کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1179525 962781 2015 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Spatio-temporal bivariate statistical models for atmospheric trace-gas inversion
ترجمه فارسی عنوان
مدل های آماری دو بعدی دو بعدی اسپکتیو-زمان برای گشتاور گازهای اتمسفری
کلمات کلیدی
مدلهای چند متغیره مشروط، انتشارات متان، آمار زمین شناسی چند متغیره، همیلتون مونت کارلو، آمار فضایی
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی

Atmospheric trace-gas inversion refers to any technique used to predict spatial and temporal fluxes using mole-fraction measurements and atmospheric simulations obtained from computer models. Studies to date are most often of a data-assimilation flavour, which implicitly consider univariate statistical models with the flux as the variate of interest. This univariate approach typically assumes that the flux field is either a spatially correlated Gaussian process or a spatially uncorrelated non-Gaussian process with prior expectation fixed using flux inventories (e.g., NAEI or EDGAR). Here, we extend this approach in three ways. First, we develop a bivariate model for the mole-fraction field and the flux field. The bivariate approach allows optimal prediction of both the flux field and the mole-fraction field, and it leads to significant computational savings over the univariate approach. Second, we employ a lognormal spatial process for the flux field that captures both the lognormal characteristics of the flux field (when appropriate) and its spatial dependence. Third, we propose a new, geostatistical approach to incorporate the flux inventories in our updates, such that the posterior spatial distribution of the flux field is predominantly data-driven. The approach is illustrated on a case study of methane (CH4) emissions in the United Kingdom and Ireland.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 149, Part B, 15 December 2015, Pages 227–241
نویسندگان
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