scholarly journals Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric transport model (A-GELCA v1.0): development and validation

2015 ◽  
Vol 8 (7) ◽  
pp. 5983-6019
Author(s):  
D. A. Belikov ◽  
S. Maksyutov ◽  
A. Yaremchuk ◽  
A. Ganshin ◽  
T. Kaminski ◽  
...  
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Automatic Differentiation ◽  
Transport Model ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Coupled Model ◽  
Particle Dispersion ◽  
Limiting Factors ◽  
Eulerian Model

Abstract. We present the development of the Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian plume diffusion model (LPDM). The tangent and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre- and post-processing aimed at improving the performance of the computing, including MPI (Message Passing Interface). As results, the adjoint of Eulerian model is discrete. Construction of the adjoint of the Lagrangian component did not require any code modification, as LPDMs are able to track a significant number of particles back in time and thereby calculate the sensitivity of observations to the neighboring emissions areas. Eulerian and Lagrangian adjoint components were coupled at the time boundary in the global domain.The results are verified using a series of test experiments. The forward simulation shown the coupled model is effective in reproducing the seasonal cycle and short-term variability of CO2 even in the case of multiple limiting factors, such as high uncertainty of fluxes and the low resolution of the Eulerian model. The adjoint model demonstrates the high accuracy compared to direct forward sensitivity calculations and fast performance. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation.

scholarly journals Adjoint of the global Eulerian–Lagrangian coupled atmospheric transport model (A-GELCA v1.0): development and validation

2016 ◽  
Vol 9 (2) ◽  
pp. 749-764 ◽  
Author(s):  
Dmitry A. Belikov ◽  
Shamil Maksyutov ◽  
Alexey Yaremchuk ◽  
Alexander Ganshin ◽  
Thomas Kaminski ◽  
...  
Keyword(s):  
Message Passing ◽  
Message Passing Interface ◽  
Automatic Differentiation ◽  
Transport Model ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Coupled Model ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Eulerian Model

Abstract. We present the development of the Adjoint of the Global Eulerian–Lagrangian Coupled Atmospheric (A-GELCA) model that consists of the National Institute for Environmental Studies (NIES) model as an Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward tangent linear and adjoint components of the Eulerian model were constructed directly from the original NIES TM code using an automatic differentiation tool known as TAF (Transformation of Algorithms in Fortran; http://www.FastOpt.com), with additional manual pre- and post-processing aimed at improving transparency and clarity of the code and optimizing the performance of the computing, including MPI (Message Passing Interface). The Lagrangian component did not require any code modification, as LPDMs are self-adjoint and track a significant number of particles backward in time in order to calculate the sensitivity of the observations to the neighboring emission areas. The constructed Eulerian adjoint was coupled with the Lagrangian component at a time boundary in the global domain. The simulations presented in this work were performed using the A-GELCA model in forward and adjoint modes. The forward simulation shows that the coupled model improves reproduction of the seasonal cycle and short-term variability of CO2. Mean bias and standard deviation for five of the six Siberian sites considered decrease roughly by 1 ppm when using the coupled model. The adjoint of the Eulerian model was shown, through several numerical tests, to be very accurate (within machine epsilon with mismatch around to ±6 e−14) compared to direct forward sensitivity calculations. The developed adjoint of the coupled model combines the flux conservation and stability of an Eulerian discrete adjoint formulation with the flexibility, accuracy, and high resolution of a Lagrangian backward trajectory formulation. A-GELCA will be incorporated into a variational inversion system designed to optimize surface fluxes of greenhouse gases.

scholarly journals Simulation of variability in atmospheric carbon dioxide using a global coupled Eulerian – Lagrangian transport model

2011 ◽  
Vol 4 (2) ◽  
pp. 317-324 ◽  
Author(s):  
Y. Koyama ◽  
S. Maksyutov ◽  
H. Mukai ◽  
K. Thoning ◽  
P. Tans
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Dispersion Model ◽  
Regional Scale ◽  
Coupled Model ◽  
Particle Dispersion ◽  
Atmospheric Composition ◽  
Tracer Transport ◽  
Eulerian Model ◽  
Co2 Concentrations

Abstract. This study assesses the advantages of using a coupled atmospheric-tracer transport model, comprising a global Eulerian model and a global Lagrangian particle dispersion model, to improve the reproducibility of tracer-gas variations affected by the near-field surface emissions and transport around observation sites. The ability to resolve variability in atmospheric composition on an hourly time-scale and a spatial scale of several kilometers would be beneficial for analyzing data from continuous ground-based monitoring and from upcoming space-based observations. The coupled model yields an increase in the horizontal resolution of transport and fluxes, and has been tested in regional-scale studies of atmospheric chemistry. By applying the Lagrangian component to the global domain, we extend this approach to the global scale, thereby enabling computationally efficient global inverse modeling and data assimilation. To validate the coupled model, we compare model-simulated CO2 concentrations with continuous observations at three sites: two operated by the National Oceanic and Atmospheric Administration, USA, and one operated by the National Institute for Environmental Studies, Japan. As the goal of this study is limited to introducing the new modeling approach, we selected a transport simulation at these three sites to demonstrate how the model may perform at various geographical areas. The coupled model provides improved agreement between modeled and observed CO2 concentrations in comparison to the Eulerian model. In an area where variability in CO2 concentration is dominated by a fossil fuel signal, the correlation coefficient between modeled and observed concentrations increases by between 0.05 to 0.1 from the original values of 0.5–0.6 achieved with the Eulerian model.

scholarly journals Pollutant transport in coastal areas with and without background wind

1997 ◽  
Vol 15 (4) ◽  
pp. 476-486 ◽  
Author(s):  
J. Camps ◽  
J. Massons ◽  
M. R. Soler ◽  
E. C. Nickerson
Keyword(s):  
Large Scale ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Coastal Region ◽  
Coupled Model ◽  
Pollutant Dispersion ◽  
Particle Dispersion ◽  
Background Wind ◽  
Meteorological Model ◽  
Pollutant Concentrations

Abstract. A three-dimensional meteorological model and a Lagrangian particle dispersion model are used to study the effects of a uniform large-scale wind on the dispersion of a non-reactive pollutant in a coastal region with complex terrain. Simulations are carried out both with and without a background wind. A comparison between model results and measured data (wind and pollutant concentrations) indicates that the coupled model system provides a useful mechanism for analyzing pollutant dispersion in coastal regions.

scholarly journals Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

2016 ◽  
Author(s):  
D. A. Belikov ◽  
S. Maksyutov ◽  
A. Ganshin ◽  
R. Zhuravlev ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Particle Dispersion ◽  
Bias Reduction ◽  
Total Carbon ◽  
Short Term

Abstract. The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions.

scholarly journals Simulation of atmospheric carbon dioxide variability with a global coupled Eulerian-Lagrangian transport model

2010 ◽  
Vol 3 (4) ◽  
pp. 2051-2070
Author(s):  
Y. Koyama ◽  
S. Maksyutov ◽  
H. Mukai ◽  
K. Thoning ◽  
P. Tans
Keyword(s):  
Atmospheric Chemistry ◽  
Transport Model ◽  
Regional Scale ◽  
Coupled Model ◽  
Particle Dispersion ◽  
Atmospheric Composition ◽  
Small Subset ◽  
Tracer Transport ◽  
Eulerian Model ◽  
Co2 Concentrations

Abstract. This study assesses the advantages of using a coupled atmospheric-tracer transport model, comprising a global Eulerian model and a global Lagrangian particle dispersion model, for reproducibility of tracer gas variation affected by near field around observation sites. The ability to resolve variability in atmospheric composition on an hourly time scale and a spatial scale of several kilometers would be beneficial for analyzing data from continuous ground-based monitoring and upcoming space-based observations. The coupled model yields increased horizontal resolution of transport and fluxes, and has been tested in regional-scale studies of atmospheric chemistry. By applying the Lagrangian component to the global domain, we extend this approach to the global scale, thereby enabling global inverse modeling and data assimilation. To validate the coupled model, we compare model-simulated CO2 concentrations with continuous observations at two sites operated by the National Oceanic and Atmospheric Administration, USA and one site operated by National Institute for Environmental Studies, Japan. As the purpose of this study is limited to demonstration of the new modeling approach, we select a small subset of 3 sites to highlight use of the model in various geographical areas. To explore the capability of the coupled model in simulating synoptic-scale meteorological phenomena, we calculate the correlation coefficients and variance ratios between deseasonalized model-simulated and observed CO2 concentrations. Compared with the Eulerian model alone, the coupled model yields improved agreement between modeled and observed CO2 concentrations.

scholarly journals Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

2017 ◽  
Vol 17 (1) ◽  
pp. 143-157 ◽  
Author(s):  
Dmitry A. Belikov ◽  
Shamil Maksyutov ◽  
Alexander Ganshin ◽  
Ruslan Zhuravlev ◽  
Nicholas M. Deutscher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Particle Dispersion ◽  
Bias Reduction ◽  
Total Carbon ◽  
Short Term

Abstract. The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

scholarly journals Inversion of long-lived trace gas emissions using combined Eulerian and Lagrangian chemical transport models

2011 ◽  
Vol 11 (18) ◽  
pp. 9887-9898 ◽  
Author(s):  
M. Rigby ◽  
A. J. Manning ◽  
R. G. Prinn
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Transport Model ◽  
Initial Conditions ◽  
Dispersion Model ◽  
Chemical Transport ◽  
Particle Dispersion ◽  
Global Network ◽  
Lagrangian Model ◽  
Chemical Transport Model

Abstract. We present a method for estimating emissions of long-lived trace gases from a sparse global network of high-frequency observatories, using both a global Eulerian chemical transport model and Lagrangian particle dispersion model. Emissions are derived in a single step after determining sensitivities of the observations to initial conditions, the high-resolution emissions field close to observation points, and larger regions further from the measurements. This method has the several advantages over inversions using one type of model alone, in that: high-resolution simulations can be carried out in limited domains close to the measurement sites, with lower resolution being used further from them; the influence of errors due to aggregation of emissions close to the measurement sites can be minimized; assumptions about boundary conditions to the Lagrangian model do not need to be made, since the entire emissions field is estimated; any combination of appropriate models can be used, with no code modification. Because the sensitivity to the entire emissions field is derived, the estimation can be carried out using traditional statistical methods without the need for multiple steps in the inversion. We demonstrate the utility of this approach by determining global SF6 emissions using measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) between 2007 and 2009. The global total and large-scale patterns of the derived emissions agree well with previous studies, whilst allowing emissions to be determined at higher resolution than has previously been possible, and improving the agreement between the modeled and observed mole fractions at some sites.

scholarly journals Boundary layer and free-tropospheric dimethyl sulfide in the Arctic spring and summer

2017 ◽  
Vol 17 (14) ◽  
pp. 8757-8770 ◽  
Author(s):  
Roghayeh Ghahremaninezhad ◽  
Ann-Lise Norman ◽  
Betty Croft ◽  
Randall V. Martin ◽  
Jeffrey R. Pierce ◽  
...  
Keyword(s):  
Dimethyl Sulfide ◽  
Transport Model ◽  
Dispersion Model ◽  
Open Water ◽  
Particle Dispersion ◽  
The Arctic ◽  
Chemical Transport Model ◽  
Model Simulations ◽  
Baffin Bay ◽  
Mixing Ratios

Abstract. Vertical distributions of atmospheric dimethyl sulfide (DMS(g)) were sampled aboard the research aircraft Polar 6 near Lancaster Sound, Nunavut, Canada, in July 2014 and on pan-Arctic flights in April 2015 that started from Longyearbyen, Spitzbergen, and passed through Alert and Eureka, Nunavut, and Inuvik, Northwest Territories. Larger mean DMS(g) mixing ratios were present during April 2015 (campaign mean of 116  ±  8 pptv) compared to July 2014 (campaign mean of 20  ±  6 pptv). During July 2014, the largest mixing ratios were found near the surface over the ice edge and open water. DMS(g) mixing ratios decreased with altitude up to about 3 km. During April 2015, profiles of DMS(g) were more uniform with height and some profiles showed an increase with altitude. DMS reached as high as 100 pptv near 2500 m. Relative to the observation averages, GEOS-Chem (www.geos-chem.org) chemical transport model simulations were higher during July and lower during April. Based on the simulations, more than 90 % of the July DMS(g) below 2 km and more than 90 % of the April DMS(g) originated from Arctic seawater (north of 66° N). During April, 60 % of the DMS(g), between 500 and 3000 m originated from Arctic seawater. During July 2014, FLEXPART (FLEXible PARTicle dispersion model) simulations locate the sampled air mass over Baffin Bay and the Canadian Arctic Archipelago 4 days back from the observations. During April 2015, the locations of the air masses 4 days back from sampling were varied: Baffin Bay/Canadian Archipelago, the Arctic Ocean, Greenland and the Pacific Ocean. Our results highlight the role of open water below the flight as the source of DMS(g) during July 2014 and the influence of long-range transport (LRT) of DMS(g) from further afield in the Arctic above 2500 m during April 2015.

CIP and Parallel Computing Based Numerical Solutions of 3-D Slamming Problems

2015 ◽  
Author(s):  
Peng Wen ◽  
Wei Qiu
Keyword(s):  
Parallel Computing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Numerical Solutions ◽  
Three Dimensional ◽  
Simulation Method ◽  
Water Entry ◽  
Computational Domain ◽  
Cip Method ◽  
Constrained Interpolation

This paper presents the further development of numerical simulation method to solve 3-D highly non-linear slamming problems using parallel computing algorithms. The water entry problems are treated as multi-phase problems (solid, water and air) and governed by the Navier-Stokes (N-S) equations. They are solved by the three-dimensional constrained interpolation profile (CIP) method. The interfaces between different phases are captured using density functions. In the computation, the 3-D CIP method is employed for the advection phase of the N-S equations and a pressure-based algorithm is applied for the non-advection phase. The bi-conjugate gradient stabilized method (BiCGSTAB) is utilized to solve the linear equation systems. A Message Passing Interface (MPI) parallel computing scheme was implemented in the computations. For the parallel computations, the three-dimensional Cartesian decomposition of the computational domain was used. The speed-up performance of various decomposition schemes were studied. Validation studies were carried out for the water entry of a 3-D wedge and a 3-D ship section with prescribed velocities. The computed slamming force, pressure distribution and free-surface elevations are compared with experimental results and numerical results by other methods.

scholarly journals Regional CO<sub>2</sub> inversions with LUMIA, the Lund University Modular Inversion Algorithm, v1.0

2021 ◽  
Vol 14 (6) ◽  
pp. 3383-3406
Author(s):  
Guillaume Monteil ◽  
Marko Scholze
Keyword(s):  
Inverse Modeling ◽  
Transport Model ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Model Errors ◽  
Inversion Algorithm ◽  
In Situ Data ◽  
Increasing Demand ◽  
Modular Inversion

Abstract. Atmospheric inversions are used to derive constraints on the net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. The resolution and accuracy that the fluxes can be estimated with depends, among other factors, on the quality and density of the observational coverage, on the precision and accuracy of the transport model used by the inversion to relate fluxes to observations, and on the adaptation of the statistical approach to the problem studied. In recent years, there has been an increasing demand from stakeholders for inversions at higher spatial resolution (country scale), in particular in the framework of the Paris agreement. This step up in resolution is in theory enabled by the growing availability of observations from surface in situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the bottom-up modeling community (vegetation models, fossil fuel emission inventories, etc.). However, it calls for new developments in the inverse models: diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency. In this context, we developed LUMIA, the Lund University Modular Inversion Algorithm. LUMIA is a Python library for inverse modeling built around the central idea of modularity: it aims to be a platform that enables users to construct and experiment with new inverse modeling setups while remaining easy to use and maintain. It is in particular designed to be transport-model-agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. We have constructed a first regional inversion setup using the LUMIA framework to conduct regional CO2 inversions in Europe using in situ data from surface and tall-tower observation sites. The inversions rely on a new offline coupling between the regional high-resolution FLEXPART Lagrangian particle dispersion model and the global coarse-resolution TM5 transport model. This test setup is intended both as a demonstration and as a reference for comparison with future LUMIA developments. The aims of this paper are to present the LUMIA framework (motivations for building it, development principles and future prospects) and to describe and test this first implementation of regional CO2 inversions in LUMIA.

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