الگوی تغییرات اقلیمی توام دما و بارش سالیانه شهر ایلام با تابع مفصل

تغییرات اقلیمی به عنوان یک پدیده‌ی طبیعی، معمولاً پدیده هایی چند متغیره هستند که تحت تاثیر عوامل مختلف بوده و از نوعی ناهمگنی برخوردارند. بررسی این پدیده ها به صورت جامع و واحد و همگن به ویژه در حالت چند متغیره می تواند به نتایج کاملاً گمراه کننده ای منجر شود. از این رو، توزیع احتمالاتی داده‌های تصادفی چند متغیره در مقایسه با حالت یک متغیره آنها به دلیل وابستگی غیرخطی بین متغیرهای تصادفی، پیچیده تر است. یکی از روش‌های حل این مشکل استفاده از توابع مفصل می باشد که در این مقاله، با استفاده از توابع مفصل، یک الگوی تحلیلی توام بین دما و بارش در پیش بینی تغییرات اقلیمی شهر ایلام ارائه گردید. نتایج نشان داد که عملکرد توابع مفصل نزدیک به هم بوده و از بین توابع مفصل مورد بررسی، مفصل لگامبل-بارنت قابلیت مدل کردن وابستگی بارش و دمای ایستگاه ایلام مناسبتر بود. این نتایج بر اساس مقایسه ی اندازه های وابستگی بین داده‌های اصلی و داده های شبیه سازی شده برای 1000 نمونه نیز مورد بررسی قرار گرفته است.

the pattern of climatic changes including annual temperature and precipitation of ilam city with copula function

climatic changes as a natural phenomenon are usually multivariate phenomena that are influenced by various factors and have a kind of heterogeneity. examining these phenomena in a comprehensive, single and homogeneous manner, especially in multivariate mode, can lead to completely misleading results. in many practical problems, identifying the appropriate model for the possible distribution of climate changes is of particular importance. because climate changes, as a natural phenomenon, are usually multi-variable phenomena that are influenced by various factors and have a kind of heterogeneity. investigating these phenomena in a comprehensive, unified and homogeneous manner, especially in multivariate mode, can lead to completely misleading results. in general, the probability distribution of multivariate random data is more complicated compared to their univariate state due to the nonlinear dependence between random variables. one of the ways to solve this problem is the use of detailed functions, which has been the focus of researchers in recent years. due to its high flexibility, the application and use of detailed function is a very useful tool in most scientific fields, including medicine, agriculture, meteorology, marketing, management, etc. the theory of detailed functions as the basis of this science was presented by sklar (1956). detailed functions are a powerful tool for constructing multivariate distribution functions based on one-dimensional marginal distribution functions. in fact, detailed functions describe the type and how the variables are related. show. detailed functions express the non-parametric and dependence features of distribution functions of random variables well. detailed functions can be used in risk measurement problems. because, in quantitative risk problems, the role dependence structure it plays an important role and with the knowledge of the dependence structure, a measure of risk can be obtained with the help of the detailed function.therefore, the probability distribution of multivariate random data is more complicated compared to their univariate state due to the nonlinear dependence between random variables. one of the ways to solve this problem is the use of detailed functions. in this article, using detailed functions, a combined analytical model between temperature and precipitation was presented in the prediction of climate changes in ilam city. the results showed that the performance of the joint functions were close to each other and among the examined joint functions, the legamble-barnett joint was more suitable for modeling the dependence of rainfall and temperature at ilam station. these results have been analyzed based on the comparison of the dependence sizes between the original data and the simulated data for 1000 samples.