Estimating ammonia emissions using an adjoint-free 4D-Var approach

Estimating ammonia emissions using an adjoint-free 4D-Var approach: Using data assimilation to combine the LOTOS-EUROS model with LML and synthetic IRS satellite observations

Leegwater, Marjolein (TU Delft Electrical Engineering, Mathematics and Computer Science)

Heemink, A.W. (mentor)
Segers, A.J, (mentor)
Budko, N.V. (graduation committee)

Delft University of Technology

Applied Mathematics | Computational Science and Engineering

2021-07-28

Ammonia (NH3) is an important chemical compound in the nitrogen cycle. Ammonia is an essential nutrient and an important part of fertilizer, which in soil leads to increased growth of crops. However, the current excess of NH3 emissions is a hazard to environmental and human health. While ammonia
emissions need to be reduced, the emission estimates are still highly uncertain.
In this study, a method is developed to combine the chemical transport model LOTOS-EUROS with measured ammonia concentrations to improve the ammonia emission estimates in the Netherlands and the surrounding regions. The measurements used are generated by the miniDOAS instruments on LML stations and the IRS instrument on board the future MTG-S satellite. The proposed method is an adjoint-free 4DVar method. The 4DVar method aims to retrieve the emission parameters for which the LOTOS-EUROS model determines NH3 concentrations that resemble the measurements while keeping the emissions fairly similar to the original inventories. A linear approximate model has been developed, which uses the near-linearity of the NH3 concentrations in terms
of the NH3 emissions. When using the approximate model, the 4DVar
method can be solved without using an adjoint model, making the method adjoint-free. Subsequently, the 4DVar cost function is rewritten to allow the emission parameters to have a lognormal prior distribution. A maximum likelihood approach is developed to estimate the parameters of both the prior distribution of the emissions and the likelihood of the measured observations. Last, a preconditioner in reduced space has been considered, to estimate emissions on a fine spatial resolution, while keeping the computational cost
feasible. This preconditioner uses the property that the emission parameters are correlated in space. First, the methodology has been tested in an identical twin experiment where the emissions vary only in time and strength, using the LML observations. It was concluded that the methodology worked well
for short periods (less than 30 days), but the results were dominated by observational noise. When using the real observations in the 4DVar
method, the results seem unrealistic Second, the method has been tested in an identical twin experiment where emissions vary in space, as well as in time and strength. Here, synthetic IRS observations of the future MTG-S satellite are
used. The optimized emissions did resemble the true emissions of the twin experiment. Observational noise appeared to no longer be an issue. However, the results were not perfect. The regions with the highest emission increase appeared to be underestimated, low emission areas appeared to have large
relative estimation errors, and estimates for coastal regions seemed to be incorrect. Hence, on a local scale, the emission estimates can be imperfect, but overall the adjoint-free 4DVar method does greatly improve the emission inventories.

Ammonia
4D-Var
Emission
Inverse modeling

http://resolver.tudelft.nl/uuid:9e08c5d8-47f2-4643-b688-25313735425f

Student theses

master thesis

© 2021 Marjolein Leegwater