Adjoint model enhanced plume reconstruction from tomographic remote sensing measurements

A new mathematical optimization method is presented for reconstructing pollution plume concentrations from tomographic remote sensing measurements on neighborhood scales (about 1 km × 1 km) using Differential Optical Absorption Spectroscopy (DOAS). The new method, called CAT–4Dvar, combines Computer Aided Tomography (CAT) and 4D variational (4Dvar) data assimilation. The objective of the method is to produce accurate reconstructions compared to the Algebraic Reconstruction Technique (ART) and other non-variational methods with only a small number of DOAS telescopes. A forward and adjoint 3D grid dispersion model was developed based on advection and diffusion solvers commonly used in air quality modeling. The adjoint model optimizes the model emissions and horizontal diffusion coefficient based on the difference between tomographic DOAS observations and ray path-integrated concentrations predicted by the forward model. It also updates the corresponding error covariances based on the Hessian of the cost function. An enhanced reconstruction is obtained from the forward model with optimized parameter values. In a synthetic experiment involving two hypothetical DOAS instruments, the CAT–4Dvar method yielded excellent results compared to ART, reducing the overall nearness index from 57% to 11%.

► A new method is presented for reconstructing plumes via tomographic remote sensing.
► The method uses a forward and adjoint dispersion model to optimize model parameters.
► The method produces accurate reconstructions with only two remote sensing telescopes.
► The new method compares favorably with the Algebraic Reconstruction Technique.