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العنوان
Atmospheric dispersion modeling by using ensemble methods /
الناشر
Sayed Abdelmonam Mekhaimr ,
المؤلف
Sayed Abdelmonam Mekhaimr
هيئة الاعداد
باحث / Sayed Abdelmonam Mekhaimr
مشرف / Mohamed Magdy Abdelwahab
مناقش / Mohamed Magdy Abdelwahab
مناقش / Mohamed Magdy Abdelwahab
تاريخ النشر
2019
عدد الصفحات
306 P. , (9) Folded page of platas :
اللغة
الإنجليزية
الدرجة
الدكتوراه
التخصص
الفيزياء والفلك (المتنوعة)
تاريخ الإجازة
9/12/2019
مكان الإجازة
جامعة القاهرة - كلية العلوم - Meteorology
الفهرس
Only 14 pages are availabe for public view

from 336

from 336

Abstract

The atmospheric transport and dispersion modeling (ATM) of man-made radionuclides (RN) plays a vital role in monitoring and verifying any possible violation of the comprehensive nuclear-test-ban treaty (CTBT). Both forward ATM and backward (adjoint) ATM are used in this field. On the one hand, the forward ATM is used when the RN sources are known (e.g., nuclear power plants, medical isotope facility, etc.) in order to estimate the RN background at CTBT’s international monitoring system (IMS) stations. Also, The forward modeling is very important in CTBT’s on-site inspection (OSI) for a nuclear test, where it can be used to specify the best location and time of RN sample collection. On the other hand, The adjoint modeling is used as part of inverse modeling in order to estimate the unknown sources of RNs measurements which are observed at one or more of the IMS stations. from a decision maker point of view, these two types of modeling are essential tools in the field of CTBT’s verification system, but the existence of many sources of uncertainty (radionuclides measurements, meteorological fields, and modeling errors) represents the main challenge in using ATM model outputs. Therefore, many of the workers in this field continuously emphasize the importance of uncertainty quantification of the ATM models outputs. During the last two decades, many statistical techniques were developed in order to quantify the uncertainties in the meteorological fields (forecasts or simulations) by using the ensemble approacheveloping some statistical methods to quantify the uncertainty of these two types of modeling