scholarly journals Quantification of the modelling uncertainties in atmospheric release source assessment and application to the reconstruction of the autumn 2017 Ruthenium 106 source

2021 ◽  
Author(s):  
Joffrey Dumont Le Brazidec ◽  
Marc Bocquet ◽  
Olivier Saunier ◽  
Yelva Roustan
Keyword(s):  
Source Term ◽  
Transport Model ◽  
Unknown Origin ◽  
Physical Parameters ◽  
Observation Error ◽  
Model Errors ◽  
Mcmc Algorithms ◽  
Source Assessment ◽  
Modelling Uncertainties ◽  
Atmospheric Release

Abstract. Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as the likelihood covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to improve on these distributions, and therefore to get a better representation of the uncertainties. First, a method based on ensemble forecasting is proposed: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Secondly, the choice of the statistical likelihood is shown to alter the nuclear source assessment, and several suited distributions for the errors are advised. Finally, two advanced designs of the covariance matrix associated to the observation error are proposed. These methods are applied to the case of the detection of Ruthenium 106 of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, to quantify the uncertainties associated to the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.

scholarly journals Quantification of uncertainties in the assessment of an atmospheric release source applied to the autumn 2017 <sup>106</sup>Ru event

2021 ◽  
Vol 21 (17) ◽  
pp. 13247-13267
Author(s):  
Joffrey Dumont Le Brazidec ◽  
Marc Bocquet ◽  
Olivier Saunier ◽  
Yelva Roustan
Keyword(s):  
Source Term ◽  
Transport Model ◽  
Unknown Origin ◽  
Ensemble Forecasting ◽  
Physical Parameters ◽  
Observation Error ◽  
A Posteriori ◽  
Model Errors ◽  
Mcmc Algorithms ◽  
Atmospheric Release

Abstract. Using a Bayesian framework in the inverse problem of estimating the source of an atmospheric release of a pollutant has proven fruitful in recent years. Through Markov chain Monte Carlo (MCMC) algorithms, the statistical distribution of the release parameters such as the location, the duration, and the magnitude as well as error covariances can be sampled so as to get a complete characterisation of the source. In this study, several approaches are described and applied to better quantify these distributions, and therefore to get a better representation of the uncertainties. First, we propose a method based on ensemble forecasting: physical parameters of both the meteorological fields and the transport model are perturbed to create an enhanced ensemble. In order to account for physical model errors, the importance of ensemble members are represented by weights and sampled together with the other variables of the source. Second, once the choice of the statistical likelihood is shown to alter the nuclear source assessment, we suggest several suitable distributions for the errors. Finally, we propose two specific designs of the covariance matrix associated with the observation error. These methods are applied to the source term reconstruction of the 106Ru of unknown origin in Europe in autumn 2017. A posteriori distributions meant to identify the origin of the release, to assess the source term, and to quantify the uncertainties associated with the observations and the model, as well as densities of the weights of the perturbed ensemble, are presented.

scholarly journals Variational assimilation of IASI SO<sub>2</sub> plume height and total-column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model

2016 ◽  
Author(s):  
Julius Vira ◽  
Elisa Carboni ◽  
Roy G. Grainger ◽  
Mikhail Sofiev
Keyword(s):  
Source Term ◽  
Transport Model ◽  
Model Simulation ◽  
Observation Error ◽  
A Posteriori ◽  
So2 Emissions ◽  
Plume Height ◽  
Observation Operator ◽  
Truncated Iteration ◽  
Total Column

Abstract. This study focuses on two new aspects on inverse modelling of volcanic emissions. First, we derive an observation operator for satellite retrievals of plume height, and second, we solve the inverse problem using the 4D-Var method. The approach is demonstrated by assimilating IASI SO2 plume height and total column retrievals in a source term inversion for the 2010 eruption of Eyjafjallajökull. The inversion resulted in temporal and vertical reconstruction of the SO2 emissions during the 1–20 May, 2010 with formal vertical and temporal resolutions of 500 m and 12 hours. The plume height observation operator is based on simultaneous assimilation of the plume height and total column retrievals. The plume height is taken to represent the vertical centre of mass, which is transformed into the first moment of mass. This makes the observation operator linear and simple to implement. The necessary modifications to the observation error covariance matrix are derived. Regularisation by truncated iteration is investigated as a simple and efficient regularisation method for the 4D-Var based inversion. In an experiment with synthetic observations, the truncated iteration was found to perform similarly to the commonly used Tikhonov regularisation. However, the truncated iteration allows the amount of regularisation to be varied a posteriori, without repeating the inversion. For inverting the Eyjafjallajökull SO2 emission at the temporal and vertical resolution used in this study, the 4D-Var method required about 70 % less computational effort than commonly used methods based on performing a separate model simulation for each degree of freedom in the estimated source term. Compared to the inversion using only total column retrievals, assimilating the plume height resulted in a vertical emission profile more closely matching the ash plume heights observed by radar. The a posteriori source term gave an estimate of 0.29 Tg erupted SO2 of which 95 % was injected below 11 km.

scholarly journals Variational assimilation of IASI SO<sub>2</sub> plume height and total column retrievals in the 2010 eruption of Eyjafjallajökull using the SILAM v5.3 chemistry transport model

2017 ◽  
Vol 10 (5) ◽  
pp. 1985-2008 ◽  
Author(s):  
Julius Vira ◽  
Elisa Carboni ◽  
Roy G. Grainger ◽  
Mikhail Sofiev
Keyword(s):  
Source Term ◽  
Mean Squared Error ◽  
Transport Model ◽  
Observation Error ◽  
A Posteriori ◽  
Centre Of Mass ◽  
Plume Height ◽  
Observation Operator ◽  
Truncated Iteration ◽  
Total Column

Abstract. This study focuses on two new aspects of inverse modelling of volcanic emissions. First, we derive an observation operator for satellite retrievals of plume height, and second, we solve the inverse problem using an algorithm based on the 4D-Var data assimilation method. The approach is first tested in a twin experiment with simulated observations and further evaluated by assimilating IASI SO2 plume height and total column retrievals in a source term inversion for the 2010 eruption of Eyjafjallajökull. The inversion resulted in temporal and vertical reconstruction of the SO2 emissions during 1–20 May 2010 with formal vertical and temporal resolutions of 500 m and 12 h.The plume height observation operator is based on simultaneous assimilation of the plume height and total column retrievals. The plume height is taken to represent the vertical centre of mass, which is transformed into the first moment of mass (centre of mass times total mass). This makes the observation operator linear and simple to implement. The necessary modifications to the observation error covariance matrix are derived.Regularization by truncated iteration is investigated as a simple and efficient regularization method for the 4D-Var-based inversion. In the twin experiments, the truncated iteration was found to perform similarly to the commonly used Tikhonov regularization, which in turn is equivalent to a Gaussian a priori source term. However, the truncated iteration allows the level of regularization to be determined a posteriori without repeating the inversion.In the twin experiments, assimilating the plume height retrievals resulted in a 5–20 % improvement in root mean squared error but simultaneously introduced a 10–20 % low bias on the total emission depending on assumed emission profile. The results are consistent with those obtained with real data. For Eyjafjallajökull, comparisons with observations showed that assimilating the plume height retrievals reduced the overestimation of injection height during individual periods of 1–3 days, but for most of the simulated 20 days, the injection height was constrained by meteorological conditions, and assimilation of the plume height retrievals had only small impact. The a posteriori source term for Eyjafjallajökull consisted of 0.29 Tg (with total column and plume height retrievals) or 0.33 Tg (with total column retrievals only) erupted SO2 of which 95 % was injected below 11 or 12 km, respectively.

Spatial Variability of Physical Parameters and Processes in Two Field Soils

1991 ◽  
Vol 22 (5) ◽  
pp. 327-340 ◽  
Author(s):  
K. Høgh Jensen ◽  
J. C. Refsgaard
Keyword(s):  
Transport Model ◽  
Flow Direction ◽  
Measurement Data ◽  
Flow Simulation ◽  
Solute Concentration ◽  
Conclusive Evidence ◽  
Physical Parameters ◽  
Effective Parameters ◽  
The Mean ◽  
Tracer Experiments

A numerical analysis of solute transport in two spatially heterogeneous fields is carried out assuming that the fields are composed of ensembles of one-dimensional non-interacting soil columns, each column representing a possible soil profile in statistical terms. The basis for the analysis is the flow simulation described in Part II (Jensen and Refsgaard, this issue), which serves as input to a transport model based on the convection-dispersion equation. The simulations of the average and variation in solute concentration in planes perpendicular to the flow direction are compared to measurements obtained from tracer experiments carried out at the two fields. Due to the limited amount of measurement data, it is difficult to draw conclusive evidence of the simulations, but reliable simulations are obtained of the mean behaviour within the two fields. The concept of equivalent soil properties is also tested for the transport problem in heterogeneous soils. Based on effective parameters for the retention and hydraulic conductivity functions it is possible to predict the mean transport in the two experimental fields.

scholarly journals A 4D-Var inversion system based on the icosahedral grid model (NICAM-TM 4D-Var v1.0) – Part 2: Optimization scheme and identical twin experiment of atmospheric CO<sub>2</sub> inversion

2017 ◽  
Vol 10 (6) ◽  
pp. 2201-2219 ◽  
Author(s):  
Yosuke Niwa ◽  
Yosuke Fujii ◽  
Yousuke Sawa ◽  
Yosuke Iida ◽  
Akihiko Ito ◽  
...  
Keyword(s):  
Transport Model ◽  
Surface Fluxes ◽  
Identical Twin ◽  
Inversion Problem ◽  
Model Errors ◽  
Optimization Scheme ◽  
Error Covariance ◽  
Optimal Setting ◽  
Source Sink ◽  
Quasi Newton

Abstract. A four-dimensional variational method (4D-Var) is a popular technique for source/sink inversions of atmospheric constituents, but it is not without problems. Using an icosahedral grid transport model and the 4D-Var method, a new atmospheric greenhouse gas (GHG) inversion system has been developed. The system combines offline forward and adjoint models with a quasi-Newton optimization scheme. The new approach is then used to conduct identical twin experiments to investigate optimal system settings for an atmospheric CO2 inversion problem, and to demonstrate the validity of the new inversion system. In this paper, the inversion problem is simplified by assuming the prior flux errors to be reasonably well known and by designing the prior error correlations with a simple function as a first step. It is found that a system of forward and adjoint models with smaller model errors but with nonlinearity has comparable optimization performance to that of another system that conserves linearity with an exact adjoint relationship. Furthermore, the effectiveness of the prior error correlations is demonstrated, as the global error is reduced by about 15 % by adding prior error correlations that are simply designed when 65 weekly flask sampling observations at ground-based stations are used. With the optimal setting, the new inversion system successfully reproduces the spatiotemporal variations of the surface fluxes, from regional (such as biomass burning) to global scales. The optimization algorithm introduced in the new system does not require decomposition of a matrix that establishes the correlation among the prior flux errors. This enables us to design the prior error covariance matrix more freely.

scholarly journals Key factors affecting single scattering albedo calculation: Implications for aerosol climate forcing

2017 ◽  
Author(s):  
Duseong S. Jo ◽  
Rokjin J. Park ◽  
Jaein I. Jeong ◽  
Gabriele Curci ◽  
Hyung-Min Lee ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Radiative Forcing ◽  
Transport Model ◽  
Geometric Mean ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Geometric Standard Deviation ◽  
Physical Parameters ◽  
Chemical Transport Model ◽  
Aerosol Mass

Abstract. Single Scattering Albedo (SSA), the ratio of scattering efficiency to total extinction efficiency, is an essential parameter used to estimate the Direct Radiative Forcing (DRF) of aerosols. However, SSA is one of the large contributors to the uncertainty of DRF estimations. In this study, we examined the sensitivity of SSA calculations to the physical properties of absorbing aerosols, in particular, Black Carbon (BC), Brown Carbon (BrC), and dust. We used GEOS-Chem 3-D global chemical transport model (CTM) simulations and a post-processing tool for the aerosol optical properties (FlexAOD). The model and input parameters were evaluated by comparison against the observed aerosol mass concentrations and the Aerosol Optical Depth (AOD) values obtained from global surface observation networks such as the global Aerosol Mass Spectrometer (AMS) dataset, the Surface Particulate Matter Network (SPARTAN), and the Aerosol Robotic Network (AERONET). The model was generally successful in reproducing the observed variability of both the Particulate Matter 2.5 μm (PM2.5) and AOD (R ~ 0.76) values, although it underestimated the magnitudes by approximately 20 %. Our sensitivity tests of the SSA calculation revealed that the aerosol physical parameters, which have generally received less attention than the aerosol mass loadings, can cause large uncertainties in the resulting DRF estimation. For example, large variations in the calculated BC absorption may result from slight changes of the geometric mean radius, geometric standard deviation, real and imaginary refractive indices, and density. The inclusion of BrC and observationally-constrained dust size distributions also significantly affected the SSA, and resulted in a remarkable improvement for the simulated SSA at 440 nm (bias was reduced by 44–49 %) compared with the AERONET observations. Based on the simulations performed during this study, we found that the global aerosol direct radiative effect was increased by 10 % after the SSA bias was reduced.

scholarly journals Numerical Modeling of Contaminant Transport with Spatially-Dependent Dispersion and Non-Linear Chemical Reaction

2007 ◽  
Vol 12 (3) ◽  
pp. 329-343 ◽  
Author(s):  
A. J. Chamkha
Keyword(s):  
Contaminant Transport ◽  
Chemical Reaction ◽  
Numerical Solutions ◽  
Dispersion Coefficient ◽  
Transport Model ◽  
Physical Parameters ◽  
Contaminant Concentration ◽  
Dimensionless Time ◽  
Polynomial Type ◽  
Special Cases

A one-dimensional advective-dispersive contaminant transport model with scale-dependent dispersion coefficient in the presence of a nonlinear chemical reaction of arbitrary order is considered. Two types of variations of the dispersion coefficient with the downstream distance are considered. The first type assumes that the dispersivity increases as a polynomial function with distance while the other assumes an exponentiallyincreasing function. Since the general problem is nonlinear and possesses no analytical solutions, a numerical solution based on an efficient implicit iterative tri-diagonal finitedifference method is obtained. Comparisons with previously published analytical and numerical solutions for special cases of the main transport equation are performed and found to be in excellent agreement. A parametric study of all physical parameters is conducted and the results are presented graphically to illustrate interesting features of the solutions. It is found that the chemical reaction order and rate coefficient have significant effects on the contaminant concentration profiles. Furthermore, the scale-dependent polynomial type dispersion coefficient is predicted to obtain significant changes in the contaminant concentration at all dimensionless time stages compared with the constant dispersion case. However, relatively smaller changes in the concentration level are predicted for the exponentially-increasing dispersion coefficient.

scholarly journals First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system

2009 ◽  
Vol 9 (2) ◽  
pp. 6691-6737 ◽  
Author(s):  
S. Massart ◽  
C. Clerbaux ◽  
D. Cariolle ◽  
A. Piacentini ◽  
S. Turquety ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Polar Regions ◽  
Observation Error ◽  
Random Errors ◽  
Chemistry Transport Model ◽  
Ozone Data ◽  
The One ◽  
New Generation

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is one of the five European new generation instruments carried by the polar-orbiting MetOp-A satellite. Data assimilation is a powerful tool to combine these data with a numerical model. This paper presents the first steps made towards the assimilation of the total ozone columns from the IASI measurements into a chemistry transport model. The IASI ozone data used are provided by an inversion of radiances performed at the LATMOS (Laboratoire Atmosphères, Milieux, Observations Spatiales). As a contribution to the validation of this dataset, the LATMOS-IASI data are compared to a four dimensional ozone field, with low systematic and random errors compared to ozonesondes and OMI-DOAS data. This field results from the combined assimilation of ozone profiles from the MLS instrument and of total ozone columns from the SCIAMACHY instrument. It is found that on average, the LATMOS-IASI data tends to overestimate the total ozone columns by 2% to 8%. The random observation error of the LATMOS-IASI data is estimated to about 6%, except over polar regions and deserts where it is higher. Using this information, the LATMOS-IASI data are then assimilated, combined with the MLS data. This first LATMOS-IASI data assimilation experiment shows that the resulting analysis is quite similar to the one obtained from the combined MLS and SCIAMACHY data assimilation.

scholarly journals Performance Optimization of FD-SOI Hall Sensors Via 3D TCAD Simulations

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2751 ◽  
Author(s):  
Linjie Fan ◽  
Jinshun Bi ◽  
Kai Xi ◽  
Sandip Majumdar ◽  
Bo Li
Keyword(s):  
Performance Optimization ◽  
Computer Aided Design ◽  
Transport Model ◽  
Optimal Structure ◽  
Physical Parameters ◽  
Voltage Sensitivity ◽  
Offset Voltage ◽  
Trade Offs ◽  
Hall Sensors ◽  
Tcad Simulations

This work investigates the behavior of fully depleted silicon-on-insulator (FD-SOI) Hall sensors with an emphasis on their physical parameters, namely the aspect ratio, doping concentration, and thicknesses. Via 3D-technology computer aided design (TCAD) simulations with a galvanomagnetic transport model, the performances of the Hall voltage, sensitivity, efficiency, offset voltage, and temperature characteristics are evaluated. The optimal structure of the sensor in the simulation has a sensitivity of 86.5 mV/T and an efficiency of 218.9 V/WT at the bias voltage of 5 V. In addition, the effects of bias, such as the gate voltage and substrate voltage, on performance are also simulated and analyzed. Optimal structure and bias design rules are proposed, as are some adjustable trade-offs that can be chosen by designers to meet their own Hall sensor requirements.

Improved Estimation and Forecasting Through Residual-Based Model Error Quantification

SPE Journal ◽  
2020 ◽  
Vol 25 (02) ◽  
pp. 951-968 ◽  
Author(s):  
Minjie Lu ◽  
Yan Chen
Keyword(s):  
History Matching ◽  
Historical Data ◽  
Total Error ◽  
Model Error ◽  
Complex Nature ◽  
Model Parameters ◽  
Observation Error ◽  
Model Errors ◽  
Ensemble Smoother ◽  
Iterative Ensemble Smoother

Summary Owing to the complex nature of hydrocarbon reservoirs, the numerical model constructed by geoscientists is always a simplified version of reality: for example, it might lack resolution from discretization and lack accuracy in modeling some physical processes. This flaw in the model that causes mismatch between actual observations and simulated data when “perfect” model parameters are used as model inputs is known as “model error”. Even in a situation when the model is a perfect representation of reality, the inputs to the model are never completely known. During a typical model calibration procedure, only a subset of model inputs is adjusted to improve the agreement between model responses and historical data. The remaining model inputs that are not calibrated and are likely fixed at incorrect values result in model error in a similar manner as the imperfect model scenario. Assimilation of data without accounting for model error can result in the incorrect adjustment to model parameters, the underestimation of prediction uncertainties, and bias in forecasts. In this paper, we investigate the benefit of recognizing and accounting for model error when an iterative ensemble smoother is used to assimilate production data. The correlated “total error” (a combination of model error and observation error) is estimated from the data residual after a standard history-matching using the Levenberg-Marquardt form of iterative ensemble smoother (LM-EnRML). This total error is then used in further data assimilations to improve the estimation of model parameters and quantification of prediction uncertainty. We first illustrate the method using a synthetic 2D five-spot example, where some model errors are deliberately introduced, and the results are closely examined against the known “true” model. Then, the Norne field case is used to further evaluate the method. The Norne model has previously been history-matched using the LM-EnRML (Chen and Oliver 2014), where cell-by-cell properties (permeability, porosity, net-to-gross, vertical transmissibility) and parameters related to fault transmissibility, depths of water/oil contacts, and relative permeability function are adjusted to honor historical data. In this previous study, the authors highlighted the importance of including large amounts of model parameters, the proper use of localization, and heuristic adjustment of data noise to account for modeling error. In this paper, we improve the last aspect by quantitatively estimating model error using residual analysis.

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