Evaluating the methane budget in Europe using inverse modelling
The Kalman filter is used as a data-assimilation technique to calculate methane concentrations and emissions in Europe. The filter combines the results of model calculations and measurements to achieve an optimal estimate using knowledge of the system or model noise and the measuring noise.
The results of the Eulerian 3 D Euros model, which calculates emissions, transport and concentrations of methane all over in Europe, and the results of five methane measuring stations in the Netherlands and one in Ireland are used simultaneously in the filter. The filter is very time consuming on a computer and therefore Kriging and a Reduced Rank Square Root (RRSQRT) algorithm are implemented to reduce calculation time.
The results show that it is very important to carefully select the optimal parameters in the model: the system and measurement noise, the Kriging interpolation factors and the number of significant Eigenvalues in the RRSQRT algorithm to get reliable outcomes. Systematic differences between measurements and model calculations are caused by errors in the emission input. A fixed point smoother has been implemented - in combination with the filter - to be able to get an optimal estimate of methane emissions in Europe. The working of the smoother and the influence of the smoother parameters on the model results have been analysed.