Inverse Modeling System Research Articles

Bayesian hybrid-kernel machine-learning-assisted sensitivity analysis and sensitivity-relevant inverse modeling for groundwater DNAPL contamination

Accurate source characterization and transport parameter estimation is important when seeking to predict the spatiotemporal distribution of dense non-aqueous phase liquid (DNAPL) contaminants in groundwater. However, this is a complex multimodal search problem prone to equifinality and premature convergence, which leads to considerable error. To address this, a sensitivity-relevant dynamic swarm intelligence (SRD-SI) algorithm embedded in a homotopy-variation mechanism is proposed in the present study to rationally balance the inversion processes of sensitivity-varied source characteristics and DNAPL transport parameters. In this approach, global optima are progressively approached in conjunction with the homotopy variation of the search space. Furthermore, to avoid computationally expensive numerical model repetition during the sensitivity analysis and inverse iterations, a Bayesian-based optimization framework that combines multiple kernel functions in a kernel extreme learning machine (KELM) model is designed considering the complex site conditions and statistical characteristics of the input variables, thus creating the Bayesian hybrid KELM (BHK-ELM) model for the reliable surrogate modeling of numerical DNAPL-transport simulations. The results show that the BHK-ELM model recognizes and effectively reconstructs the complex input − output mapping of the numerical model by increasing the determination coefficient R2 to 0.9988 while improving the computational efficiency approximately 4500-fold. Because source characteristics and boundary conditions are far more sensitive than transport parameters to the contaminant distribution, conventional inverse modeling methods struggle to accurately identify these transport parameters. In contrast, the proposed inverse modeling system combining sensitivity analysis, swarm intelligence, and homotopy variation is more stable and provides significantly more accurate estimations for all unknown variables. Compared with the traditional SI algorithm, the homotopy-variation SRD-SI reduced the maximum inversion relative error from 46.22 % to 9.53 %, while the mean inversion relative error was reduced from 11.09 % to 3.90 %.

A top-down estimation of subnational CO2 budget using a global high-resolution inverse model with data from regional surface networks

Top-down approaches, such as atmospheric inversions, are a promising tool for evaluating emission estimates based on activity-data. In particular, there is a need to examine carbon budgets at subnational scales (e.g. state/province), since this is where the climate mitigation policies occur. In this study, the subnational scale anthropogenic CO2 emissions are estimated using a high-resolution global CO2 inverse model. The approach is distinctive with the use of continuous atmospheric measurements from regional/urban networks along with background monitoring data for the period 2015–2019 in global inversion. The measurements from several urban areas of the U.S., Europe and Japan, together with recent high-resolution emission inventories and data-driven flux datasets were utilized to estimate the fossil emissions across the urban areas of the world. By jointly optimizing fossil fuel and natural fluxes, the model is able to contribute additional information to the evaluation of province–scale emissions, provided that sufficient regional network observations are available. The fossil CO2 emission estimates over the U.S. states such as Indiana, Massachusetts, Connecticut, New York, Virginia and Maryland were found to have a reasonable agreement with the Environmental Protection Agency (EPA) inventory, and the model corrects the emissions substantially towards the EPA estimates for California and Indiana. The emission estimates over the United Kingdom, France and Germany are comparable with the regional inventory TNO–CAMS. We evaluated model estimates using independent aircraft observations, while comparison with the CarbonTracker model fluxes confirms ability to represent the biospheric fluxes. This study highlights the potential of the newly developed inverse modeling system to utilize the atmospheric data collected from the regional networks and other observation platforms for further enhancing the ability to perform top-down carbon budget assessment at subnational scales and support the monitoring and mitigation of greenhouse gas emissions.

Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data

Abstract. Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications.

Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris

Abstract. Existing CO2 emissions reported by city inventories usually lag in real-time by a year or more and are prone to large uncertainties. This study responds to the growing need for timely and precise estimation of urban CO2 emissions to support present and future mitigation measures and policies. We focus on the Paris metropolitan area, the largest urban region in the European Union and the city with the densest atmospheric CO2 observation network in Europe. We performed long-term atmospheric inversions to quantify the citywide CO2 emissions, i.e., fossil fuel as well as biogenic sources and sinks, over 6 years (2016–2021) using a Bayesian inverse modeling system. Our inversion framework benefits from a novel near-real-time hourly fossil fuel CO2 emission inventory (Origins.earth) at 1 km spatial resolution. In addition to the mid-afternoon observations, we attempt to assimilate morning CO2 concentrations based on the ability of the Weather Research and Forecasting model with Chemistry (WRF-Chem) transport model to simulate atmospheric boundary layer dynamics constrained by observed layer heights. Our results show a long-term decreasing trend of around 2 % ± 0.6 % per year in annual CO2 emissions over the Paris region. The impact of the COVID-19 pandemic led to a 13 % ± 1 % reduction in annual fossil fuel CO2 emissions in 2020 with respect to 2019. Subsequently, annual emissions increased by 5.2 % ± 14.2 % from 32.6 ± 2.2 Mt CO2 in 2020 to 34.3 ± 2.3 Mt CO2 in 2021. Based on a combination of up-to-date inventories, high-resolution atmospheric modeling and high-precision observations, our current capacity can deliver near-real-time CO2 emission estimates at the city scale in less than a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.

Anthropogenic NOx Emission Estimations over East China for 2015 and 2019 Using OMI Satellite Observations and the New Inverse Modeling System CIF-CHIMERE

The Chinese government introduced regulations to control emissions and reduce the level of NOx pollutants for the first time with the 12th Five-Year Plan in 2011. Since then, the changes in NOx emissions have been assessed using various approaches to evaluate the impact of the regulations. Complementary to the previous studies, this study estimates anthropogenic NOx emissions in 2015 and 2019 over Eastern China using as a reference the Hemispheric Transport of Air Pollution (HTAP) v2.2 emission inventory for 2010 and the new variational inversion system the Community Inversion Framework (CIF) interfaced with the CHIMERE regional chemistry transport model and OMI satellite observations. We also compared the estimated NOx emissions with the independent Multi-resolution Emission Inventory for China (MEIC) v1.3, from 2015. The inversions show a slight global decrease in NOx emissions (in 2015 and 2019 compared to 2010), mainly limited to the most urbanized and industrialized locations. In the locations such as Baotou, Pearl River Delta, and Wuhan, the estimations in 2015 compared to 2010 are consistent with the target reduction (10%) of the 12th Five-Year Plan. Comparisons between our emission estimates and MEIC emissions in 2015 suggest that our estimates likely underestimate the emission reductions between 2010 and 2015 in the most polluted locations of Eastern China. However, our estimates suggest that the MEIC inventory overestimates emissions in regions where MEIC indicates an increase of the emissions compared to 2010.

Using Ensemble Meteorological Data Sets to Treat Meteorological Uncertainties in a Bayesian Volcanic Ash Inverse Modeling System: A Case Study, Grímsvötn 2011

AbstractAtmospheric dispersion models are employed in forecasting the atmospheric transport of ash clouds following a volcanic eruption. Errors in input meteorological data can, however, lead to discrepancies between the modeled and observed ash cloud locations. Furthermore, meteorological errors can affect the performance of inversion techniques used to estimate ash emissions. This study explores using ensemble meteorological data sets to overcome issues with meteorological errors in a Bayesian inversion system, Inversion Technique for Emissions Modelling (InTEM) for volcanic ash. Two measures (the evidence value and a cost function) are obtained from the Bayesian framework, and each measure is used to select a “best” meteorological data set from within the ensemble. These two best meteorological data sets are constructed in an iterative manner which is ideally suited for operational use when ash cloud forecasts are updated at regular intervals. The method is applied to the 2011 eruption of the Icelandic volcano Grímsvötn and improvements to ash cloud forecasts using the best meteorological data sets assessed. In this case study, errors in the deterministic meteorological data are significant, resulting in a reduction of ash emissions estimated by InTEM for volcanic ash. We also illustrate how the best meteorological data sets lead to more accurate modeling of the ash cloud.

Tube-based iterative-learning-model predictive control for batch processes using pre-clustered just-in-time learning methodology

Iterative-learning-model predictive control (ILMPC) has been considered a potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along cycles and reject real-time disturbances within cycles. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when it suffers the problem of model-plant mismatches. Therefore, simultaneously guaranteeing system convergence along the cycle-axis and robust stability on the time-axis is a challenging task in the framework designing of ILMPC control systems. To this end, we present a type of tube-based ILMPC strategy based on an empirical model for nonlinear batch processes. First, to describe the dynamic behavior of nonlinear batch processes as much as possible with the goal of low computational cost and high modeling accuracy, a pre-clustered just-in-time learning (JITL) model focused on operation data is developed. Then, an ancillary MPC controller is combined with the ILMPC algorithm to form a tube scheme to relieve the impact of model error. In addition, we construct an inverse model system (IMS) to systematically determine the first cycle control input trajectory of the proposed tube-based ILMPC algorithm. As a result, they simultaneously ensure the system convergence along the cycle-axis and robust stability on the time-axis. Still, they can improve convergence speed and tracking performance. Finally, comparative simulations demonstrate the superiority of the proposed control algorithm.

Effect of assimilating CO2 observations in the Korean Peninsula on the inverse modeling to estimate surface CO2 flux over Asia.

To investigate the impact of two CO2 observation datasets obtained from the Korean Peninsula on the surface CO2 flux estimation over Asia, the two datasets are assimilated into the CarbonTracker (CT) inverse modeling system and the estimated surface CO2 fluxes are analyzed. Anmyeon-do (AMY) and Gosan (GSN) sites in the Korean Peninsula have observed surface CO2 mole fraction since the late 1990s. To investigate the effect of assimilating the additional Korean observations on the surface CO2 flux estimation over Asia, two experiments are conducted. The reference experiment (CNTL) only assimilates observations provided by National Oceanic and Atmospheric Administration (NOAA), while the other experiment (EXP1) assimilates both NOAA observations and two Korean observation datasets. The results are analyzed for 9 years from 2003 to 2011 in Asia region because both AMY and GSN datasets exist almost completely for this period. The annual average of estimated biosphere CO2 flux of EXP1 shows more flux absorption in summer and less flux emission from fall to spring compared to CNTL, mainly on Eurasia Temperate and Eurasia Boreal regions. When comparing model results to independent CO2 concentration data from surface stations and aircraft, the root mean square error is smaller for EXP1 than CNTL. The EXP1 yields more reduction on uncertainty of estimated biosphere CO2 flux over Asia, and the observation impact of AMY, GSN sites on flux estimation is approximately 11%, which is greater than other observation sites around the world. Therefore, the two CO2 observation sets in the Korean Peninsula are useful in reducing uncertainties for regional as well as global scale CO2 flux estimation.

Using ensemble meteorological datasets to overcome limitations in a Bayesian volcanic ash inverse modelling system

Using ensemble meteorological datasets to overcome limitations in a Bayesian volcanic ash inverse modelling system

A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

AbstractThis article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just‐in‐time learning and extreme learning machine (JITL‐ELM). Then, based on the local dynamic JITL‐ELM model, an optimal quadratic‐criterion‐based iterative learning control (Q‐ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q‐ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model‐plant mismatch and real‐time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.

On the model uncertainties in Bayesian source reconstruction using an ensemble of weather predictions, the emission inverse modelling system FREAR v1.0, and the Lagrangian transport and dispersion model Flexpart v9.0.2

Abstract. Bayesian source reconstruction is a powerful tool for determining atmospheric releases. It can be used, amongst other applications, to identify a point source releasing radioactive particles into the atmosphere. This is relevant for applications such as emergency response in case of a nuclear accident or Comprehensive Nuclear-Test-Ban treaty verification. The method involves solving an inverse problem using environmental radioactivity observations and atmospheric transport models. The Bayesian approach has the advantage of providing an uncertainty quantification on the inferred source parameters. However, it requires the specification of the inference input errors, such as the observation error and model error. The latter is particularly hard to provide as there is no straightforward way to determine the atmospheric transport and dispersion model error. Here, the importance of model error is illustrated for Bayesian source reconstruction using a recent and unique case where radionuclides were detected on several continents. A numerical weather prediction ensemble is used to create an ensemble of atmospheric transport and dispersion simulations, and a method is proposed to determine the model error.

A pragmatic protocol for characterising errors in atmospheric inversions of methane emissions over Europe

This study aims at estimating errors to be accounted for in atmospheric inversions of methane (CH4) emissions at the European scale. Four types of errors are estimated in the concentration space over the model domain and at selected measurement sites. Furthermore, errors in emission inventories are estimated at country and source sector scales. A technically ready method is used, which is implemented by running a set of simulations of hourly CH4 mixing ratios for 2015 using two area-limited transport models at three horizontal resolutions with multiple data sets of emissions and boundary and initial conditions as inputs. The obtained error estimates provide insights into how these errors could be treated in an inverse modelling system for inverting CH4 emissions over Europe. The main results show that sources of transport errors may better be controlled alongside the emissions, which differs from usual inversion practices. The average total concentration error is estimated at 29 ppb. The assessed error of total CH4 emissions is 22% and emission errors are heterogeneous at sector (23–49%) and country scales (16–124%), with largest errors occurring in the waste sector due to uncertainties in activity data and emission factors and in Finland due to uncertainties in natural wetland emissions.

Source backtracking for dust storm emission inversion using an adjoint method: case study of Northeast China

Abstract. Emission inversion using data assimilation fundamentally relies on having the correct assumptions about the emission background error covariance. A perfect covariance accounts for the uncertainty based on prior knowledge and is able to explain differences between model simulations and observations. In practice, emission uncertainties are constructed empirically; hence, a partially unrepresentative covariance is unavoidable. Concerning its complex parameterization, dust emissions are a typical example where the uncertainty could be induced from many underlying inputs, e.g., information on soil composition and moisture, land cover and erosive wind velocity, and these can hardly be taken into account together. This paper describes how an adjoint model can be used to detect errors in the emission uncertainty assumptions. This adjoint-based sensitivity method could serve as a supplement of a data assimilation inverse modeling system to trace back the error sources in case large observation-minus-simulation residues remain after assimilation based on empirical background covariance. The method follows an application of a data assimilation emission inversion for an extreme severe dust storm over East Asia (Jin et al., 2019b). The assimilation system successfully resolved observation-minus-simulation errors using satellite AOD observations in most of the dust-affected regions. However, a large underestimation of dust in Northeast China remained despite the fact that the assimilated measurements indicated severe dust plumes there. An adjoint implementation of our dust simulation model is then used to detect the most likely source region for these unresolved dust loads. The backward modeling points to the Horqin desert as the source region, which was indicated as a non-source region by the existing emission scheme. The reference emission and uncertainty are then reconstructed over the Horqin desert by assuming higher surface erodibility. After the emission reconstruction, the emission inversion is performed again, and the posterior dust simulations and reality are now in much closer harmony. Based on our results, it is advised that emission sources in dust transport models include the Horqin desert as a more active source region.

Inverse modeling of fire emissions constrained by smoke plume transport using HYSPLIT dispersion model and geostationary satellite observations

Abstract. Smoke forecasts have been challenged by high uncertainty in fire emission estimates. We develop an inverse modeling system, the HYSPLIT-based Emissions Inverse Modeling System for wildfires (or HEIMS-fire), that estimates wildfire emissions from the transport and dispersion of smoke plumes as measured by satellite observations. A cost function quantifies the differences between model predictions and satellite measurements, weighted by their uncertainties. The system then minimizes this cost function by adjusting smoke sources until wildfire smoke emission estimates agree well with satellite observations. Based on HYSPLIT and Geostationary Operational Environmental Satellite (GOES) Aerosol/Smoke Product (GASP), the system resolves smoke source strength as a function of time and vertical level. Using a wildfire event that took place in the southeastern United States during November 2016, we tested the system's performance and its sensitivity to varying configurations of modeling options, including vertical allocation of emissions and spatial and temporal coverage of constraining satellite observations. Compared with currently operational BlueSky emission predictions, emission estimates from this inverse modeling system outperform in both reanalysis (21 out of 21 d; −27 % average root-mean-square-error change) and hindcast modes (29 out of 38 d; −6 % average root-mean-square-error change) compared with satellite observed smoke mass loadings.

A novel framework for inverse procedural texture modeling

Procedural textures are powerful tools that have been used in graphics for decades. In contrast to the alternative exemplar-based texture synthesis techniques, procedural textures provide user control and fast texture generation with low-storage cost and unlimited texture resolution. However, creating procedural models for complex textures requires a time-consuming process of selecting a combination of procedures and parameters. We present an example-based framework to automatically select procedural models and estimate parameters. In our framework, we consider textures categorized by commonly used high level classes. For each high level class we build a data-driven inverse modeling system based on an extensive collection of real-world textures and procedural texture models in the form of node graphs. We use unsupervised learning on collected real-world images in a texture class to learn sub-classes. We then classify the output of each of the collected procedural models into these sub-classes. For each of the collected models we train a convolutional neural network (CNN) to learn the parameters to produce a specific output texture. To use our framework, a user provides an exemplar texture image within a high level class. The system first classifies the texture into a sub-class, and selects the procedural models that produce output in that sub-class. The pre-trained CNNs of the selected models are used to estimate the parameters of the texture example. With the predicted parameters, the system can generate appropriate procedural textures for the user. The user can easily edit the textures by adjusting the node graph parameters. In a last optional step, style transfer augmentation can be applied to the fitted procedural textures to recover details lost in the procedural modeling process. We demonstrate our framework for four high level classes and show that our inverse modeling system can produce high-quality procedural textures for both structural and non-structural textures.

Cobalt-Based Fischer–Tropsch Synthesis: A Kinetic Evaluation of Metal–Support Interactions Using an Inverse Model System

Metal–support interactions in the cobalt–alumina system are evaluated using an inverse model system generated by impregnating Co3O4 with a solution of aluminum sec-butoxide in n-hexane. This results in the formation of nano-sized alumina islands on the surface of cobalt oxide. The activated model systems were kinetically evaluated for their activity and selectivity in the Fischer–Tropsch synthesis under industrially relevant conditions (220 °C, 20 bar). The kinetic measurements were complemented by H2-chemisorption, CO-TPR, and pyridine TPD. It is shown that the introduction of aluminum in the model system results in the formation of strong acid sites and enhanced CO dissociation, as evidenced in the CO-TPR. The incorporation of aluminum in the model systems led to a strong increase in the activity factor per surface atom of cobalt in the rate expression proposed by Botes et al. (2009). However, the addition of aluminum also resulted in a strong increase in the kinetic inhibition factor. This is accompanied by a strong decrease in the methane selectivity, and an increase in the desired C5+ selectivity. The observed activity and selectivity changes are attributed to the increase in the coverage of the surface with carbon with increasing aluminum content, due to the facilitation of CO dissociation in the presence of Lewis acid sites associated with the alumina islands on the catalytically active material.

Development of an efficient laser grid mapping technique: P-SLAM

ABSTRACTOccupancy grid mapping in the field of mobile robotics is an integrative and promising solution for the various prerequisite such as path planning, autonomous navigation, localization, SLAM (Simultaneously Localization And Mapping) etc. The reliability of the occupancy grid mapping depends upon diverse parameters such as sensor accuracy, size of a grid cell, intrinsic and extrinsic parameters, sensor registration, sensor modelling, scanning angle, ambient conditions, etc. This research reveals the uncertainty in the generation of the laser occupancy grid map with the implementation of conventional laser geometry technique when the detected obstacle/target is on the perimeter of the grid cell. During autonomous navigation, the obstacle and the mobile robot are in a dynamic state that consequence in the wrong perception of the environment by identifying the wrong grid cell as occupied in the occupancy grid map when the obstacle is on the perimeter of the grid cell. The examined error is reduced by a newly designed Perimeter-based SLAM (P-SLAM) technique based on the vector algebra, laser geometry, Inverse Sensor Model and coordinate system. The obtained results with the implementation of P-SLAM are validated w.r.t conventional approaches with qualitative and quantitative analysis by performing real-world experiments.

Dynamics of the Interaction Between Ceria and Platinum During Redox Processes

The work is focused on understanding the dynamics of the processes which occur at the interface between ceria and platinum during redox processes, by investigating an inverse catalytic model system made of ceria epitaxial islands and ultrathin films supported on Pt(111). The evolution of the morphology, structure and electronic properties is analyzed in real-time during reduction and oxidation, using low-energy electron microscopy and spatially resolved low-energy electron diffraction. The reduction is induced using different methods, namely thermal treatments in ultra-high vacuum and in H2 as well as deposition of Ce on the oxide surface, while re-oxidation is obtained by exposure to oxygen at elevated temperature. The use of two different epitaxial systems, continuous films and nanostructures, allows determining the influence of platinum proximity on the stabilization of the specific phases observed. The factors that limit the reversibility of the observed modifications with the different oxidation treatments are also discussed. The obtained results highlight important aspects of the cerium oxide/Pt interaction that are relevant for a complete understanding of the behavior of Pt/CeO2 catalysts.

Abundance and sources of atmospheric halocarbons in the Eastern Mediterranean

Abstract. A wide range of anthropogenic halocarbons is released to the atmosphere, contributing to stratospheric ozone depletion and global warming. Using measurements of atmospheric abundances for the estimation of halocarbon emissions on the global and regional scale has become an important top-down tool for emission validation in the recent past, but many populated and developing areas of the world are only poorly covered by the existing atmospheric halocarbon measurement network. Here we present 6 months of continuous halocarbon observations from Finokalia on the island of Crete in the Eastern Mediterranean. The gases measured are the hydrofluorocarbons (HFCs), HFC-134a (CH2FCF3), HFC-125 (CHF2CF3), HFC-152a (CH3CHF2) and HFC-143a (CH3CF3) and the hydrochlorofluorocarbons (HCFCs), HCFC-22 (CHClF2) and HCFC-142b (CH3CClF2). The Eastern Mediterranean is home to 250 million inhabitants, consisting of a number of developed and developing countries, for which different emission regulations exist under the Kyoto and Montreal protocols. Regional emissions of halocarbons were estimated with Lagrangian atmospheric transport simulations and a Bayesian inverse modeling system, using measurements at Finokalia in conjunction with those from Advanced Global Atmospheric Gases Experiment (AGAGE) sites at Mace Head (Ireland), Jungfraujoch (Switzerland) and Monte Cimone (Italy). Measured peak mole fractions at Finokalia showed generally smaller amplitudes for HFCs than at the European AGAGE sites except for periodic peaks of HFC-152a, indicating strong upwind sources. Higher peak mole fractions were observed for HCFCs, suggesting continued emissions from nearby developing regions such as Egypt and the Middle East. For 2013, the Eastern Mediterranean inverse emission estimates for the four analyzed HFCs and the two HCFCs were 13.9 (11.3–19.3) and 9.5 (6.8–15.1) Tg CO2eq yr−1, respectively. These emissions contributed 16.8 % (13.6–23.3 %) and 53.2 % (38.1–84.2 %) to the total inversion domain, which covers the Eastern Mediterranean as well as central and western Europe. Greek bottom-up HFC emissions reported to the UNFCCC were higher than our top-down estimates, whereas for Turkey our estimates agreed with UNFCCC-reported values for HFC-125 and HFC-143a, but were much and slightly smaller for HFC-134a and HFC-152a, respectively. Sensitivity estimates suggest an improvement of the a posteriori emission estimates, i.e., a reduction of the uncertainties by 40–80 % in the entire inversion domain, compared to an inversion using only the existing central European AGAGE observations.

Effect of Data Assimilation Parameters on The Optimized Surface CO2 Flux in Asia

In this study, CarbonTracker, an inverse modeling system based on the ensemble Kalman filter, was used to evaluate the effects of data assimilation parameters (assimilation window length and ensemble size) on the estimation of surface CO2 fluxes in Asia. Several experiments with different parameters were conducted, and the results were verified using CO2 concentration observations. The assimilation window lengths tested were 3, 5, 7, and 10 weeks, and the ensemble sizes were 100, 150, and 300. Therefore, a total of 12 experiments using combinations of these parameters were conducted. The experimental period was from January 2006 to December 2009. Differences between the optimized surface CO2 fluxes of the experiments were largest in the Eurasian Boreal (EB) area, followed by Eurasian Temperate (ET) and Tropical Asia (TA), and were larger in boreal summer than in boreal winter. The effect of ensemble size on the optimized biosphere flux is larger than the effect of the assimilation window length in Asia, but the importance of them varies in specific regions in Asia. The optimized biosphere flux was more sensitive to the assimilation window length in EB, whereas it was sensitive to the ensemble size as well as the assimilation window length in ET. The larger the ensemble size and the shorter the assimilation window length, the larger the uncertainty (i.e., spread of ensemble) of optimized surface CO2 fluxes. The 10-week assimilation window and 300 ensemble size were the optimal configuration for CarbonTracker in the Asian region based on several verifications using CO2 concentration measurements.