Greenhouse gas network design using backward Lagrangian particle  dispersion modelling − Part 1: Methodology and Australian test case

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes, comprising contributions from the biosphere and fossil fuel combustion, and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly timescale. Prior uncertainties are derived on a weekly timescale for biosphere fluxes and fossil fuel emissions from high-resolution model runs using the Community Atmosphere Biosphere Land Exchange (CABLE) model and the Fossil Fuel Data Assimilation System (FFDAS) respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground-based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimisation scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50%, we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

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Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 1: Methodology and Australian test case

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-14-7557-2014 ◽

2014 ◽

Vol 14 (6) ◽

pp. 7557-7595 ◽

Cited By ~ 3

Author(s):

T. Ziehn ◽

A. Nickless ◽

P. J. Rayner ◽

R. M. Law ◽

G. Roff ◽

...

Keyword(s):

Network Design ◽

Time Scale ◽

Fossil Fuel ◽

Dispersion Model ◽

Surface Flux ◽

Particle Dispersion ◽

Surface Fluxes ◽

Test Case ◽

Lagrangian Particle ◽

Australian Continent

Abstract. This paper describes the generation of optimal atmospheric measurement networks for determining carbon dioxide fluxes over Australia using inverse methods. A Lagrangian particle dispersion model is used in reverse mode together with a Bayesian inverse modelling framework to calculate the relationship between weekly surface fluxes and hourly concentration observations for the Australian continent. Meteorological driving fields are provided by the regional version of the Australian Community Climate and Earth System Simulator (ACCESS) at 12 km resolution at an hourly time scale. Prior uncertainties are derived on a weekly time scale for biosphere fluxes and fossil fuel emissions from high resolution BIOS2 model runs and from the Fossil Fuel Data Assimilation System (FFDAS), respectively. The influence from outside the modelled domain is investigated, but proves to be negligible for the network design. Existing ground based measurement stations in Australia are assessed in terms of their ability to constrain local flux estimates from the land. We find that the six stations that are currently operational are already able to reduce the uncertainties on surface flux estimates by about 30%. A candidate list of 59 stations is generated based on logistic constraints and an incremental optimization scheme is used to extend the network of existing stations. In order to achieve an uncertainty reduction of about 50% we need to double the number of measurement stations in Australia. Assuming equal data uncertainties for all sites, new stations would be mainly located in the northern and eastern part of the continent.

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Ground-based CO2 monitoring network design over India to constrain the regional terrestrial fluxes

10.5194/egusphere-egu2020-7881 ◽

2020 ◽

Author(s):

Nalini Krishnankutty ◽

Sijikumar Sivaraman ◽

Vinu Valsala ◽

Yogesh Tiwari ◽

Radhika Ramachandran

Keyword(s):

Dispersion Model ◽

Monitoring Network ◽

Surface Flux ◽

Northeast India ◽

Particle Dispersion ◽

Surface Fluxes ◽

Monitoring Network Design ◽

North East ◽

Observational Uncertainty ◽

Flux Estimation

The present study aims to design an optimal CO2 monitoring network over India to better constrain the Indian terrestrial surface fluxes using Lagrangian Particle Dispersion Model FLEXPART and Bayesian inversion methods. Prior and posterior cost functions are calculated using potential emission sensitivity from FLEXPART, prior flux uncertainties from CASA-GFED biosphere fluxes and CDIAC fossil fuel fluxes, and assumed uniform observational uncertainty of 2 ppm. A total of 73 regular grid cells are identified over the Indian land mass in 2&#176;x2&#176; latitude by longitude resolution assuming each cell can hold a potential site. Further, using incremental optimization methodology, the effectiveness of CO2 observations from these locations to reduce the Indian terrestrial flux uncertainty is quantified. The study is carried out in three parts. Firstly, we evaluated the existing stations over India in terms of reduction in uncertainty brought out by them in the surface flux estimation over the Indian landmass. This provides a unique opportunity for the representative stations to restart the observational programs based on their role in the flux estimation. In second part, we devised a methodology to design an extended network by adding a few more potential stations to the existing stations. Thirdly, we identified a completely new set of optimal stations for measuring atmospheric CO2 over India, which do not have any liabilities of pre-existing stations. The study depicts that the existing stations could bring down the uncertainty in the range of 18% to 36%. Among the existing stations, Kharagpur, Sagar, Shadnagar, Kodaikanal and Pondicherry are the best stations, which are indeed adding value to the CO2 flux inversions by reducing the uncertainty in the range of 4% to 13%. Addition of five new stations to the base network formed an extended network, which could reduce the uncertainty by an additional 15% for all the seasons reaching up to 45%. The new stations are mainly located over the east and north-east India with few exceptions during post-monsoon where stations are identified over the west and south India as well. The study identified 12 stations for each season and formed a &#8216;new network&#8217; that could achieve the equivalent uncertainty reduction as compared with the 14 stations in the &#8216;extended network&#8217;. From this, an &#8216;optimal network&#8217; and the best network consisting of 17 stations were identified that could best represent flux scenario and transport over India in all the four seasons. In northeast India, flux uncertainty is quite large, also the prevailing westerly wind in most parts of the year contributes to the surface CO2 signature of India to that location, demanding requirement of CO2 observations throughout the year. The study highlights a major zone of CO2 &#8216;observational void&#8217; that exists in potential locations near east and northeast parts of India. Immediate requirement of CO2 monitoring initiative in these areas is highly recommended.

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Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-2051-2015 ◽

2015 ◽

Vol 15 (4) ◽

pp. 2051-2069 ◽

Cited By ~ 16

Author(s):

A. Nickless ◽

T. Ziehn ◽

P.J. Rayner ◽

R.J. Scholes ◽

F. Engelbrecht

Keyword(s):

Network Design ◽

Covariance Matrix ◽

South African ◽

Spatial Resolution ◽

Particle Dispersion ◽

Test Case ◽

Lagrangian Particle ◽

Error Covariance ◽

Optimal Network Design ◽

Optimal Network

Abstract. This is the second part of a two-part paper considering a measurement network design based on a stochastic Lagrangian particle dispersion model (LPDM) developed by Marek Uliasz, in this case for South Africa. A sensitivity analysis was performed for different specifications of the network design parameters which were applied to this South African test case. The LPDM, which can be used to derive the sensitivity matrix used in an atmospheric inversion, was run for each candidate station for the months of July (representative of the Southern Hemisphere winter) and January (summer). The network optimisation procedure was carried out under a standard set of conditions, similar to those applied to the Australian test case in Part 1, for both months and for the combined 2 months, using the incremental optimisation (IO) routine. The optimal network design setup was subtly changed, one parameter at a time, and the optimisation routine was re-run under each set of modified conditions and compared to the original optimal network design. The assessment of the similarity between network solutions showed that changing the height of the surface grid cells, including an uncertainty estimate for the ocean fluxes, or increasing the night-time observation error uncertainty did not result in any significant changes in the positioning of the stations relative to the standard design. However, changing the prior flux error covariance matrix, or increasing the spatial resolution, did. Large aggregation errors were calculated for a number of candidate measurement sites using the resolution of the standard network design. Spatial resolution of the prior fluxes should be kept as close to the resolution of the transport model as the computing system can manage, to mitigate the exclusion of sites which could potentially be beneficial to the network. Including a generic correlation structure in the prior flux error covariance matrix led to pronounced changes in the network solution. The genetic algorithm (GA) was able to find a marginally better solution than the IO procedure, increasing uncertainty reduction by 0.3 %, but still included the most influential stations from the standard network design. In addition, the computational cost of the GA compared to IO was much higher. Overall the results suggest that a good improvement in knowledge of South African fluxes is available from a feasible atmospheric network, and that the general features of this network are invariable under several reasonable choices in a network design study.

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Inverse modeling of halocarbons: sensitivity to the baseline definition

10.5194/egusphere-egu21-4029 ◽

2021 ◽

Author(s):

Martin Vojta ◽

Rona Thompson ◽

Christine Groot Zwaaftink ◽

Andreas Stohl

Keyword(s):

Atmospheric Chemistry ◽

Inverse Modeling ◽

Sulfur Hexafluoride ◽

Dispersion Model ◽

Particle Dispersion ◽

Surface Fluxes ◽

Lagrangian Particle ◽

Global Fields ◽

Mixing Ratios ◽

Model Based

The identification of the baseline is an important task in inverse modeling of greenhouse gases, as it represents the influence of atmospheric chemistry and transport and surface fluxes from outside the inversion domain, or flux contributions prior to the length of the backward calculation for Lagrangian models. When modeling halocarbons, observation-based approaches are often used to calculate the baseline, although model-based approaches are an alternative. Model-based methods need global unbiased fields of mixing ratios of the observed species, which are not always easy to get and which need to be interfaced with the model used for the inversion. To find the best way to identify the baseline and to investigate whether the usage of observation-based approaches is suitable for inverse modeling of halocarbons, we use and analyze a model-based and two frequently used observation-based methods to determine the baseline and investigate their influence on inversion results. The model-based method couples global fields of mixing ratios with backwards-trajectories at their point of termination. We simulate those global fields with a Lagrangian particle dispersion model, FLEXPART_CTM, that uses a nudging routine to relax model data to observed values. The second method under investigation is the robust estimation of baseline signal (REBS) method, that is purely based on statistical analysis of observations. The third analyzed method is also primarily observation-based, but uses model information to subtract prior simulated mixing ratios from selected observations. We apply those three methods to sulfur hexafluoride (SF6) and use the Bayesian inversion framework FLEXINVERT for the inverse modeling and the Lagrangian particle dispersion model FLEXPART to calculate the source-receptor-relationship used in the inversion.

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