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

Nickless, AZiehn, TRayner, PJScholes, RJEngelbrecht, F2014-07-252014-07-252014-05Nickless, A, Ziehn, T, Rayner, P.J, Scholes, R.J and Engelbrecht, F. 2014. Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case. Atmospheric Chemistry and Physics Discussions, vol. 14, pp 11301-113421680-7375http://www.atmos-chem-phys-discuss.net/14/11301/2014/acpd-14-11301-2014.htmlhttp://hdl.handle.net/10204/7525Copyright: 2014 European Geosciences Union (EGU). This is an OA journal. Published in Atmospheric Chemistry and Physics Discussions, vol. 14, pp 11301-11342This is the second part of a two-part paper considering network design based on a Lagrangian stochastic particle dispersion model (LPDM), aimed at reducing the uncertainty of the flux estimates achievable for the region of interest by the continuous observation of atmospheric CO(sub2) concentrations at fixed monitoring stations. The LPDM model, which can be used to derive the sensitivity matrix used in an inversion, was run for each potential site for the months of July (representative of the Southern Hemisphere Winter) and January (Summer). The magnitude of the boundary contributions to each potential observation site was tested to determine its inclusion in the network design, but found to be minimal. Through the use of the Bayesian inverse modelling technique, the sensitivity matrix, together with the prior estimates for the covariance matrices of the observations and surface fluxes were used to calculate the posterior covariance matrix of the estimated fluxes, used for the calculation of the cost function of the optimisation procedure. The optimisation procedure was carried out for South Africa 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 two months. The conditions were subtly changed, one at a time, and the optimisation routine re-run under each set of modified conditions, and compared to the original optimal network design.enInverse modellingOptimal network designBayesian synthesis inversionLagrangian stochastic particle dispersion modelLPDMBayesian inverse modelling techniqueGreenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test caseArticleNickless, A., Ziehn, T., Rayner, P., Scholes, R., & Engelbrecht, F. (2014). Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case. http://hdl.handle.net/10204/7525Nickless, A, T Ziehn, PJ Rayner, RJ Scholes, and F Engelbrecht "Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case." (2014) http://hdl.handle.net/10204/7525Nickless A, Ziehn T, Rayner P, Scholes R, Engelbrecht F. Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case. 2014; http://hdl.handle.net/10204/7525.TY - Article AU - Nickless, A AU - Ziehn, T AU - Rayner, PJ AU - Scholes, RJ AU - Engelbrecht, F AB - This is the second part of a two-part paper considering network design based on a Lagrangian stochastic particle dispersion model (LPDM), aimed at reducing the uncertainty of the flux estimates achievable for the region of interest by the continuous observation of atmospheric CO(sub2) concentrations at fixed monitoring stations. The LPDM model, which can be used to derive the sensitivity matrix used in an inversion, was run for each potential site for the months of July (representative of the Southern Hemisphere Winter) and January (Summer). The magnitude of the boundary contributions to each potential observation site was tested to determine its inclusion in the network design, but found to be minimal. Through the use of the Bayesian inverse modelling technique, the sensitivity matrix, together with the prior estimates for the covariance matrices of the observations and surface fluxes were used to calculate the posterior covariance matrix of the estimated fluxes, used for the calculation of the cost function of the optimisation procedure. The optimisation procedure was carried out for South Africa 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 two months. The conditions were subtly changed, one at a time, and the optimisation routine re-run under each set of modified conditions, and compared to the original optimal network design. DA - 2014-05 DB - ResearchSpace DP - CSIR KW - Inverse modelling KW - Optimal network design KW - Bayesian synthesis inversion KW - Lagrangian stochastic particle dispersion model KW - LPDM KW - Bayesian inverse modelling technique LK - https://researchspace.csir.co.za PY - 2014 SM - 1680-7375 T1 - Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case TI - Greenhouse gas network design using backward Lagrangian particle dispersion modelling – Part 2: Sensitivity analyses and South African test case UR - http://hdl.handle.net/10204/7525 ER -