Recently updated on September 19th, 2024 at 01:59 pm<\/p>","protected":false},"excerpt":{"rendered":"
Greenhouse gas emissions, especially CO2 and CH4, at different spatial scales, were obtained using inverse modeling, the so-called top-down approach. Inversion models link emissions into the atmosphere with concentrations measured in the air using meteorological and air mass transport models. This approach is based on the scientific progress obtained in this sector to promote connections between the scientific and regulatory worlds.<\/p>\n","protected":false},"author":93,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"tags":[1466,1405,1321,1380],"targets":[1313,1317],"rapporti_tipologie":[762],"class_list":["post-191900","rapporti","type-rapporti","status-publish","hentry","tag-climate","tag-climate-change","tag-emissions","tag-greenhouse-gas","targets-decision-makers","targets-research","rapporti_tipologie-report-en"],"acf":{"projects":{"ID":191053,"post_author":"464","post_date":"2024-07-10 16:51:45","post_date_gmt":"2024-07-10 14:51:45","post_content":"","post_title":"Models and tools for interventions, including preventive interventions, for defending and improving grid security and resilience","post_excerpt":"Models and tools for interventions, including preventive interventions, for defending and improving grid security and resilience","post_status":"publish","comment_status":"open","ping_status":"closed","post_password":"","post_name":"models-and-tools-for-interventions-including-preventive-interventions-for-defending-and-improving-grid-security-and-resilience","to_ping":"","pinged":"","post_modified":"2024-08-09 16:02:23","post_modified_gmt":"2024-08-09 14:02:23","post_content_filtered":"","post_parent":0,"guid":"https:\/\/www.rse-web.it\/progetti\/models-and-tools-for-interventions-including-preventive-interventions-for-defending-and-improving-grid-security-and-resilience\/","menu_order":0,"post_type":"progetti","post_mime_type":"","comment_count":"0","filter":"raw"},"order_posts":"","dont_show_search":false,"related_posts":false,"dont_show_hompage":false,"show_on_slider":false,"single_post_data":{"titolo_spot":"","post_content":"
A top-down approach was applied in order to estimate the atmospheric emissions of the two most important greenhouse gases, i.e. carbon dioxide (CO2) and methane (CH4) in the European domain from 2018 to 2020. For this purpose, in situ atmospheric measurements, distributed over part of the domain of interest, a particle transport model, a first hypothesis emission field and an inversion algorithm were used.
\nIn line with the aims of the World Meteorological Organization, this study intends to promote the development of top-down modeling and foster connections between the scientific world, policymakers and regulators.
\nSome countries (e.g. Austria, United Kingdom and Australia) have already included emission estimates obtained from reverse modeling in their national reports (National Inventory Reports) for several years, and submitted them every year to the UNFCCC (United Nations Framework Convention on Climate Change). The application potential of these methods is now recognized given the growing ability to systematically and continuously monitor atmospheric concentrations and greenhouse gas emissions.
\nFrom an emission field, through the atmospheric transport model, the inversion system generates a simulated concentration time series at the measurement stations. The inversion algorithm optimizes the first-guess flow field, taking into account the associated uncertainty value, and minimizing discrepancies between real and simulated observations. The emission field obtained from this process is called a posteriori field.
\nAccurate estimates of national greenhouse gas emissions, mainly from the energy sector, are especially useful to understand the effectiveness of the actions taken to achieve the emission reduction objectives that the country must achieve to comply with international climate agreements.
\nThe national emissions obtained with the top-down approach were compared with the national inventory data (bottom-up approach) and with those reported in the EDGARv6.0 database (Emissions Database for Global Atmospheric Research), obtaining a fair agreement with both, considering the uncertainty associated with the estimate. For the estimates obtained at higher spatial resolution, different locations of emission points in correspondence with large urban and industrial areas are highlighted, but also the absence of locations in similar areas.<\/p>\n","link_estreno":false,"scarica_file":[{"download_option":"download","file_name":"Download Report","download":{"ID":173828,"id":173828,"title":"21010315","filename":"21010315.pdf","filesize":2129057,"url":"https:\/\/www.rse-web.it\/wp-content\/uploads\/2022\/11\/21010315.pdf","link":"https:\/\/www.rse-web.it\/en\/rapporti\/stima-delle-emissioni-dei-gas-serra-a-differenti-scale-spaziali-mediante-modellistica-inversa-e-confronto-con-gli-inventari-nazionali-per-valutare-lefficacia-delle-politiche-europee-nel-contr\/attachment\/21010315\/","alt":"","author":"93","description":"","caption":"","name":"21010315","status":"inherit","uploaded_to":173826,"date":"2022-11-22 13:27:02","modified":"2022-11-22 13:27:02","menu_order":0,"mime_type":"application\/pdf","type":"application","subtype":"pdf","icon":"https:\/\/www.rse-web.it\/wp-includes\/images\/media\/document.png"}}],"button":{"text":"","link":""},"referente_group":false,"data_emissione":"2021-12-31","autori":"F. D\u2019Assisi Apadula, A. Lanza (RSE S.p.A.), M. Maione, F. Graziosi (UniUR Carlo BO - DISPEA)","rapporto":"","rif_rse":"21010315"},"satellite_post_url":""},"yoast_head":"\n