid substitution, Geophysics, 73, no. 2. B51–B65.
30
White, R. E., Simm, R. and Xu, S., 1998, Well tie, fluid substitution and AVO modelling: A North Sea example. Geophys. Prosp., 46, 323-346.
31
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36
Zhao, L., Nasser, M. and Han, D., 2013, Quantitative geophysical pore-type characterization and its geological implication in carbonate reservoirs, Geophysical Prospecting, 61, 827–841.
37
ORIGINAL_ARTICLE
Determination and depth estimation of lineaments in Northwest of Iranshahr city using airborne magnetic and electromagnetic data
Airborne magnetic and electromagnetic methods are among the most efficient geophysical techniques for the detection of buried anomalies. There are several methods that can be used to estimate the depths of the buried anomalies. In general, modeling methods can be used not only to estimate the depths of the buried anomalies, but also, to determine physical and other geometric factors of the anomalies such as lateral extension, thickness, dip and so on. In this research, magnetic lineaments have been determined using the airborne magnetic data, acquired in a part of Bazman area with an area of 24 square kilometers located in about 125 kilometers northwest of the city of Iranshahr. By applying filters such as reduction to the pole, first horizontal derivatives, analytical signal, tilt angle and upward continuation filters. For processing and interpretation of the airborne magnetic data, the Oasis Montaj module of Geosoft software package has been used. The processing, display and interpretation of the airborne electromagnetic data have been made Conductivity Depth Imaging (CDI) using EM Flow and Profile Analyst software packages of Encom Company. Furthermore, the depths of the lineaments in this area have been estimated using Euler deconvolution method. Then, the obtained results have been compared with the results of airborne electromagnetic investigations for the frequencies of 900, 7200 and 56000 Hz using horizontal and vertical coplanar coils. Also, the obtained findings from the airborne magnetic and electromagnetic methods have been validated by the geological information of the area. The airborne magnetic and electromagnetic data of the area have been acquired using airborne magnetometer and DIGHEM5 electromagnetic instruments, respectively. The airborne magnetic and electromagnetic surveys over the study area have been made by Geological Survey of Iran (GSI) in 2005. As a result of this study, 22 magnetic lineaments in the area have been identified in which 4 lineaments coincide on the main faults of the area as the validation results indicate. In this regard, the main faults can be observed on the obtained magnetic maps in which different filters have been applied, however, the tilt angle magnetic map indicates the main faults of the area more clearly. This implies the better performance of the tilt angle filter over the other filters in displaying magnetic lineaments. Totally, 22 magnetic lineaments have been determined on the magnetic maps. By the results of this study, we can conclude that the main faults of the area have an approximate trend of northeast-southwest. Some of these faults, which have been determined from the airborne magnetic investigations of the area, cannot be determined from geological studies of the area as they have been overlain by the Quaternary sediments. The different performances of these main faults on the lithological variations and tectonic activities of the area have been clearly evident by the result of this study. The main faults of the area have also played a vital role on the formation of folds and fractures, and occurrence of weak earthquakes. The approximate depths of the lineaments, which have been estimated by applying the Euler deconvolution method on the acquired magnetic data are around 100-200 meters.
https://jesphys.ut.ac.ir/article_67775_74c2cff7824b35827365a1a0f53204f1.pdf
2019-03-21
31
46
10.22059/jesphys.2018.259304.1007013
Euler deconvolution
Airborne magnetic
Airborne electromagnetic
Reduction to the pole
First horizontal derivatives
Analytic signal
Mohadeseh
Abdollahi
mohadese.abdollahi@yahoo.com
1
M.Sc. Student, Department of Geophysics, School of Mining, Petroleum & Geophysics Engineering, Shahrood University Technology, Shahrood, Iran
AUTHOR
Ali Reza
Arab Amiri
alirezaarabamiri@yahoo.com
2
Associate Professor, Department of Geophysics, School of Mining, Petroleum & Geophysics Engineering, Shahrood University Technology, Shahrood, Iran
LEAD_AUTHOR
Abolghasim
Kamkar Rouhani
kamkarr@yahoo.com
3
Associate Professor, Department of Geophysics, School of Mining, Petroleum & Geophysics Engineering, Shahrood University Technology, Shahrood, Iran
AUTHOR
Ali
Nejati Kalateh
nejati@shahroodut.ac.ir
4
Associate Professor, Department of Geophysics, School of Mining, Petroleum & Geophysics Engineering, Shahrood University Technology, Shahrood, Iran
AUTHOR
Mohammad Reza
Akhavan Aghdam
r.akhavan51@gmail.com
5
Senior Expert, Geological Survey & Mineral Exploration of Iran, Tehran, Iran
AUTHOR
آقانباتی، س. ع.، زمینشناسی ایران،1383، چاپ سوم، سازمان زمینشناسی و اکتشافات معدنی کشور.
1
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Ma, G., 2013, Edge detection of potential field data using improved local phase filter, Exploration Geophysics, 44(1), 36-41.
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Miller, H. G. and Singh, V., 1994, Potential field tilt -A new concept for location of potential field sources: J Applied Geophysics, 32, 213-217.
26
Nabighian, M. N., 1972, The analytic signal of two-dimensional magnetic bodies with polygonal cross-section: Its properties and use for automated anomaly interpretation: J Geophysics, 37, 507-517.
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Roest, W. R., Verhoef, J. and Pilkington, M., 1992, Magnetic interpretation using the 3D analytic signal: J Geophysics, 57, 116-125.
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Verduzco, B., Fairhead, J. D., Green, C. M. and MacKenzie, C., 2004, New insights into Magnetic derivatives for structural mapping: J The Leading Edge 23, 116-119.
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Telford, W. M., Geldart, L. P. and Sheriff, R. E., 1990, Applied geophysics “2ndEd: Cambridge University Press”.770pp.
30
Thompson, D. T., 1982, A new technique for making computer-assisted depth estimates from magnetic data: J Geophysics, 47, 31-37.
31
ORIGINAL_ARTICLE
Using graph theory in 3D inversion of gravity data to delineate the skeleton of homogeneous subsurface sources
In this paper, three-dimensional (3D) inversion of gravity data using graph theory is used. The methodology was initially introduced by Bijani et al. (2015) and, here, we provide more details for the steps and required parameters of the algorithm. An ensemble of simple point masses are used to model a homogenous subsurface body. Then, in the presented inversion methodology, the model parameters are the Cartesian coordinates of point masses and their total mass. Consequently, the algorithm is able to reconstruct the skeleton of the subsurface body and to yield its total mass. Here, the set of point masses is associated to the vertices of a weighted full graph in which the weights are computed by the Euclidean distances separating vertices in pairs. Then, the Kruskal’s algorithm can be used to solve the Minimum Spanning Tree (MST) problem for the graph. A stabilizer, called equidistance function, is obtained using the MST, which computes the statistical variance of the distances among point masses. The function restricts the spatial distribution of points, and suggests a homogeneous distribution for the point masses in the subsurface. Here, a non-linear global objective function for the model parameters comprising data misfit term and equidistance function with balancing provided by a regularization parameter that should be minimized. A genetic algorithm (GA) is used for the minimization of the objective function. GA consists of a random search algorithm based on the mechanism of natural selection and natural genetics. Then, to solve the optimization problem in our algorithm, there is no need to calculate the derivatives of the objective function with respect to model parameters, or any matrix operation. Simulations for two synthetic examples, including a vertical and a dipping dike, demonstrate the efficiency and effectiveness of the implementation of the present algorithm. The skeleton and total mass of the bodies are estimated very accurately. We also show that although the search limits for the model parameters must be used, they are not very limitative. Even with less realistic bounds, acceptable approximations of the body are still obtained. Unlike Bijani et al. (2015) which used the L-curve method for estimating the regularization parameter, here, we present a new strategy to approximate the parameter. We demonstrate that if: 1. the equidistance function converges almost monotonically to zero with increasing numbers of generation; 2. minimum of the objective function at the final iteration becomes small; and 3. the predicted data by the reconstructed model is approximately close to observed data, then, the selected regularization parameter is nearly optimum and the results are reliable. This provides a suitable and inexpensive methodology for estimating the regularization parameter. The method is tested on gravity data from the Mobrun ore body, north east of Noranda, Quebec, Canada. The anomaly is associated with a massive body of base metal sulfide, mainly pyrite, which has displaced volcanic rocks of middle Precambrian age (Grant and West, 1965). With application of the algorithm, a skeleton of the body is obtained which extends about 350 m in the east direction, and shows a maximum extension of 200 m in depth.
https://jesphys.ut.ac.ir/article_69149_375ef799cf90f66cd7fda76e7b7d7e45.pdf
2019-03-21
47
62
10.22059/jesphys.2019.260544.1007018
Gravimetry
inversion
Graph
Minimum Spanning tree
Genetic Algorithm
Mobrun
Soosan
Soodmand niri
susan.soodmand@ut.ac.ir
1
M.Sc. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Vahid
Ebrahimzadeh Ardestani
ebrahimz@ut.ac.ir
2
Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Saeed
Vatankhah
svatan@ut.ac.ir
3
Assistant Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
آقاجانی، ح.، مرادزاده، ع. و زنگ، ه.، 1389، برآورد موقعیت افقی و ژرفای بیهنجاریهای گرانی به کمک گرادیان کل بهنجارشده، علوم زمین، 76، 169-176.
1
Bijani, R., Ponte-Neto, C. F., Carlos, D. U. and Silva Dias, F. J. S., 2015, Three-dimensional gravity inversion using graph theory to delineat the skeleton of homogeneous sources, Geophysics., 80, G53-G66.
2
Blakely, R. J., 1995, Potential Theory in Gravity and Magnetic Applications, Cambridge University Press, Cambridge.
3
Boschetti, F., Dentith, M. and List, R., 1995, A staged genetic algorithm for tomographic inversion of seismic refraction data, Exploration Geophysics, 26, 331-335.
4
Boschetti, F., Dentith, M. and List, R., 1997, Inversion of potential field data by genetic algorithms, Geophysical Prospecting, 45, 461-478.
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Bott, M., 1960, The use of rapid digital computing methods for direct gravity interpretation of sedimentary basins, Geophys. J. Int., 3, 63–67.
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Boulanger, O. and Chouteau M., 2001, Constraint in 3D gravity inversion, Geophysical Prospecting, 49, 265–280.
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Chakravarthi, V. and Sundararajan, N., 2007, 3D gravity inversion of basement relief a depth-dependent density approach, Geophysics, 72, I23-I32.
8
Deo, N., 1974, Graph theory with applications to engineering and computer science: PHI Learning Pvt. Ltd.
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Goldberg, D. E. and Holland, J. H., 1988, Genetic algorithms and machine learning, Machine Learning, 3, 95–99.
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Grant, F. S. and West, G. F., 1965, Interpretation Theory in Applied Geophysics, McGraw-Hill.
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Kruskal, J. B., Jr., 1956, On the shortest spanning subtree of a graph and the traveling salesman problem, Proceedings of the American Mathematical Society, 7, 48–50.
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Last, B. J. and Kubik, K., 1983, Compact gravity inversion, Geophysics, 48, 713–721.
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Li, Y. and Oldenburg, D. W., 1998, 3D inversion of gravity data, Geophysics, 63, 109–119.
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Martins, C. M., Lima, W. A., Barbosa, V. C. and Silva, J. B., 2011, Total variation regularization for depth-to-basement estimate: Part 1 — Mathematical details and applications, Geophysics, 76, I1–I12.
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Montana, D. J., 1994, Strongly typed genetic programming, Evolutionary Computation, 3, 199–230.
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Vatankhah, S., Ardestani, V. E. and Renaut R. A., 2015, Application of the principle and unbiased predictive risk estimator for determining the regularization parameter in 3-D focusing gravity inversion, Geophys. J. Int., 200, 265-277.
18
Vatankhah, S., Renaut, R. A. and Ardestani, V. E., 2017, 3-D Projected L1 inversion of gravity data using truncated unbiased predictive risk estimator for regularization parameter estimation, Geophys. J. Int., 210, 1872-1887.
19
Zeyen, H. and Pous, J., 1993, 3-D joint inversion of magnetic and gravimetric data with a priori information, Geophys. J. Int., 112, 244–256.
20
ORIGINAL_ARTICLE
The effects of oscillatory behavior of the mother wavelet in the discrete wavelet transform in order to suppress seismic random noise
Seismic data have a variable characteristic. Overlooking this important characteristic will reduce the effectiveness of any signal processing tool. Wavelet transform is a useful tool in seismic data processing and in recent years it has been the subject of attention of geophysicists. In this study we investigate the role of the resolution of the wavelet transform and the Q-factor (Q-factor in band-pass filters is the ratio of central frequency to the bandwidth) of the mother-wavelet on the filter performance with the goal of reducing the random noise and examining the effects of the mother wavelet Q-factor and its oscillatory behavior on the filter performance. We use Rational-Dilation Wavelet Transform (RADWT) and Dual-tree RADWT. These methods have the capability to achieve variable frequency resolution that can also provide a variety of Q-factors. To evaluate the effect of Q-factor of mother wavelet on filter function, the DT-RADWT with different Q-factors is applied on a Ricker Wavelet and synthetic shot gathers and the results are discussed in the manuscript. In the following, we investigate the relationship between seismic signal Q-factor and suitable Q-factor for seismic data processing. The method is applied to high-frequency shallow Sub-Bottom Profiler data and land data. In this study, a new wavelet transform called Rational Dilation Wavelet Transform (RADWT) and its Dual Tree analytical version DT-RADWT is used to attenuate random noise in seismic data. These transforms can achieve a limited range of Q-factor by selecting appropriate parameters p, q and s. The advantage of this transform over the common discrete wavelet transforms is that its rational sampling which provides higher time-frequency resolution. We also investigate the effect of Q-factor of mother wavelet on the performance of wavelet transform filters, and the relation between seismic signal Q-factor and Wavelet transform filter Q-factor. Increasing the Q-factor can reduce the bandwidth of wavelet in each scale. We test the effect of random noise on Q-factor of Ricker wavelet, with different noise levels. The results showed that by changing the level of random noise, the range of Q-factor remains constant. Next, we added the constant noise to Ricker wavelet, and we analyzed the noise-infected wavelet by RADWT and DT-RADWT with different Q-factors, here the soft threshold was used. The result of denoising is presented in Table 2. In last part of manuscript high Q-factor Dual Tree Rational wavelet transform was used to attenuate random noise from synthetic shot gather and marine and land seismic data (figures 9 & 11& 14& 15). Suitable parameters for random noise attenuation, p, q, and s was selected respectively 7, 8, 1 that made WT Q-factor 7.48. This research investigated the role of Q-factor value in suppressing random noise from reflection seismic data. Many Q-factors were tested to evaluate the effect of wavelet transform Q-factor on random noise denoising, and it was observed that with an increase in the Q-factor of the wavelet transform, the signal-to-ratio of filtered trace was improved. The data Q-factor was also calculated, but there was no significant correlation between the appropriate Q-factor of WT for noise reduction and the signal Q-factor. DT-RADWT was better than RADWT in distinguish was the random noise from the signal, due to the use of two parallel filter banks. DT-RADWT with high Q-factor was applied to synthetic data with a variable level of random noise and results are summarized in table4. In addition, the method was also applied to real shallow marine data from sub-bottom profiler with a wide frequency content. Results confirm the effectiveness of WT filter which is increased with the increase of wavelet transform Q-factor.
https://jesphys.ut.ac.ir/article_69152_6aa620baed70b9c3f08c23f5c8d78745.pdf
2019-03-21
63
79
10.22059/jesphys.2019.263998.1007031
Random Noise
Discrete Wavelet Transform
Time-Frequency Domain
Wavelet Q-factor
Offshore Data
Rational Dilation
Dual-Tree Wavelet Transform
Mohammad
Irani Mehr
iranimehr@ut.ac.ir
1
Ph.D. Student, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Mohammad Ali
Riahi
mariahi@ut.ac.ir
2
Professor, Department of Earth Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Ali Reza
Goudarzi
a.goudarzi@kgut.ac.ir
3
Assistant Professor, Department of Earth Sciences, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
AUTHOR
ایرانیمهر، م. و ریاحی، م. ع.، 1393، تضعیف نوفه تصادفی با تبدیل موجک گسسته ضریب اتساع گویا، مجله ژئوفیزیک ایران، دوره 8، شماره 3، 25-35.
1
روشندل کاهو، ا. و نجاتی کلاته، ع.، 1389، تضعیف نوفههای اتفاقی در دادههای لرزهای با استفاده از تجزیة مد تجربی، مجله فیزیک زمین و فضا، 9، 1390، صفحه 61-68.
2
شکفته زوارم، م.، روشندل کاهو، ا. و گرایلو، ه.، 1394، تضعیف نوفههای تصادفی در دادههای لرزهای بازتابی با استفاده از فیلتر انتشار ناهمسانگرد غیرخطی تانسوری، نشریه پژوهشهای ژئوفیزیک کاربردی، دوره1شماره2، 105-118.
3
Aiswarya, K. and Jayaraj, V., 2014, Image Denoising Based On Symmetrical Fractional Overcomplete Wavelet Transform, Unique Journal of Engineering and Advanced Sciences, Vol. 02, no. 1, 101-109.
4
Askari, R. and Siahkoohi, H. R., 2008, Ground roll attenuation using the S and x-f-k transforms, Geophysical Prospecting, 56, 105-114.
5
Auscher, P., 1992, Wavelet bases for L2(R) with rational dilation factor, Wavelets and Their Applications. Jones and Barlett,439-451.
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Bagheri, M. and Riahi, M. A., 2016, Seismic data random noise attenuation using DBM filtering, Bollettino di Geofisica Teorica ed Applicata Vol. 57, No. 1, 1-11.
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Barnes, A. E., 1993, Instantaneous spectral bandwidth and dominant frequency with applications to seismic reflection data, Geophysics, Vol. 58, No. 3, P. 419-428.
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Baussard, A., Nicolier, F. and Truchetet. F., 2004, Rational multiresolution analysis and fast wavelet transform: application to wavelet shrinkage denoising, Signal Processing, Vol., 84, No.10,1735–1747.
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Bayram, I. and Selesnick, I., 2009, Frequency-domain Design of Overcomplete Rational-Dilation Wavelet Transforms, IEEE Trans. Signal Process, Vol. 57, No. 8, 2957–2972.
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Bayram, I. and Selesnick, I., 2011, A Dual-Tree Rational-Dilation Complex Wavelet Transform, IEEE Transactions on Signal Processing, Vol. 59, No.12, 6251 – 6256.
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Borhani, M. and Sedghi, V., 2004, 2-D Dual-Tree Wavelet Based Local Adaptive Image Denoising, The 12nd Iranian Conference on Electrical Engineering, P. 12_017
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Canales, L., 1984, Random noise reduction: Presented at the 54th Annual International Meeting, SEG. 525–527.
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Chase, M. K., 1992, Random noise reduction by 3‐D spatial prediction filtering. SEG Technical Program Expanded Abstracts 1992: pp. 1152-1153
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Donoho, D. L. and Johnstone, I. M., 1994, Ideal spatial adaptation via wavelet shrinkage: Biometrika, Vol. 81, P. 425–455.
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Fugal, L. D., 2009, Conceptual Wavelets in Digital Signal Processing, Space & Signals Technologies LLC.
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Goudarzi, A. and Riahi, M. A., 2013, TQWT and WDGA-Innovative methods for ground roll attenuation, J. Geophys. Eng., Vol 10, No. 6, P. 065007.
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Irani Mehr, M. and Abedi, M. M., 2017, Random Noise Attenuation Using Variable WQ-factor Wavelet Transform, 79th EAGE Conference and Exhibition, Paris.
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Johnstone, I. M. and Silverman, B. W., 1997, Wavelet threshold estimators for data with correlated noise: J. R. Statist. Soc. B, Vol. 59, P. 319– 51.
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Kingsbury, N., 2002, Complex wavelets for shift invariant analysis and filtering of signals, Applied and Computational Harmonic Analysis, Vol. 10, No.3, P. 234-253.
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Lari, H. and Gholami, A., 2014, Curvelet-TV regularized Bregman iteration for seismic random noise attenuation, Journal of Applied Geophysics, Vol 109, no. 1: 233-241.
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Mallat, S., 2008, A Wavelet Tour of Signal Processing, Academic Press, 3rd edition.
22
Merklin, L. and Levchenko, O., 2005, Seismic Engineering Survey in the Caspian Sea for Oil and Gas Companies, 2nd Workshop “Seabed Acoustics” in Rostock-Warnemünde.
23
Meyer, Y., 1992, Wavelets and Operators, Cambridge: Cambridge University Press.
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Sheriff, R. E. and Geldart, L. P., 1995, Exploration seismology, 2nd ed, Cambridge university press.
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Selesnick, I., 2001, Hilbert transform pairs of wavelet bases, IEEE Signal Processing Letters, Volume 8, No.6, P.170-173.
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Selesnick I., 2004, The Double-Density Dual-Tree DWT, IEEE Transactions on Signal Processing, Volume: 52, Issue: 5. P. 1304-1314.
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Yilmaz, Ö., 2001, Seismic Data Analysis, Society of Exploration Geophysicists, second edition.
28
ORIGINAL_ARTICLE
Calculation of period of δ Scuti stars using generalized Lomb-Scargle periodogram technique
Astronomical observations are usually sparse and non-uniform, sometimes contaminated with random or systematic noises. They always packed in certain time periods (nights) separated by several hours or maybe days. Fourier analysis which regularly used to analyze periodicities in time series could not be implemented in astronomical time series because it generates fake signals in power spectrum. Lomb-Scargle periodogram is a well-known algorithm to detect periodicities in a set of non-uniformly spaced data. This method implements least-squares fitting of sine and cosine waves in form of and search for best fitted frequencies. It is suitable for time series with zero mean. The significance of the detected periods is inferred by comparing power of the signal with a random estimation of false alarm probability (FAP). In this paper, we manipulate the generalized Lomb-Scargle periodogram (GLS) to calculate periods for a typical δ Scuti star. The GLS is an extension to the Lomb-Scargle periodogram which takes into account the measurement of errors and also is more suitable for time series with non-zero average. GLS tries to fit the equation to the time series and find the power spectrum for frequencies. We consider a given periodogram peak, derived from GLS, significant when it exceeds the one present “false alarm probability” level (FAP), which means there is 99% confidence that it is real and could not be simulated by Gaussian noise. FAP levels are calculated by performing random permutations of the data with similar times of observations. δ Scuti variable stars lie in the lower part of the instability strip on the main sequence in Hertzprung-Rassell diagram with luminosity classes between III to V. They are belonging to the disc population and usually are not observed in globular clusters. The variability of this type of stars was discovered in 1963 and was assigned to the group of irregular variables. They are obeying a period-luminosity relation like cepheids and can be used as standard candles. Their pulsating period is less than 1 day. Many of these stars show multiple periods with amplitudes less than 0.1 mag. These stars pulsate in radial and non-radial modes and are important as their pulsations can be used as tracer of their internal structures. We implement the generalized Lomb-Scargle periodogram to detect period for high amplitude variable star BS Aqr (HD223338) which is a δ Scuti of A8 III spectral type with very short period (0.01-0.2 days) and low amplitudes (less than 0.9 mag) and almost sinusoidal light curves. Different interpretations are given in the literature about the nature of variability for this star. Its period is continuously decreasing. Most authors have agreed with the monoperiodic nature of this star. Using photometric data taken in La Silla Observatory that covered 30 September to 6 November 1983, we find that BS Aqr is an monoperiodic δ Scuti and detect a period of 0.1977 days for the star pulsation. The result is in good agreement with pervious results from this star which demonstrates the capability of the Generalized Lomb-Scargle method to study brightness variation in variable stars.
https://jesphys.ut.ac.ir/article_69141_df7bfa7cf4c60b1a0f0194b1b8c5a178.pdf
2019-03-21
81
88
10.22059/jesphys.2019.249957.1006967
Variable star
Period
Periodogram technique
δ Scuti
Fatemeh
Azizi
f.azizi@pnu.ac.ir
1
Assistant Professor, Department of Physics, Payame Noor University, Tehran, Iran
LEAD_AUTHOR
Mohammad Taghi
Mirtorabi
torabi@alzahra.ac.ir
2
Associate Professor, Department of Physics, Alzahra University, Tehran, Iran
AUTHOR
Azizi, F. and Mirtorabi, M. T., 2018, A survey of TiOλ567 nm absorption in solar-type, MNRAS, 475, 2253–2268.
1
Bidaran, B., Mirtorabi, M. T. and Azizi, F., 2016, A new titanium oxide index in the visual band, MNRAS, 2043-2047.
2
Breger, M., 2000, Delta Scuti Star (Review), ASP Conference Series, 210, 3.
3
Burning, F. J. M., 1963, Bull. Astr. Inst. Netherlands, 17, 22.
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Cumming, A., Marcy, G. W. and Butler, R. P., 1999, The Lick Planet Search: Detectability and Mass Thresholds, ApJ, 526, 890.
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Elst, E. W., 1986, On the light curve of BS Aquarii, Acta Astron. 36, 405.
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Jian-Ning, Fu., Shi-Yang, Jiang., Sheng-Hong, Gu., Yu-Lei, Qiu., 1997, Has the delta Scuti star BS Aqr a companion?, IBVS, No. 4518.
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Gilliland, R. L. and Baliunas, S. L., 1987, Objective characterization of stellar activity cycles. I - Methods and solar cycle analyses, ApJ, 314, 766.
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Rodriguez, E. and Breger, M., 2001, delta Scuti and related stars: Analysis of the R00 Catalogue, A&A, 366,178.
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Yang, D., Jiang, S., Tang, Q. and Wang, H., 1993, The Rates of Period Change in BS Aqr and DY Her, Inf. Bull. Var. Stars, No. 383.
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22
ORIGINAL_ARTICLE
Comparison of four Sensitivity Analysis Methods of HBV Conceptual Model Parameters in Karkheh Basin and its Sub-basins
The HBV (Hydrologiska Byråns Vattenbalansavedlning) is a conceptual model widely used for hydrological forecasting and water resource studies. In this study, sensitivity analysis of parameters of the HBV model is investigated for Karkhe basin and its sub-basins for four different periods 1, 5, 10 and 25 years with four methods including FAST (Fourier Amplitude Sensitivity Test), RSA (Regional Sensitivity Analysis), Sobol and regression. After determining the most sensitive parameters, the model is calibrated using Nondominated Sorting Genetic Algorithm (NSGA) method. In all statistical periods, one year has been used for warm-up to eliminate the effects of initial conditions. In this study, the MOUSE Toolbox is used to analyze the sensitivity of the HBV model parameters. This software is based on Java programming language. To analyze the sensitivity of the HBV model parameters based on the Monte Carlo sampling method and the Halton sequence method for each of the samples (time periods) in each sub-basin separately, 1000 samples are taken for the set of input parameters with a specified range for each parameter taken. Objective functions for evaluating performance of model are NSE, RMSE, RSR and BIAS. The results of sensitivity analysis of the parameters show that Sobol and RSA are more reliable methods because of variability in time intervals and different sub-basins. Fast and regression methods in the Karkheh basin and its sub-basins for different time periods show similar results that considering the change in hydroclimate conditions in this basin, isn't practical and the results of these methods can not be used for investigating sensitivity of parameters and their identification in the studied basin. The most sensitive parameters of HBV model for Karkheh basin and its sub-basins in soil routine is maximum soil moisture content (Fcap) and in the response routine is the storage of soil surface moisture content (hl1). These parameters have shown the most sensitive factor in minimum fluxes. The snow routine parameters, especially the threshold temperature for ice freezing (ttlim), are sensitive in the sub-basins of Ghare Sou and Kashkan in short periods (1 and 5 years). For a specific sub-basin, the sensitivity of the parameters in different time periods is not completely stable and a little variability has been observed in different periods. But the most sensitive parameters (hl1 and fcap) have maintained their sustainability almost in all periods. Parameters of response and soil routines are more sensitive to the parameters of snow and routing routines. The results of the interaction between the parameters using the Sobol method in different sub-basins indicate that the strongest interactions are between the soil routine parameters, especially Fcap, with the response routine parameters and also the response routine parameters with each other. The time variability of parameters indicates that the soil routine and response parameters in the minimum discharge show the most sensitivity. Other parameters are more sensitive in the dry season of the basin (summer and autumn). The HBV model has the ability to simulate runoff in the Karkhe basin and its sub-basins with high precision. This study shows that selection of shorter period of calibration gives better simulation results. For one year's period the best NSE, RSR and RMSE are in Gamasyab sub-basin respectively 0.95, 0.21 and 1.4 and the best BIAS is in Kashkan sub-basin and Karkhe basin with 0.13.
https://jesphys.ut.ac.ir/article_69145_7e6a3643ad7b73a6486ee948dd29db8a.pdf
2019-03-21
89
105
10.22059/jesphys.2019.253304.1006979
HBV conceptual model
Sensitivity analysis
calibration
Karkhe basin
Maryam
Shafiei
shafiei.m@ut.ac.ir
1
Ph.D. Graduated, Department of Irrigation and Reclamation Engineering, Natural Resources and Agricultural Campus, University of Tehran, Karaj, Iran
AUTHOR
Javad
Bazrafshan
jbazr@ut.ac.ir
2
Associate Professor, Department of Irrigation and Reclamation Engineering, Natural Resources and Agricultural Campus, University of Tehran, Karaj, Iran
LEAD_AUTHOR
Parviz
Irannejad
piran@ut.ac.ir
3
Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
یعقوبی، م. و مساح بوانی، ع.، 1393، تحلیل حساسیت و مقایسه عملکرد سه مدل مفهومی HBV، IHARCES و HEC-HMS در شبیهسازی بارش-رواناب پیوسته در حوضههای نیمهخشک (بررسی موردی: حوضه اعظم هرات-یزد)، مجله فیزیک زمین و فضا، 40 (2)، 172-153.
1
Abebe, N. A., Ogden, F. L. and Pradhan, N. R., 2010, Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: Implications for parameter estimation, Journal of Hydrology, 389(3), 301-310.
2
Akhtar, M., Ahmad, N. and Booij, M., 2008, The impact of climate change on the water resources of Hindukush–Karakorum–Himalaya region under different glacier coverage scenarios, Journal of hydrology, 355(1), 148-163.
3
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12
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41
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45
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46
ORIGINAL_ARTICLE
Using cubic Hermite polynomials in constructing monotone semi-Lagrangian methods for advection equation
Semi-Lagrangian methods have been widely applied in general circulation models of the atmosphere as they do not suffer from a Courant–Fredericks–Levy (CFL) constraint for computational stability. Ease of application, high accuracy and speed of execution in general circulation models are other reasons for the popularity of semi-Lagrangian methods. Two fundamenta lissues in semi-Lagrangian methods are related to the trajectory computation and interpolation from the regular grid to departure points. If sufficiently accurate schemes are used to solve for trajectories with interpolations, one can expect good performance from the semi-Lagrangian scheme in solving the equations of motion of the atmosphere. Two general methods of solving the trajectory equation are the forward and backward methods. Most semi-Lagrangian methods use backward-trajectory schemes for estimating positions of the air parcels that arrive at the grid points in the future time step. Solving the trajectory equation is carried out by iteration. In the research reported, two iterations are used for trajectory computation. The fundamental difference between the forward and backward trajectory scheme rests in the calculation of advective quantity at the departure and destination points. While in the backward solution procedure, it is necessary to make interpolation from the regular grid to departure points; in the forward scheme, it is necessary to make interpolation from the irregular grid of destination points to the regular grid. The usually used interpolation methods in the semi-Lagrangian method include piecewise cubic Lagrange and Hermite, cascades, and monotone Hermite. Increasing the degree of polynomial interpolation leads to a higher degree of formal accuracy, but it leads to the generation of unwanted oscillation in regions with severe gradients of the transported quantities. Eliminating the unwanted oscillations is done through a variety of methods which generally increase the computational cost and reduce the accuracy of the scheme. To address the issue, in this research, a new selective monotone semi-Lagrangian method is developed and tested along with two standard methods based on the Lagrange and Hermite interpolations. The Lagrange polynomials have been considered by researchers for the high speed of computation in operational models. The fictitious oscillations produced at the edges of sharp gradients of the advected quantities are the main shortcoming of this method. The fictitious oscillations cannot be eliminated by increasing the degree of interpolation polynomials, which can only lead to a reduction in the wavelength of the oscillations. The results presented on increasing the degree of interpolation polynomials clearly show that the removal of the fictitious oscillations requires the use of monotone polynomials for interpolation. It is important to note that the Hermite interpolation polynomials are not inherently monotone. To make them monotone, one needs to manipulate the derivatives at the grid points appropriately. This process, however, may lead to a substantial deteriration of accuracy. For this reason, in this paper, a selective interpolation method is desined to obtain the best accuracy in solution of the advection equation, while preserving monotnonicity and removing the issue with the fictitious oscillations. In the selective method, first the interpolation is done by the non-monotonic cubic Hermite and then a properly designed slope function is calculated at each grid interval. If the slope function takes values outside the range, it indicates that a fictitious oscillation has occurred in the interpolantion. To remove the oscillation, the non-monotone interpolation is abandoned and the monotone interpolation is performed by limiting the derivative to the monotone region. This technique can minimize the error caused by the changes in the derivatives. Results are shown to demonstrate the working and superiority of the seclective montone scheme.
https://jesphys.ut.ac.ir/article_69144_e8fbce9675ca2a1000628bdfdc804bf0.pdf
2019-03-21
107
127
10.22059/jesphys.2019.253532.1006982
Advection
Semi-Lagrangian
Monotone
Hermite polynomial
Mass
Humidity
Ali
Mohammadi
mohammadi.a@ut.ac.ir
1
Ph.D. Student, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Ali Reza
Mohebalhojeh
amoheb@ut.ac.ir
2
Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Majid
Mazraeh Farahani
mazraeh@ut.ac.ir
3
Associate Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
آزادی، م.، 1373، مدلسازی معادلات هواشناختی بهروشهای نیمهلاگرانژی، کاربست به معادله تاوایی فشارورد. پایاننامه کارشناسی ارشد هواشناسی، موسسه ژئوفیزیک دانشگاه تهران.
1
اصفهانیان، و. و اشرفی، خ.، 1382، اعمال روش نیمه لاگرانژی–نیمه ضمنی برای حل معادلات آب کمعمق. نشریه دانشکده فنی، 37، (3).
2
محبالحجه، ع. ر. و مشایخی، ر.، 1383، نمایش شارشهای تاواری و امواج گرانی در الگوریتمهای حل عددی معادلات بسیط فشارورد منطقهای، مجله فیزیک زمین و فضا، 30 (1)، 37-47.
3
محمدی، ع.، محبالحجه، ع. ر. و مزرعه فراهانی، م.، 1397، چندجملهای درونیاب هرمیت درجه سوم یکنوا و کاربرد آن در تبدیل مختصات برای مدلهای پیشبینی عددی وضع هوا، مجله انجمن ژئوفیزیک ایران، 12 (3)، 21-38.
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50
ORIGINAL_ARTICLE
Evaluation of estimator variables in air temperature estimation in January and June based on land cover
The near-surface temperature, Ts measured by ground stations provides limited information on the spatial distribution of Ta pattern. A correct estimation of Ta distribution pattern is necessary for a wide range of applications such as hydrology, ecology, meteorology (Wenbin et al., 2013) and biology of vector-borne diseases. In this study, near-surface air temperatures (Ta) using environmental parameters including land surface temperature (LST), altitude, slope, vegetation, latitude, albedo, and mean sea level pressure (MSLP), were estimated for January and July in the period 2001-2015 for Iran. In this study, due to the use of different data sources with different spatial resolutions, all maps were converted to the same spatial resolution of Era-Interim (0.125˚). Then spatial distributions of Ta and LST were determined. The spatial distribution patterns of these two components were also determined by applying the Moran spatial autocorrelation index. Finally, according to the land cover, multivariate regression models are presented for estimating Ta based on seven parameters, including LST, altitude, slope, vegetation and latitude, albedo and MSLP. In the following, the characteristics of each of these data are also described. Standardized regression coefficients were used to determine the most important estimator in each land cover. The correlation between the parameters involved in the study with the absolute difference between the air and surface temperature are negative in January, which means that by increasing, slope, altitude, NDVI, latitude, albedo and MSLP, the difference is reduced and vice versa. Nonetheless, this kind of relationship is not valid in the whole study area, and there are some exceptions. In July the relationship between this difference and slope and NDVI is positive, which means that with increasing altitude, latitude, albedo and MSLP, the differences also increase. In January, waters (99%), urban areas (95%), and barren or sparsely vegetated (92%) have the highest R2. While, mixed forests had the lowest R2 equal to 27% (Figure 4). The least errors are related to urban areas (0.69 ° C), water (0.75 ° C), and then forest areas (0.9 ° C). The highest errors were observed in cropland and open shrubland equal to 1.35˚C and 1.34˚C. The highest R2 was calculated for water (95%), urban areas (94%), mixed forest and open shrubland (93%). The least error occurred in mixed forest (0.3˚C). The main objective of the present study was to develop a model of air temperature estimation from surface temperature and other auxiliary variables (elevation, slope, vegetation, latitude, land cover, albedo and mean sea level pressure). Regression models were presented for estimating Ta in monthly scale. The results can be summarized as: Between the air and surface temperature, the most variability is related to the Ta which in the region of Iran has an annual variation coefficient of 92% in January and 41% in July. In January, slope and altitude are the most important variables in the estimation model so that up to 16% and 12% can explain LST-Ta differences, respectively, while latitude and MSLP are the most important variables in July so that each one of them explains up to 9.6% of these differences in July. The role of land cover in estimating Ta is very important. In addition, the number of pixels located on each land cover category can also play a decisive role in estimation model. Category of water, urban and barren area in January, exhibited the highest R2 of 99%, 95% and 92%, respectively. The lowest R2 (approximately 27%) is related to grassland and mixed forest. In July, the highest R2 is related to water and urban areas about 95 and 94%. R2 of grassland increases by approaching summer. The lowest error is recorded for urban area, water and mixed forest in January while the lowest error is related to mixed forest, open shrubland and barren areas in July. The accuracy of estimation models varies according to the months and the land cover. Based on standardized regression coefficients, in January altitude (in barren, urban and cropland area) mean sea level pressure (in grassland and shrubland), slope of mixed forest area and latitude in water area were of great importance in air temperature estimation. While, in June, due to presence of low pressure unter in Iran, the role of local climatic factors has been minimized and mean sea level pressures was the most important estimator almost in all landcovers.
https://jesphys.ut.ac.ir/article_67777_ccd91184326d16b2d1282593154ef9e3.pdf
2019-03-21
129
147
10.22059/jesphys.2018.253769.1006985
Air temperature estimation
Land surface temperature
Regression
Estimator variables
Chenour
Mohammadi
chenour.mohammadi@modares.ac.ir
1
Ph.D. Student, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
AUTHOR
Manuchehr
Farajzadeh
farajzam@modares.ac.ir
2
Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Yousef
Ghavidel Rahimi
ghavidel@modares.ac.ir
3
Associate Professor, Department of Physical Geography, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
AUTHOR
Abbas Ali
Aliakbari-Bidokhti
bidokhti@ut.ac.ir
4
Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
بابایی فینی، ا.، 1394، بررسی رابطه دمای سطح زمین و شاخص بهنجارشده پوشش گیاهی در محیط شهری (مطالعه موردی: کلانشهراصفهان)، م. علمی-پژوهشی (دانشگاه آزاد) 90، 75-29.
1
واعظ موسوی، س.ع. و مختارزاده، م.، 1394، تخمین دمای هوای سطح زمین با استفاده از داده LST سنجنده MODIS، بیست و دومین همایش ملی ژئوماتیک.
2
پرویز، ل.، خلقی، م. و ولیزاده، خ.، 1389، تخمین دمای هوا با استفاده از روش شاخص پوشش گیاهی-دما (TVX)، مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، سال پانزدهم، شماره پنجاه و ششم، 33-21.
3
Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N. and Santos, A., 2012, Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens. Environ., 124,108–121.
4
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., Van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E.V., Isaksen, L., Kallberg, P., Kohler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., Rosnay, P.de, Tavolato, C., Thepaut, J.-N. and Vitrat, F., 2011, The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc, 137, 553–597, DOI:10.1002/qj.828.
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Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A. and Huang, X., 2010, MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ., 114, 168–182.
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Ghasemi, A., 2015, Changes and trends in maximum, minimum and mean temperature series in Iran. Atmos. Sci. Lett., 16, 366-372.
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Janatian, N., Sadeghi, M., Sanaeinejad, S. H., Bakhshian, E., Farid, A., Hasheminia, S. M. and Ghazanfari, S., 2016, A statistical framework for estimating air temperature using MODIS land surface temperature data. Int. j. climatol., 37, 1181-1194.
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10
Mooney, P. M., Mulligan, F. J. and Fealy, R., 2011, Comparison of ERA-40, ERA-Interim and NCEP/NCAR reanalysis data with observed surface air temperatures over Ireland. Int. j. climatol., 31, 545–557.
11
Moradi, M., Salahi, B. and Masoodian, S. A., 2016, Land surface temperature zoning of Iran with MODIS data. Journal of natural environment hazards, 5, 101-116.
12
Mutiibwa, D., Strachan, S. and Albright, T., 2015, Land Surface Temperature and Surface Air Temperature in Complex Terrain. Journal of selected topics in applied earth observation and remote sensing, 8, 4762 – 4774, doi: 10.1109/JSTARS.2015.2468594.
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Phan, T. N., Kappas, M. and Degener, J., 2017, Different combination of MODIS land surface temperature data for daily air surface temperature estimation in North West Vietnam. Geophysical Research Abstracts, 19, 5213-1.
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Revadekar, J. V., Hameed, S., Collins, D., Manton, M., Shikh, M., Bogaonkar, H. P., Kothawale, D. R., Adnan, M., Ahmed, A. U., Ashraf, J., Baidya, S., Islam, N., Jayasinghearachchi, D., Manzoor, N., Premalal, K.H.M.S. and Shreshta, M. L., 2013, Impact of altitude and latitude on changes in temperature extremes over South Asia during 1971–2000. Int. j. climatol., 33, 199–209.
15
Shah, D. B., Pandya, M. R., Trivedi, H. J. and Jani, A. R., 2013, Estimating minimum and maximum air temperature using MODIS data over Indo-Gangetic Plain. J. Earth Syst. Sci., 122, 1593–1605.
16
Shen, S. and Leptoukh, G. G., 2011, Estimation of surface air temperature over central and eastern Eurasia from MODIS land surface temperature. Environ. Res. Lett., 6, 045206.
17
Shi, Y., Jiang, Z., Dong, L. and Shen, S., 2017, Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta, China. Journal of meteorological research, 31, 448.454.
18
Sun, H., Chen, Y., Gong, A., Zhao, X., Zhan, W. and Wang, M., 2014, Estimating mean air temperature using MODIS day and night land surface temperatures. Theor. Appl. Climatol., 118, 81–92.
19
Vancutsem, C., Ceccato, P., Dinku, T. and Connor, S. J., 2010, Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens. Environ., 114, 449-465.
20
Wan, Z., 1999, MODIS Land-Surface Temperature Algorithm Theoretical Basis Document (LST ATBD),v.3.3
21
Wan, Z. and Dozier, J., 1989, Land-surface temperature measurement from space: physical principles and inverse modeling. IEEE Trans. Geosci. Remote Sens., 27, 3, 268-278.
22
Wenbin, Z., Aifeng, L. and Shaofeng, J., 2013, Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sens. Environ., 130, 62-73.
23
Xu, Y., Knudby, A. and Chak, H. H., 2014, Estimating daily maximum air temperature from MODIS in British Columbia, Canada. Int. J. Remote Sens., 35, 8108-8121.
24
ORIGINAL_ARTICLE
Calibration of Amirabad radar parameters for estimating precipitation in hot weather
Meteorological radar is usually used to estimate rainfall. The relationship between rainfall and the reflectivity of the radar is exponential. Measurement of the intensity and amount of precipitation in the management of water resources, agriculture, and flood alert is widely used. Radar and rain gauges can better estimate the amount and spatial distribution of rainfall. Marshall et al. (1947) proposed based on the relationship between the reflectivity coefficient Z and the precipitation intensity R. Here, a and b are coefficients of the model and may differ in different places and seasons. The factors affecting these variables are: 1- type of rainfall, 2- Season; 3-Geographic and Topographic Surface of the Region. The size of precipitation drops and their distribution varies in different rainfalls. The sources of error in the radar are (1) the difference in radar reflection height, that is related to the height of the ground, while the rain-gauge measures rainfall on the earth's surface. 2) Radar calibration error. 3) Echoes of recurrences from obstacles near the ground. 4) Radar beam attenuation 5) Unrealized echoes of solid phenomena such as hail, snow, melting region. Estimates are more credible near radar. The best way to collect rainbow data is to use both radar and rain gauge simultaneously. Data used in this study include two series of ground station data and radar data. The rain gauge was used between 30 and 100 kilometers from Amirabad radar. The rainfall in July and September 2015 were selected. The severity of the two selected rainfall was appropriate, and their rainfall was remarkable. In this research, radar beam angles were measured at 0.2, 0.3, 0.4, 0.5 and 0.6 degrees as well as radar beam at constant altitudes of 200, 500 and 1000 meters from ground level. At the specified times, the radar reflection value was matched to the amount of precipitation obtained from the rainfalls during the same time interval. In the coordinate system on the vertical axis, the values of log Z (logarithm of reflectivity) were plotted on the horizontal axis and log R (rainfall rainfall intensity logarithm) and correlation between the logarithm of reflection and the logarithm of precipitation were obtained by regression method by which linear equation is extracted where the slope of this line is equal to b and the width of its origin is log a. For all the studied stations and for both selected precipitation and all selected angles, the values of the new radar parameter were obtained separately and the new values of radar precipitation were estimated with the help of new parameters and the relation . Using the obtained coefficients, the intensity and total radar rainfall were estimated. The results were different for each station. Regarding estimated radar rainfall values and station distance from the radar, for each station, the optimal beam angle was chosen to have the best estimate of precipitation. In Gorgan, Sari, and Dash-e-Naz ratio of precipitation estimated by radar to rain gauge measurement is about 90 percent. Meanwhile in Babolsar and Banda-e-Gaz the ratio is only 2 percent. Estimated rainfall was 12 percent higher at Gomishan station. At Amol station, it was 25% less than the rain, measured. Because it was difficult to get radar coefficients for each station as it took a lot of time. So, for the rain event of September 1 and 2, 2015, using the rainfall data of all ground stations and the radar reflection coefficient Z, a general equation was obtained. Comparison of total radar precipitation data before calibration and after calibration, with rainfall values of ground stations, showed that in most stations, the total estimated rainfall data of the radar after calibration, approached the amounts of actual rainfalls. The average rainfall increased from 6.8 mm to 28.5 mm, and just 3 mm lower than the average rain gauges. Estimated rainfall data in two samples of the hot season of the Amir Abad radar showed that the range of radar parameters was high, and their value was very different from the radar default value. The estimated rainfall was much lower than the rainfall before calibration. If a radar is calibrated for each precipitation and location, the estimated radar precipitation value is very close to what is measured by ground stations. The results of this study showed that radar coefficients are different for each rainfall. It is also different for rainfall that occurs in one area at different times, and this depends on the geographic location and distance from the radar. To achieve better results, the number of additional stations and the number of additional rainfalls should be studied.
https://jesphys.ut.ac.ir/article_67785_ca6fa98d9d84e684f87f23c7c40989bd.pdf
2019-03-21
149
163
10.22059/jesphys.2018.252269.1006991
Radar
Rainfall estimation
calibration
Eastern Caspian
Parviz
Panjehkoobi
parvizpanj@gmail.com
1
Ph.D. Student, Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
AUTHOR
Seyed Abolfazl
Masoodian
porcista@yahoo.ie
2
Professor, Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan, Iran
LEAD_AUTHOR
Abdolazim
Ghanghermeh
aghanghermeh@gmail.com
3
Assistant Professor, Department of Geography, Faculty of Humanities, Golestan University, Gorgan, Iran
AUTHOR
اسکولین، م.، 1392، مقدمهای بر سیستم رادار، ترجمه: سهیلیفرد، م. و آقابابایی، م.، انتشارات ادبستان (ویرایش سوم).
1
عبدالهی، ب.، حسینی، م. و ابراهیمی، ک.، 1396، ارزیابی دادههای ماهوارهای CMORPH و
2
TRMM 3B42RT V7 به منظور تخمین بارش در حوضهی گرگانرود، علوم مهندسی و آبخیزداری ایران، 11 (36)، 55-68.
3
محمدیها، ا.، معماریان، م.ح. و ریحانیپروری، م.، 1392، ارزیابی برآوردهای رادار هواشناسی تهران از کمیت بارش بهروش Z-R برای سه رویداد بارش سالهای 2010 و 2011، مجله فیزیک زمین و فضا، 39 (2)، 187-204.
4
Atlas, D. and Ulbrich, C. W., 1977, Path- and area-integrated rainfall measurement by microwave attenuation in the 1-3 cm band. J. Appl. Meteorol., 16, 1322–1331.
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Atlas, D., 1954, The estimation of cloud parameters by radar;Journal of meteorology;309-317
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Battan, L. J., 1973, Radar observation of the atmosphere. The University of Chicago Press, Chicago, 324 pp.
7
Hagen, M. and Yuter, S., 2002, Relations between radar reflectivity, Liquid-water content and rainfall rate durins the MAP SOP; Journal Reserch Meteorological,Vol, 129,477-493
8
Gunn, R. and Kinzer, G. D., 1949, The terminal velocity of fall for water droplets in stagnant air. J. Meteorol., 6, 243–248.
9
Josephine, V. S., Mudgal, B. V. and Thampi, S. B., 2014, Applicability of Doppler weather radar based rainfall data for runoff estimation in Indian watersheds – A case study of Chennai basin, Sadhana, Vol. 39: 989–997
10
Lee, G. W. and Zawadazki, I., 2004,Variability of drop size distribution: Noise and Noise filtering in disdrometric data, Journal of applied meteorology , Vol 44 , 634-652
11
Lee, G. and Zawadzki, I., 2005, Variability of drop size distributions: time-scale dependence of the variability and its effects on rain estimation, Journal of Applied Meteorology 44(2), 241–255.
12
Marshall, J. S. and Palmer, W. M., 1948, The distribution of raindrops with size, Journal. Of Meteorological., 5, 165-166.
13
Marshall, J. S., Langille, R. C. and Palmer, W. M., 1947, Measurment of rainfall by radar;Journal of meteorology ; 186-192.
14
Marshall, J. S., Hitschfeld, W. and Gunn, K. L. S., 1955, Advances in radar weather, Adv. Geophys., 2, 1–56.
15
Michela, C., Alberto, D. F., Francesco, D., Marco, M. and Andrea, M., 2008, A Radar-based climatology of convective activity in the Veneto region , foralps, Technical Report, 4, Trento, Italy, 44 pp
16
Nikahd, A., Hashim, M. and Nazemosadat, M. J., 2016, An improved algorithm in unipolar weather radar calibration for rainfall estimation; Innov. Infrastruct. Solut; 1-11
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Overeem, A., Buishand, T. A. and Holleman, I., 2009, Extreme rainfall analysis and estimation Of depth-duration-frequency curves using weather radar, Weter Resources Reserch, Vol. 45: 1-15
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Pedersen, l., Jensen, N. E. and Madsen, H., 2010, Calibration of Local Area Weather Radar—Identifying significant factors affecting the calibration; Atmospheric Research 97 129–143
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RB5-Manuals-Rainbow Training Manual, 2012.
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Rollenbeck, R. and Bendix, V., 2006, Experimental calibration of a cost-effective X-band weather radar for climate ecological studies in southern Ecuador; Atmospheric Research 79 ; 296– 316.
21
Ryzhkov, A. and Zrnic, D. S., 1995, Precipitation and attenuation measurement at a 10-cm wavelength Journal of applied meteorology , Vol 34;2121-2134
22
Tokay, A., Hartmann, P. and Battaglia, A., 2008, A Field Study of Reflectivity and Z–R Relations Using Vertically Pointing Radars and Disdrometers, Journal of Atmosphric and Oceanic Technology Vol 26 ,1120-1134
23
Ulbrich, C. W., 1983, Natural variations in the analytical form of the raindrop size distribution. J. Clim. Appl. Meteorol., 22,1764–1775.
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Uijlenhoet, R., 2001, Raindrop size distributions and radar reflectivity–rainrate relationships for radar hydrology. Hydrology and Earth System Sciences 5 (4), 615–627.
25
Wang, G., Liu, L. and Ding, Y., 2012, Improvement of Radar Quantitative Precipitation Estimation Based on Real-Time Adjustments to Z-R Relationships and Inverse Distance Weighting Correction Schemes Advancesin Atmospheric, Vol. 29, No. 3, 575-584
26
Zawadzki, I., 1984, Factors affecting the precision of radar measurements of rain. Preprints, 22nd Conf. on Radar Meteorology,Zurich, Switzerland, Amer. Meteor. Soc., 251–256.
27
Zawadzki, I., 1988, Equilibium raindrop size distributions in tropical rain; Journal of atemosoheric scinces, Vol 45 ,No 22,3552-3559.
28
ORIGINAL_ARTICLE
New Approach of Low-Frequency Electromagnetic Wave Generation in the Near-Earth Environment
This work presents the study on the electromagnetic wave penetration into the ionosphere in the frequency range of 10 Hz to 3 kHz and 3 kHz to 30 kHz, corresponding to the Extremely Low Frequency (ELF) and Very Low Frequency (VLF) for telecommunication applications and earthquake prediction. The ELF-VLF waves can also be generated through natural phenomena such as lightning as well as pre-seismic activities. The ELF generation before major earthquakes has been reported in several studies. Therefore, having a complete model capable of simulating the ELF waves generation and propagation in the disturbed ionospheric conditions, associated with pre-earthquake activities can be used to save human lives by predicting the exact location of a major earthquake. This study aims at developing a computational model in order to investigate the ELF –VLF wave generation and propagation in the lower ionosphere that can be used as a precursor for seismic events. Another application of this frequency band is in the radio navigation. The VLF navigation system known as OMEGA was very popular and used for many applications such as navigation of ships, airplanes and also in the land. The system was in use until the late 1990s when it was replaced by Global Positioning Systems (GPS) due to high accuracy and low cost. Very recently, there has been an effort to renew the VLF navigation systems at a low cost. This will require a new approach for VLF wave generation in the ionosphere at a lower cost in comparison with regular transmitters. The efficiency of VLF wave generation in the lower ionosphere using a ground-based dipole antenna in the equatorial region is examined in this study. In this study, we have shown that transmitted signal from the ground into the ionosphere can generate a current in the lower ionosphere, which may expand up to a few kilometers depending on the ionospheric conductivities and the frequency or modulation of the transmitted signal from the ground. This study investigates the generation of secondary currents and the artificial antenna in the ionosphere in order to develop a new technique for generating these signals for navigation applications. The approache for improving the efficiency of this technique including pre-modulation of the ionosphere using high-power high frequency (HF) signal for modifying the conductivities in the ionosphere is discussed. The main idea here is to investigate the efficiency of Whistler wave generation in the E region in different ionospheric conditions. Specifically, the effect of pulse and continuous probing of the lower ionosphere with ELF-VLF signals and the generation of secondary waves and currents due to high conductivities are investigated. We have also proposed the application of this model to study the generation and propagation of ELF-VLF signal associated with the earthquake in the disturbed ionospheric conditions. This includes the variation of background ionospheric plasma and its effect on the penetration of the signal in the ionosphere and length scale of the excitied currents. This study may be critical to determine the exact location of a major earthquake using the pre-seismic activities such as generation and propagation of ELF waves. The variation of background ionospheric parameters such as electron density and ionospheric disturbances due to pre-earthquake conditions on the excitation and penetration of ELF-VLF waves into the E-region will be investigated in future studies.
https://jesphys.ut.ac.ir/article_69146_36965323679273b1cf1e2621f5d7dd4f.pdf
2019-03-21
165
176
10.22059/jesphys.2019.256926.1007002
Radiowave propagation
ELF-VLF wave generation
Ionosphere
radio navigation
earthquake prediction
Ali Reza
Mahmoudian
mahmoudian.a@gmail.com
1
Assistant Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Mohammad Javad
Kalaee
mjkalaee@ut.ac.ir
2
Assistant Professor, Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran
AUTHOR
Borisov, N. and Stubbe, P., 1997, Excitation of longitudinal (field-aligned) currents by modulated HF heating of the ionosphere, J. Atmos. Solar-Terr. Phys., 59, 1973–1989.
1
Cohen, M. B., Inan, U. S. and Gołkowski, M., 2008, Geometric modulation: A more effective method of steerable ELF/VLF wave generation with continuous HF heating of the lower ionosphere, Geophys. Res. Lett., 35, L12101, doi:10.1029/2008GL034061.
2
Cohen, M. B., Inan, U. S., Gołkowski, M. and Lehtinen, N. G., 2010a, On the generation of ELF/VLF waves for long-distance propagation via steerable HF heating of the lower ionosphere, J. Geophys. Res., 115, A07322, doi:10.1029/2009JA015170.
3
Cohen, M. B., Inan, U. S., Gołkowski, M. and McCarrick, M. J., 2010b, ELF/VLF wave generation via ionospheric HF heating: Experimental comparison of amplitude modulation, beam painting, and geometric modulation, J. Geophys. Res., 115, A02302, doi:10.1029/2009JA014410.
4
Cohen, M. B., Inan, U. S. and Paschal, E. P., 2010c, Sensitive broadband ELF/VLF radio reception with the AWESOME instrument, IEEE Trans. Geosci. Remote Sens., 48(1), 3–17, doi:10.1109/TGRS.2009.2028334.
5
Eliasson, B. and Papadopoulos, K., 2009, Penetration of ELF currents and electromagnetic fields into the Earth’s equatorial ionosphere, J. Geophys. Res., 114, A10301, doi:10.1029/2009JA014213.
6
Lyatsky, W., Belova, E. G. and Pashin, A. B., 1996, Artificial magnetic pulsation generation by powerful ground-based transmitter, J. Atmos. Terr. Phys., 58, 407–414.
7
Master, M. J. and Uman, A. M., 1983, Transient electric and magnetic fields associated with establishing a finite electrostatic dipole, Am. J. Phys., 51(2), 118– 126.
8
Papadopoulos, K., 2015, Ionspheric modifications using mobile, high power HF transmitters based on TPM technology, paper presented at 2015 IEEE International Conference on Plasma Science (ICOPS), 24–28 May, Antalya, Turkey.
9
Wilkes, O., Nils P. G. and Ingvar, B., 1987. Loran-C and Omega: a study of the military importance of radio navigation aids. Oslo; Oxford; New York: Norwegian University Press/Oxford University Press. ISBN 82-00-07703-9.
10
ORIGINAL_ARTICLE
Projection of Climate Change Impacts on Seasonal Precipitation in Iranian Cold Regions Based on Radiative Forcing Scenarios (RCP)
Climate change and its impacts stand as the most important challenge to the world. One of the fundamental issues that have emerged in recent decades is the limited water resources. Because of the high dependence on precipitation, water resources are heavily susceptible to damage from climate change. Projection the effects associated with climate change is a major part of strategic planning in the current century. Cold climate regions are the main reservoir and feeding source for surface and underground water and a vital supplier of hydroelectric power in Iran. Any change in the seasonal precipitation situation will have severe outcomes for the status of water resources in cold regions. The purpose of this study is to investigate the impacts of climate change on seasonal precipitation in cold regions of Iran based on the outputs of new CMIP5 models and radiative forcing (RCP) scenarios. Alongside this, changes were first observed for the period 1980-2005. Afterwards, the data for the upcoming period up to the 2090 horizon were processed using the models BCC-CSM1.1, HadGEM2-ES, GFDL-CM3, MIROC-ESM and GISS-E2-R from the series of CMIP5 models of the MarksimGCM database based on the radiative forcing scenarios RCP8.5 and RCP4.5. The data were subsequently validated based on weighing method and RMSE, MAE, MBE and R2 evaluation criteria. The results of the processing were drawn on the digital elevation layer (DEM) of cold climate regions of Iran in the form of temporal-spatial seasonal precipitation distribution. Is The results showed that based on the weighing method and applying statistical indices on the output of CMIP5 models, the output of the HadGEM2.ES general circulation model is accompanied by fewer simulation errors in illustrating the climate change of the future period than the observation or baseline period. In fact, based on the evaluation criteria or errors, this model showed a higher compliance with observational data. In the monthly pattern in cold regions of Iran during the cold months, especially in the autumn and winter months, the precipitation parameter indicates a slight increase from 10-20 mm relative to the baseline period. This small increase in precipitation over the coming decades, based on the structure of the models, cannot be stable because of increasing temperature and evapotranspiration. However, in the annual and normal long-term pattern, the precipitation level will be less than the preceding period such that in the 2020-2055 period in the annual pattern, precipitation decreases by 54 mm, equivalent to 20.1% decrease. Seasonal precipitation will decrease in winter, autumn, and summer of the upcoming period up to the 2090 horizon, according to radiative forcing scenarios. Precipitation reduction in the summer will be more severe than in other seasons. Only in spring season will the amount of precipitation in the coming period increase slightly compared to the baseline period. Most of the spatial variations in precipitation distribution will occur in the cold climate regions of Iran in the high-altitude areas of the middle Alborz and especially in high Zagros regions. The center of areas with maximum precipitation in cold regions will mainly move to higher latitudes. In fact, the regions with substantial precipitation will become smaller, whereas areas with low rainfall will be extended. Therefore, climate change will have an impact on the temporal-spatial distribution of precipitation in the cold climate regions of Iran and will face with a future with less and variable precipitations.
https://jesphys.ut.ac.ir/article_67776_4956a1954e84c6fbc31ec4a92919093b.pdf
2019-03-21
177
196
10.22059/jesphys.2018.256956.1007003
climate change
Cold region
CMIP5
RCP
Precipitation
Hamzeh
Ahmadi
hamzehahmadi2009@gmail.com
1
Ph.D. Graduated, Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
LEAD_AUTHOR
Gholam Abbas
Fallah Ghalhari
ab_fa789@yahoo.com
2
Associate Professor, Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
AUTHOR
Mohammad
Baaghideh
mbaaghideh2005@yahoo.com
3
Associate Professor, Department of Climatology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran
AUTHOR
آقاخانی افشار، ا.، حسن زاده، ی.، بسالت پور، ع. ا. و پوررضا بیلندی، ر.، 1395، ارزیابی سالیانه مؤلفههای اقلیمی حوضه آبخیز کشف رود در دورههای آتی با استفاده از گزارش پنجم هیأت بین الدول تغییر اقلیم، نشریه پژوهشهای حفاظت آب و خاک، 23(6)، 233-217.
1
آشفته، پ. و مساح بوانی، ع.، 1388، تأثیر عدمقطعیت تغییر اقلیم بر دما و بارش حوضه آیدوغموش در دوره ۲۰۶۹-۲۰۴۰ میلادی، دانش آب و خاک، 1(2)، 98-85.
2
احمدی، ح.، 1396، بررسی اثرات تغییر اقلیم بر روی درخت سیب در ایران، پایان نامه دکتری، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، گروه جغرافیا.
3
احمدی، ح.، فلاح قالهری، غ. وگودرزی، م.، 1397، برآورد و تعیین الگوی فضایی نیاز آبی درخت سیب در ایران، فصلنامه اکوهیدرولوژی، 5 (1)، 160-149.
4
بابائیان، ا.، کریمیان، م.، مدیریان، ر.، بیاتانی، ف. و فهیمی نژاد، ا.، 1395، کارایی روشهای پس پردازش آماری در بهبود پیشبینی ماهانه بارش مدل MRI-CGCM3 در خراسان رضوی. تحقیقات منابع آب ایران، 12(2)، 92-83.
5
بابائیان، ا. و کوهی، م.، 1391، ارزیابی شاخصهای اقلیم کشاورزی تحت سناریوهای تغییر اقلیم در ایستگاههای منتخب خراسان رضوی. نشریه آب و خاک (علوم و صنایع کشاورزی)، 26(4)، 967-953.
6
پرهیزکاری، ا.، محمودی، ا. و شوکت فدایی، م.، 1396، ارزیابی اثرات تغییر اقلیم بر منابع در دسترس و تولیدات کشاورزی در حوضه آبخیز شاهرود. فصلنامه تحقیقات اقتصاد کشاورزی، 9 (1)، 50-23.
7
ترکمان، م.، 1394، بررسی اثر گرمایش و تغییر اقلیم بر ویژگیهای زراعی و تولید سیب زمینی در ایران، پایاننامه دکتری، دانشگاه فردوسی، دانشکده کشاورزی، گروه اکولوژی گیاهان زراعی. مشهد.
8
تیرگرفاخری، ف.، علیجانی، ب.، ضیاییان فیروزآبادی، پ. و اکبری، م.، 1396، شبیهسازی رواناب ناشی از ذوب برف تحت سناریوهای تغییر اقلیمی در حوضه ارمند. اکوهیدرولوژِی، 4 (2)، 368-357.
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حافظپرست، م. و پورخیراله، ز.، 1396، پایش خشکسالی هواشناسی بهمنظور حفظ پایداری در سناریوهای واداشت تابشی منطقه مطالعاتی (حوضه آبریز سد دویرج)، اکوهیدرولوژی، 4 (4)، 1239-1227.
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حمیدیان پور، م.، باعقیده، م. و عباسی نیا، م.، 1395، ارزیابی تغییرات دما و بارش جنوب شرق ایران با استفاده از ریزمقیاس نمایی خروجی مدلهای مختلف گردش عمومی جو در دوره 2099-2011، پژوهشهای جغرافیای طبیعی، 48(1)، 123-107.
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دانش فراز، ر. و رزاق پور، ه.، 1393، ارزیابی اثرات تغییر اقلیم بر تبخیر – تعرق پتانسیل در استان آذربایجان غربی، فصلنامه فضای اهر، 14(46)، 211-199.
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دلقندی، م. و موذن زاده، ر.، 1395، بررسی تغییرات مکانی – زمانی بارش و دمای ایران تحت شرایط تغییر اقلیم با در نظر گرفتن عدمقطعیت مدلهای AOGCM و سناریوهای انتشار، اکوهیدرولوژی، 3 (3)، 331-321.
13
عباسی، ف.، باباییان، ا.، حبیبی نوخندان، م.، گلی مختاری، ل.، ملبوسی، ش. و عسکری، ش.، 1389، ارزیابی تأثیر تغییر اقلیم بر دما و بارش ایران در دهههای آینده، با کمک مدلMAGICC – SCENGEN، پژوهشهای جغرافیای طبیعی، 72، 109-91.
14
گودرزی، م.، صلاحی، ب. حسینی، س. ا.، 1394، بررسی تأثیر تغییرات اقلیمی بر تغییرات رواناب سطحی (مطالعه موردی: حوضه آبریز دریاچه ارومیه)، فصلنامه اکوهیدرولوژی، 2(2)، 189-175.
15
Alexander, L. V. and Arblaster, J. M., 2017, Historical and projected trends in temperature and precipitation extremes in Australia in observations and CMIP5, Weather and Climate Extremes, 15 (2017), 34–56.
16
Dang, Z. and Chen, Y., 2018, Vulnerability assessment of spring wheat production to climate change in the inner Mongolia region of China. Ecological Indicators, 85,67-78. Mo, x, g., Xia, J., 2017, Impacts of climate change on agricultural water resources and adaptation on the north China plain. Advances in Climate change Research, 8(2), 93-98.
17
Duko, C., Zwart, S. J. and Hein, L., 2018, Impact of climate change on cropping pattern in a tropical, sub tropical watershed, PoloS ONE 13(3):1-21.
18
Hur, J. Ahn, J. B., 2015, The change of first – flowering date over South Korea projected fromdownscaled IPCC AR5 simulation:Peach and Pear. International Journal of Climatology, 35:1926-1937.
19
IPCC, 2014, Summary for policymakers. In: Ipcc. Climate change, impact, adaptation and vulnerability. Contribution of working group 2 to the Fifth Assessment Report of the Intergovernment Panel of Climate Change, pp. 132. Cmbridge, UK and New York, USA, Cambridge University Press.
20
Ishida, K., Gorguner, M., Ercan, A., Trinh, T. and Kavvas, M. L., 2017, Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5future climate projections, Science of the Total Environment 592, 12–24.
21
Jones, P. G. and Thornton, P. K., 2013, Generating downscaled weather data from a suite of climate models for agricultural modelling applications. Agricultural Systems, 114, 1-5.
22
Mahmood, R. and Shaofeng, J. I. A., 2016, An extended linear scaling method for downscaling temperature and its implication in the Jhelum River basin, Pakistan, and India, using CMIP5 GCMs. Theoretical and Applied Climatology, 130(3-4), 725-734.
23
Mathukumalli, S. R., Dammu, M., Sengottaiyan, V., Ongolu, S., Biradar, A. K., Kondru, V. R. and Cherukumalli, S. R., 2016, Prediction of Helicoverpa armigera Hubner on pigeonpea during future climate change periods using MarkSim multimodel data. Agricultural and Forest Meteorology, 228, 130-138.
24
Mo, X. G., Hu, S., Lin, Z. H., Liu, S. X. and Xia, J., 2017, Impacts of climate change on agricultural water resources and adaptation on the North China Plain. Advances in Climate Change Research, 8(2), 93-98.
25
Nouri, M., Homaee, M., Bannayan, M., Hoogenboom, G, 2017, Towards shifting planting date as an adaptation practice for rainfed wheat response to climate change. Agricultural Water Management, 186, 108-119.
26
Peng, S., Ding, Y., Wen, Z., Chen, Y., Cao, Y. and Ren, J., 2017, Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agricultural and Forest Meteorology 233, 183–194.
27
Romm, J., 2015, Climate change, what everyone needs to know. Oxford, University Press.
28
Shen, Y., Li, Sh., Chen, Y., Qi, Y. and Zhang, S., 2013, Estimation of regional irrigation water requirement and water supply risk in the arid region of Northwestern China 1989–2010, Agricultural Water Management 128, 55– 64.
29
Shrestha, S., Bach, T. V. and Pandey, V. P., 2016, Climate change impacts on groundwater resources in Mekong Delta under representative concentration pathways (RCPs) scenarios. Environmental Science & Policy 61, 1–13.
30
Taylor, K. E., Stouffer, R. J. and Meehl, G. A., 2012, An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc, 93, 485–498.
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Wang, B., Liu, D. L., Asseng, S., Macadam, I. and Yu, Q., 2015, Impact of climate change on wheat floering time in sastern Australia. Agriculture and forest Meteorology 209-210,11-21.
32
Zhao, L., Xu, J., Powell, A. M. and Jiang, Z., 2015, Uncertainties of the global-to-regional temperature and precipitation simulations in CMIP5 models for past and future 100 years. Theoretical and applied climatology, 122(1-2), 259-270.
33
ORIGINAL_ARTICLE
Simulation of the surface wind field by the WRF model in Oman Sea region with different initial and boundary conditions
Oman Sea and its coastlines have an important role in the international trade, coastal management and marine industries. Large weather instability and intense wind occur in Oman Sea due to tropical cyclones. The wind field simulated by atmospheric models can be used in ocean model for wave prediction. The main purpose of this research is to investigate applicability of WRF mesoscale model version 3-7-1 in surface wind simulation using various boundary and initial conditions over Oman Sea. for this aim, three data sets including Era-Interim reanalysis data, FNL and GFS analysis data have been used. Simulated wind at the coasts of Oman has been evaluated using observational data measured at synoptic stations in Iran and Oman and also data measured by buoy at Gheshm Island. Evaluation of simulated offshore wind has been done using data from National Climatic Data Center Blended Sea Winds with 0.25 degree horizontal resolution and 6-hourly time step. Moreover, SST data from NCEP dataset with 0.083 degree in horizontal resolution have been used as WRF input data. Model outputs have been improved based on nudging technique. In this research, WRF model has been run using three 3-, 9- and 27-km nests, that the smaller one covers Oman Sea and some portions of the Persian Gulf. The model has been run for a time of 60 hour with 12 hour spin-up period for June 2009. Finally, fifteen “2-day re-started” simulations were performed to complete one month simulations. Results show that all three simulations overestimate wind speed at the considered coast area and the largest error belong to simulations that used Era-Interim dataset and the smallest error occurred in simulations that used FNL dataset. Comparison of the three datasets (analysis and reanalysis ones) with observational data indicated that using GFS dataset provided more accurate data due to its higher resolution. Moreover, ECMWF datasets underestimated them, while simulations using ECMWF them data as initialization and boundary conditions overestimated the winds. Bias-averaged values over the offshore areas demonstrated that using GFS and FNL datasets leads to underestimation, while using Era-Interim dataset resulted in overestimation in of predicted winds. Histogram of wind speed reveals that maximum error occurred for low wind speed for all three datasets (wind speed smaller than 3 m/s). In the mid-range (wind speed between 3-12 m/s), the model has an appropriate performance for simulating wind speed. Using GFS and FNL underestimates wind speed larger than 12 m/s, while using Era-Interim data overestimates that. Simulations using GFS and FNL have little discrepancy for various wind speeds, due to same model in producing these datasets. While results obtained from Era-Interim differ significantly with those from GFS and FNL datasets. Using FNL dataset produced the least error in wind direction. Since both GFS and FNL datasets are produced in NCEP with the same data assimilation techniques and forecast systems, the significant difference between these two datasets refers to the number of used observational data in producing analysis dataset (more observational datasets have been used in producing FNL dataset, comparing with those used in producing GFS dataset). Therefore, it can be concluded that dense grid of observational data in producing analysis dataset has an important role in mesoscale simulations. As a conclusion, using FNL dataset an input of WRF model led to the best performance in simulation of wind speed and wind direction for coasts and offshore part of Oman Sea.
https://jesphys.ut.ac.ir/article_67772_ae08922dabf08b7383288eeeff73ae7d.pdf
2019-03-21
197
209
10.22059/jesphys.2018.258409.1007009
Wind field
WRF model
Oman Sea
reanalysis and analysis data
Initial and boundary conditions
Nudging
Parvin
Ghafarian
p.ghafarian@inio.ac.ir
1
Assistant Professor, Atmospheric Science Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran
LEAD_AUTHOR
Nafiseh
Pegahfar
pegahfar@inio.ac.ir
2
Assistant Professor, Atmospheric Science Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran
AUTHOR
Mohammad Reza
Mohammadpour Penchah
mrmohammadpur@yahoo.com
3
Ph.D. Graduated, Department of Non-biologic Atmospheric and Oceanic Sciences, Faculty of Marine Science and Technology, University of Hormozgan, Bandaabbas, Iran
AUTHOR
آزادی، م.، صوفیانی، م.، وکیلی، غ. و قائمی، ه.، 1395، مطالعه موردی اثر گوارد دادههای ایستگاههای دیدبانی و جو بالا بر برونداد بارش مدل WRF روی منطقه ایران، م. ژئوفیزیک ایران، جلد 10، 2، 110-119.
1
غلامی، س.، قادر، س.، خالقی زواره، ح. و غفاریان، پ.، 1397، ارزیابی پیشیابی میدان باد توسط مدل WRF تحت تأثیر شرایط اولیه و مرزی متفاوت در منطقه خلیج فارس: مقایسه دادههای همدیدی و ماهوارههای QuickSCAT و ASCAT، م. فیزیک زمین و فضا، دوره 44، 1، 227-243.
2
قادر، س.، یازجی، د.، سلطانپور، م. و نعمتی، م. ح.، 1394،: بهکارگیری یک سامانه همادی توسعه داده شده برای مدل WRF جهت پیشبینی میدان باد سطحی در محدوده خلیجفارس. دو فصلنامه هیدروفیزیک-دوره اول، 1، 41-54.
3
لایقی، ب.، قادر، س.، علی اکبری بیدختی، ع. و آزادی، م.، 1396، حساسیتسنجی شبیهسازیهای مدل WRF به پارامترسازیهای فیزیکی در محدوده خلیج فارس و دریای عمان در زمان مونسون تابستانی. مجله ژئوفیزیک ایران، 11(1)، 1-19.
4
Carvalho, D., Rocha, A., Gómez-Gesteira, M. and Santos, C., 2012, A sensitivity study of the WRF model in wind simulation for an area of high wind energy. Environmental Modelling and Software, 33, 23-34.
5
Carvalho, D., Rocha, A., Gómez-Gesteira, M. and Santos, C. S., 2014a, WRF wind simulation and wind energy production estimates forced by different reanalyses: comparison with observed data for Portugal. Applied Energy, 117, pp.116-126.
6
Carvalho, D., Rocha, A., Gómez-Gesteira, M. and Santos, C. S., 2014b, Offshore wind energy resource simulation forced by different reanalyses: comparison with observed data in the Iberian Peninsula. Applied Energy, 134, 57-64.
7
Dee, D. P. and Uppala, S., 2009, Variational bias correction of satellite radiance data in the ERA‐Interim reanalysis. Quarterly Journal of the Royal Meteorological Society, 135(644), pp.1830-1841.
8
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P. and Bechtold, P., 2011, The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society, 137(656), pp. 553-597.
9
Ghader, S., Montazeri Namin, M. and Chegini, F., Bohluly, A., 2014, Hindcast of Surface Wind Field over the Caspian Sea Using WRF Model. The 11th International Conference on Coasts, Ports and Marine Structures (ICOPMAS 2014), Tehran.
10
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.K., Hnilo, J.J., Fiorino, M. and Potter, G.L., 2002, NCEP-DEO AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society, 83, 1631-1643.
11
Mass, C. and Ovens, D., 2011, January. Fixing WRF’s high speed wind bias: A new subgrid scale drag parameterization and the role of detailed verification. In 24th Conference on Weather and Forecasting and 20th Conference on Numerical Weather Prediction, Preprints, 91st American Meteorological Society Annual Meeting (Vol. 23727).
12
Menendez, M., García-Díez, M., Fita, L., Fernández, J., Méndez, F. J. and Gutiérrez, J. M., 2014, High-resolution sea wind hindcasts over the Mediterranean area. Climate dynamics, 42(7-8), pp.1857-1872.
13
Otte, T. L., 2008, The impact of nudging in the meteorological model for retrospective air quality simulations. Part I: Evaluation against national observation networks. Journal of applied meteorology and climatology, 47(7), pp.1853-1867.
14
Pickett, M. H., Tang, W., Rosenfeld, L. K. and Wash, C. H., 2003, QuikSCAT satellite comparisons with nearshore buoy wind data off the U.S. west coast. J. Atmos. Oceanic Technol., 20, 1869–1879.
15
Pielke Sr., R. A., 2002, Mesoscale Meteorological Modeling, second ed. Academic Press, San Diego, CA.
16
Simmons, A., 2006, ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF newsletter, 110, pp.25-36.
17
Simmons, A., Uppala, S., Dee, D. and Kobayashi, S., 2006, ERAInterim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, No. 110, ECMWF, Reading, United Kingdom, 25–35.
18
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Huang, X. Y., Wang, W. and Powers, J. G., 2008, A Description of the Advanced Research WRF Version 3, NCAR TECHNICAL NOTE, NCAR/TN-475STR, pp. 113.
19
Tabata, Y., Hashiguchi, H., Yamamoto, M. K., Yamamoto, M., Yamanaka, M. D., Mori, S., Syamsudin, F. and Manik, T., 2011, Lower tropospheric horizontal wind over Indonesia: a comparison of wind profiler network observations with global reanalyses. Journal of Atmospheric and Solar-Terrestrial Physics, 73(9), pp.986-995.
20
Tang, W., Liu, W. T. and Stiles, B. W., 2004, Evaluation of highresolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions. IEEE Trans. Geosci. Remote Sens., 42, 1762–1769.
21
Trenberth, K. E., Dole, R., Xue, Y., Onogi, K., Dee, D., Balmaseda, M., Bosilovich, M., Schubert, S. and Large, W., 2010, Atmospheric reanalyses: A major resource for ocean product development and modeling. Proc." OceanObs, 9.
22
Zhang, H. M., Reynolds, R. W. and Bates, J. J., 2006, P2. 23 BLENDED AND GRIDDED HIGH RESOLUTION GLOBAL SEA SURFACE WIND SPEED AND CLIMATOLOGY FROM MULTIPLE SATELLITES: 1987-PRESENT. American Meteorological Society 2006 Annual Meeting, Paper #P2.23, Atlanta, GA, January 29 - February 2, 2006.
23
ORIGINAL_ARTICLE
Investigating the role of vegetation indices and geographic components on seasonal aerosol optical depth over Iran
Investigation of the role of vegetation indices and geographic components on seasonal aerosol optical depth (AOD) over the Iranian region is carried out. Aerosols are suspended particles in an air that have diameters between 0.001 and 100 micrometers. Aerosols play an important role in the radiation properties of atmosphere and hence affect the earth climate system. Vegetation cover can impede surface erosion by wind and hence, has a close relationship with the emission. Dust emission leading to dust events in urban area can have an adverse effect on human health as well as human activities, for example by reduction in visibility. This research aims to seasonally evaluate the roles of geographical locations and vegetation indices on AOD over Iran, based on satellite data. This includes the evaluations the role of each of these components in AOD550 nm variations. In this study, the daily data of the 6-level 3 products (MYD08_M3_6) including AOD550 nm, Deep Blue Algorithm, MODIS sensor data, Aqua satellite data, are used. Pixel data were downloaded over the Iranian region from 2003 to 2017 with a spatial resolution of 1 × 1 arc. Two indicators, namely the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) of the Aqua Satellite, for the study period with AOD data were used. The optical sensitivity of aerosols data was verified using the Aqua Satellite data from the Aerosol Robotic Network (AERONET). The GWR and OLS methods were used to find the spatial relationships of aerial photo sensor optical depths with geographic location and vegetation indices. The average values of AOD over Iran, based on the data of the Aqua, are between (0.11 for spring) and (0.16 for autumn) respectively. The average AOD value in the spring indicates the enhancement of dust events in the region. In winter, the average AOD value over Iran is 0.12, with the lowest standard deviation. In the summer, according to Aqua satellite data, this value is 0.133, with the maximum scatters and deviation from the largest mean observed value. Based on the EVI and NDVI indexes, the maximum statistical values, including the range of changes, maximum, average, scatter level and deviation from the typical values of both indicators were observed in the warm season of the year. The maximum EVI index peaked in the summer with 0.478 and the lowest of 0.043 in the winter. The maximum NDVI index, like the EVI index, was obtained for the summer with 0.777 and its lowest value is -0.69 for the spring. The maximum correlation between the atmospheric optical depth and geographic components of the area is for the altitude and then the latitude and then the longitude. The correlation between the AOD with the altitude and latitude of location of the area is negative and significant, and the correlation of the AOD with longitude is not significant in any seasons. There is a negative correlation between AOD and NDVI, and also EVI index in all seasons, although it is 0.039 in winter, which is relatively low. The results of the AOD assessment show that the maximum spring and autumn has the lowest average AOD over the Iranian area. This is due to the combination of dry conditions and relatively strong wind speeds in the spring those results in dust storms that increase the amount of AOD. In contrast, the maximum AOD over Iran is for the spring with a value of 0.48 that occurs in southwestern part of Iran. The second largest focal point, highlighted in all AOD seasons, is for the Persian Gulf coast area between Bushehr and Bandar Abbas. AOD over this coastal area can be associated with favorable wind conditions in mineral dust deposition which transported to the area and sea salt. Other areas with high AOD can be found in the Makran coastal area in the southeast of Iran, between the plain of Lut and the Mangrove plain, as pervious AOD study in southeastern Iran indicted. Based on the climate distribution of the EVI and NDVI vegetation indices and the seasonal spatial variation of aerosols, it is shown that vegetation factor in dust emission efficiency varies from one region to another with season. This regional disparity is due to the variation of vegetation-humus-release and the coupling of two or more of these factors; therefore, vegetation can significantly improve the treatment of dusty storm areas with the internal sources in the country. The maximum correlations with the geographic components of the location with the optical depth over the Iranian area are for the elevation and then the latitude and then the longitude. The correlation between the AOD with height and latitude is negative and with 5% level.
https://jesphys.ut.ac.ir/article_67771_09b8da9d41d51522f83986a5c33bcfd1.pdf
2019-03-21
211
233
10.22059/jesphys.2018.260582.1007019
Aerosol Optical Depth
Vegetation indices
MODIS Sensor
Iran
Mahmoud
Ahmadi
ma_ahmadi@sbu.ac.ir
1
Associate Professor, Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Ali Reza
Shakiba
a-shakiba@sbu.ac.ir
2
Associate Professor, Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
AUTHOR
Abbas Ali
Dadashi Roudbari
dadashiabbasali@gmail.com
3
Ph.D. Student, Department of Physical Geography, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
AUTHOR
احمدی، ح.، احمدی، م. و داداشی رودباری، ع.، 1397، آشکارسازی اثرات تغییر اقلیم از طریق دمای هوا بر پدیده ریزگرد بر اساس سناریوهای واداشت تابشی RCP (مطالعه موردی: منطقه غرب ایران، استان ایلام)، دومین همایش بینالمللی گردوغبار، 5 تا 7 اردیبهشت 1397 دانشگاه ایلام.
1
احمدی، م.، داداشی رودباری، ع. و جعفری، م، 1398، تاثیر ارتفاع لایهمرزی در توفانهای گردوغبار جنوب غرب ایران (مطالعه موردی 21 تا 24 فوریه 2016)، مخاطرات محیط طبیعی, 8(19)، 151-174.
2
احمدی، م.، و داداشی رودباری، ع.، 1397 الف، ارزیابی عمق نوری هواویزهای (AOD550nm) فصلی ایران مبتنی بر برونداد مدل پایشگر ترکیبات جوی و آبوهوایی (MACC)، دومین همایش بینالمللی گردوغبار، 5 تا 7 اردیبهشت 1397 دانشگاه ایلام.
3
احمدی، م.، و داداشی رودباری، ع.، 1397 ب، پایش فصلی روند عمق نوری هواویزها (AOD550nm) در ایران مبتنی بر الگوریتم Deep Blue سنجنده MODIS، دومین کنفرانس ملی آب و هواشناسی ایران، 19 اردیبهشت 1397، دانشگاه فردوسی مشهد.
4
انصافیمقدم، ط.، خوش اخلاق، ف.، شمسی پور، ع.، اخوان، ر.، صفرراد، ط. و امیراصلانی، ف.، 1396، پایش و ارزیابی اثرات گردوغبار بر تغییرات بارش در جنوب غرب ایران ا استفاده از سنجش از دور و GIS، سنجش از دور و GIS ایران، 9 (2)، 98-79.
5
براتی، غ.، مرادی، م.، شامخی، ع. و داداشی رودباری، ع.، 1396، تحلیل روابط طوفانهای غباری جنوب ایران با کمفشار سِند، مخاطرات محیط طبیعی، 6 (13)، 91-108.
6
برتینا، ه.، صیاد، غ.، متین فر، ح. و حجتی، س.، 1393، توزیع زمانی-مکانی ذرات معلق اتمسفری در غرب کشور بر مبنای دادههای طیفی سنجنده MODIS، نشریه پژوهشهای حفاظت آب و خاک، 21 (4)، 137-119.
7
بهرامی، ح. ع.، جلالی، م.، درویشی بلورانی، ع. و عزیزی، ر.، 1392، مدلسازی مکانی-زمانی وقوع طوفانهای گردوغبار در استان خوزستان، سنجشازدور و GIS ایران، 5 (2)، 95-114.
8
بیات، ر.، جعفری، سمیه، قرمز چشمه، ب. و چرخابی، ا. م.، 1395، مطالعه تأثیر ریزگردها بر تغییرات پوشش گیاهی (مطالعه موردی: تالاب شادگان، خوزستان)، سنجشازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 7 (2)، 17-32.
9
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ORIGINAL_ARTICLE
Ion-acoustic Solitons in Solar Winds Plasma Out of Thermal Equilibrium
In this paper, by applying the reductive perturbation method to the plasma fluids equations and by using a non-Maxwellian distribution function which is labeled via an invariant spectralindex and an independent parameter as the potential degrees of freedom via perturbation, a generalized Korteweg-de Vries (KdV) equation is derived for the ion-acoustic solitons in Solar winds plasma, which involves near-equilibrium and out of thermal equilibrium states. Here, the spectralindex describes the deviations from thermal equilibrium of plasma and itself is independent of the number of degrees of freedom of plasma. The near-equilibrium states where the spectral indices are distributed with the values of are applied for the inner Heliosphere regions, and the far-equilibrium states which are described by the spectral indices as that belongs to the Heliosheath regions. The analytical solution to the generalized KdV equation is calculated and its solitary wave solution is derived. Then, effects of the spectralindex , the potential degrees of freedom via perturbation , and the speed of pulse on the generalized dispersion coefficient () and generalized nonlinear coefficient () of KdV equation, and also on the structure of the ion-acoustic solitons are studied numerically. It is found that in the asymptotic limit of , it indicates a plasma in thermal equilibrium and the generalized KdV equation reduces to the standard KdV equation and its solitary wave solution. We show that the generalized dispersion coefficient tends smoothly to the standard limit of in the near-equilibrium states as , while it tends to zero in out of thermal equilibrium regions as . Furthermore, the generalized nonlinear coefficient has negative large values in far-equilibrium states with ,while it tends smoothly to the standard limit of in the case of an equilibrium plasma with . Moreover, the invariant spectral index has a critical value in the far-equilibrium states, where for the generalized nonlinear coefficient has positive values and for the generalized nonlinear coefficient has negative values. We found that in the vicinity of , corresponds to the escape state (where the transitions between near-equilibrium and far-equilibrium states happens), the variations of the coefficients and are considerable. We also found that the generalized dispersion coefficient () and the generalized nonlinear coefficient () depend on the potential degrees of freedom via perturbation, but their dependences are not considerable. Futhermore, depending on the values of the parameters and , the occurrence of ion-acoustic solitons with both positive and negative potentials is possible. In the near-equilibrium states () only positive polarity solitons are possible, which is in consistence with the standard KdV theory. But, the occurrence of negative polarity solitons is predicted in the far-equilibrium states with . Analyzing of the solitary wave profile shows that the amplitude and steepening of the ion-acoustic solitons grows in far-equilibrium states, labeled via indices . It is because of the existence of more fraction of suprathermal particles, which provide more effective interactions with the soliton and make it more prominent. Furthermore, propagation of a soliton with more speed results in a pulse with larger amplitude and narrower width, in consistence with the standard KdV theory. Moreover, examining the results with the various degrees of freedom, shows that the amplitude and steepening of the ion-acoustic solitons decrease with an increase in the potential degrees of freedom via perturbation. It is to be noted that for a perturbed potential as in KdV theory, the potential degrees of freedom has small values. Finally, we have analytically derived the amplitude () and the width () of the ion-acoustic solitons as functions of the spectral index and the potential degrees of freedom .Then, numerical plotting of and with respect to for various values of has confirmed the mentioned results.
https://jesphys.ut.ac.ir/article_69150_5a72e613d0d1aa0172f88a73260370e2.pdf
2019-03-21
235
246
10.22059/jesphys.2019.261483.1007024
Non-Maxwellian distribution function
Invariant kappa index
degrees of freedom
Soliton
Korteweg-de Vries equation
Solar wind
Ehsan
Saberian
e.saberian@neyshabur.ac.ir
1
Assistant Professor, Department of Physics, Faculty of Basic Sciences, University of Neyshabur, Neyshabur, Iran
LEAD_AUTHOR
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