– Campus Golm, Haus 9, Raum 1.10
Parameter Estimation from Noisy Observations of a Stochastic Heat Equation
Gregor Pasemann (HU)
Ludger Scherliess (Utah State University)
Over the past decades physics-based data assimilation models have been used in many areas of science and engineering and have found extensive use in meteorology and oceanography. More recently, data assimilation models have also become prevalent for specifications and forecasts of the near-Earth space environment and in particular of the Earth’s ionosphere. Since its discovery, the ionosphere was found to display changes over a large range of spatial and temporal scales that can have detrimental effects on several human activities and systems, including high-frequency communications, over-the-horizon radars, and survey and navigation systems such as the Global Positioning System (GPS) satellites. In an effort to mitigate these adverse effects and to better understand the system data assimilation models for the ionosphere have been developed that employ a variety of data assimilation techniques. This increased use of ionospheric data assimilation models also coincides with a strong increase in data suitable for assimilation that include both space- and ground-based observational platforms. In this talk we will discuss the models, the data, and the techniques that are being used for ionospheric data assimilation.
Invited by Claudia Stolle and Yuri Shprits.