Ensemble-based data assimilation schemes for atmospheric chemistry models

The atmosphere is a complex system which includes physical, chemical and biological processes. Many of these processes affecting the atmosphere are subject to various interactions and can be highly nonlinear. This complexity makes it necessary to apply computer models in order to understand the natural behavior of the atmosphere. In addition to the chemical and physical processes it requires detailed information on the emissions, deposition and transport of trace constituents. Data assimilation represents a crucial tool for estimating and predicting the chemical processes in the atmosphere. It refers to several techniques that aim to combine the information from various sources to provide unified and consistent description of an atmospheric chemical system. The aim of this thesis was to investigate the use of several sequential data assimilation techniques in both ideal and real settings in the context of atmospheric chemistry. The increased complexity of models together with the different types of information about pollutants have the potential of contributing to a better understanding of chemical processes. Therefore, an optimal selection of the data assimilation techniques adapted to the new challenges in the atmospheric chemistry is required.Applied mathematicsElectrical Engineering, Mathematics and Computer Scienc

A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe

Fine particulate matter (PM) is relevant for human health and its components are associated with climate effects. The performance of chemistry transport models for PM, its components and precursor gases is relatively poor. The use of these models to assess the state of the atmosphere can be strengthened using data assimilation. This study focuses on simultaneous assimilation of sulphate and its precursor gas sulphur dioxide into the regional chemistry transport model LOTOS-EUROS using an ensemble Kalman filter. The process of going from a single component setup for SO2 or SO4 to an experiment in which both components are assimilated simultaneously is illustrated. In these experiments, solely emissions, or a combination of emissions and the conversion rates between SO2 and SO4 were considered uncertain. In general, the use of sequential data assimilation for the estimation of the sulphur dioxide and sulphate distribution over Europe is shown to be beneficial. However, the single component experiments gave contradicting results in direction in which the emissions are adjusted by the filter showing the limitations of such applications. The estimates of the pollutant concentrations in a multi-component assimilation have found to be more realistic. We discuss the behavior of the assimilation system for this application. The model uncertainty definition is shown to be a critical parameter. The increased complexity associated with the simultaneous assimilation of strongly related species requires a very careful specification of the experiment, which will be the main challenge in the future data assimilation applications. © 2008 Elsevier Ltd. All rights reserved