Author:

Abstract:

Clouds have a significant effect on air quality, among others by affecting photolysis (through their effect on shortwave radiation), and because of their role in chemical reactions that occur at the interface of cloud droplets. Yet, atmospheric models experience great difficulty in simulating cloud-related variables correctly, which regularly leads to poorly simulated concentrations of atmospheric pollutants such as tropospheric ozone and fine particulate matter. Considering this, the main goal of the proposed research is to improve the simulation of cloud in atmospheric models by exploiting satellite data in a data assimilation procedure,and to demonstrate the impact of an improved representation of cloud onsimulated air quality at the regional scale. The methodology will be as follows. In afirst step, relevant satellite data will be evaluated and selected. Criteria for selection will be based, among others, on the type of geophysical parameter the instrument can provide (e.g., cloud top temperature, optical depth, ...), as well as other observation characteristics (e.g., spatial and temporal resolution). Next, the selected data will be processed to yield the required information (e.g., vertical profiles of cloud liquid/ice water). Resulting profiles will be compared with data from anindependent source, e.g. from recently launched satellite instruments developed for experimental cloud studies. Retrieved cloud parameter profiles will then be inserted into the mesoscale meteorological model Advanced Regional Prediction System (ARPS) using data assimilation techniques.Subsequently, the impact of assimilating cloud data into ARPS will be evaluated, by confronting simulated values of relevant quantities (e.g., surface shortwave radiation, cloud liquid water content) with observed values. Following the succesful validation of ARPS, the urban/regional air quality model AURORA will be enhanced to fully benefit from the improved cloud simulations. This will be achieved by improving parameterisations that calculate photolysis coefficients. Finally, the accuracy ofregional air quality simulated with the improved AURORA model will be evaluated, focusing on ground-level pollutant concentrations of ozone. It is expected that a successfull achievement of the objectives will lead to significantly improved air quality simulations with the AURORA model, especially for ozone.