Inverse modeling of European CH4 emissions: sensitivity to the observational network
نویسندگان
چکیده
Inverse modeling is widely employed to provide “top-down” emission estimates using atmospheric measurements. Here, we analyze the dependence of derived CH4 emissions on the sampling frequency and density of the observational surface network, using the TM5-4DVAR inverse modeling system and synthetic observations. This sensitivity study focuses on Europe. The synthetic observations are created by TM5 forward model simulations. The inversions of these synthetic observations are performed using virtually no knowledge on the a priori spatial and temporal distribution of emissions, i.e. the emissions are derived mainly from the atmospheric signal detected by the measurement network. Using the European network of stations for which continuous or weekly flask measurements are available for 2001, the synthetic experiments can retrieve the “true” annual total emissions for single countries such as France within 20%, and for all North West European countries together within ∼5%. However, larger deviations are obtained for South and East European countries due to the scarcity of stations in the measurement network. Upgrading flask sites to stations with continuous measurements leads to an improvement for central Europe in emission estimates. For realistic emission estimates over the whole European domain, however, a major extension of the number of stations in the existing network is required. We demonstrate the potential of an extended network of a total of ∼60 European stations to provide realistic emission estimates over the whole European domain. Correspondence to: M. G. Villani ([email protected])
منابع مشابه
Inverse modeling of global and regional CH4 emissions using SCIAMACHY satellite retrievals
[1] Methane retrievals from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) instrument onboard ENVISAT provide important information on atmospheric CH4 sources, particularly in tropical regions which are poorly monitored by in situ surface observations. Recently, Frankenberg et al. (2008a, 2008b) reported a major revision of SCIAMACHY retrievals due to an u...
متن کاملSupplement of Inverse modelling of European CH4 emissions during 2006–2012 using different inverse models and reassessed atmospheric observations
متن کامل
Sensitivity of the recent methane budget to LMDz sub-grid-scale physical parameterizations
With the densification of surface observing networks and the development of remote sensing of greenhouse gases from space, estimations of methane (CH4) sources and sinks by inverse modeling are gaining additional constraining data but facing new challenges. The chemical transport model (CTM) linking the flux space to methane mixing ratio space must be able to represent these different types of ...
متن کاملEmission evaluation of CO2 and CH4 gases in the selected gas pressure booster station in the Bangestan field of the National Iranian Oil Company
Background: Iran is located in the seventh rank in terms of CO2 emissions resulting from the fuel combustion in the world. Gas compressor booster stations, due to the several sources of contaminants, are causing the release of large amounts of CO2 and CH4, which will cause climate change therefore, estimating the emissions of the gases from oil and gas, different processing units are necessary....
متن کاملModeling and Optimization of Energy Inputs and Greenhouse Gas Emissions for Eggplant Production Using Artificial Neural Network and Multi-Objective Genetic Algorithm
This paper studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic algorithm in Guilan province of Iran. Results showed that the highest share of energy consumption belongs to diesel fuel (49.24%); followed by nitrogen (33.30%). The results indicated that a total energy input of 13910.67 ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010