Prediction of Atmospheric Dispersion on All Scales for Emergency Preparedness

The Greenland ice sheet is an important element of the Earth’s climate system and is a central climate component for studying the phenomenon of abrupt changes in its overall state due to small changes in external forcing, commonly known as tipping. The focus on ice sheets is partly because of evidence of abrupt changes in the paleoclimate record of Greenland, and partly because the global consequences of tipping the ice sheet being easily understood. However, due to the vast complexity of the climate the conceptual understanding of tipping in simple lowdimensional systems requires some translation to be be applicable to any real-world system. This thesis analyzes three models along a hierarchy of dimension and number of processes to bridge that gap.
In addition, both historical and recent events have demonstrated the necessity for being able to conduct inverse modelling as an operational service. This capability would assist responsible authorities in localizing unknown sources and/or characterizing the temporal development of gas and particle emissions in emergency situations. Therefore, two inverse methods have been developed: The first allows for source localization based on a set of air concentration measurements in cases where the release location is unknown. The second enables estimation of the multi-nuclide source term from a nuclear power plant accident in cases where little or no direct information about the release is available, as it has in fact been the case in historical nuclear accidents. This second method is designed specifically for use in the early stages of an accident, where an improved source term estimate may be crucial for facilitating reliable dispersion predictions.
The developments and findings in this PhD project successfully lay the foundations for new operational tasks at DMI while also constituting important contributions to the research field of dispersion modelling, especially inverse modelling for source term estimation and localization.