Wet deposition is an important removal process in the pollution budget of the atmosphere. Wet deposition processes refer to the uptake of gaseous pollutants and aerosol particles into cloud water and precipitation, and its subsequent transfer to the ground. Next to wet deposition measurements wet deposition modelling is fundamental for policies concerning pollution reduction because it allows to calculate future emission scenarios and thus can be used for action plans.
For modelling wet deposition the description of clouds and the effective precipitation are of high importance in order to obtain a correct chemical air pollution mass balance.
The objective of this project is to improve the description of wet deposition within the Chemistry Transport Model REM-CALGRID (RCG). Former validations have shown that RCG underestimated the total amount of wet deposition of sulphur and nitrogen, although reproducing correctly the spatial distribution. Wet deposition was simulated using simple constant scavenging coefficients for gases and particles for below-cloud scavenging, only. A new scheme for wet deposition was implemented that includes a cloud water content dependent in-cloud scavenging and an extended below-cloud scavenging.
The meteorological input, especially cloud parameters and the precipitation field, are essential for the description of wet deposition processes. Prognostic weather prediction models simulate next to the precipitation field the atmospheric field of cloud water content which is needed for a detailed description of the pollutants' wet chemistry. At the same time prognostic weather prediction models often fail to describe the local precipitation and cloud fields in space and time. They over- or underestimate precipitation rates in different regions. Diagnostic analysis systems on the other hand are based on a statistical interpolation of meteorological observations. That leads to very reliable precipitation fields given that the monitoring network is adequate concerning temporal and spatial resolution and quality. At the same time the diagnostic scheme implies the limitation that only operationally measured variables can be interpolated to form the diagnostic model-output. Additional variables must be derived using the available interpolated fields.
Thus one part of the investigation will be to assess advantages and disadvantages in using a diagnostic or a prognostic meteorological driver for wet deposition modelling. The diagnostic meteorological driver used in this study is TRAMPER. Within the Optimal Interpolation scheme 3D-clouds are generated by using synoptic observations from WMO and cloud parameter statistics. As a prognostic meteorological driver the COSMO- EU model of the German Weather Service is applied. The resultant wet deposition outputfields are compared to each other and to wet deposition measurements.