Validation and Assimilation of Aeolus Wind Observations

Doctoral Thesis / Dissertation from the year 2023 in the subject Geography / Earth Science - Meteorology, Aeronomy, Climatology, grade: 1, LMU Munich (Physics), language: English, abstract: Along with scientific and technological developments, the advancement of the Global Observing System (GOS) has been one of the most important factors contributing to the increase in numerical weather forecasting (NWP) skill in recent years. The initial conditions of a forecast are provided by data assimilation systems, combining the latest short-range forecast with a selection of atmospheric observations. One of the current major limitations is the lack of global wind profile observations, particularly in regions and for spatial scales where geostrophic mass-wind coupling is weak. The European Space Agency's (ESA) Doppler Wind Lidar (DWL) satellite mission Aeolus provides a novel data set of wind profiles with quasi-global coverage intended to fill these data gaps in the current GOS. Therefore, it is of great interest to assess the impact of the Aeolus observations in NWP to demonstrate the potential value of such satellite-based DWL missions. A crucial prerequisite for using meteorological observations in NWP data assimilation systems is a comprehensive knowledge of their errors. The first part of this thesis investigates the Aeolus data quality through comparisons with three independent reference data sets: collocated radiosonde observations and model equivalents of the global ICOsahedral Nonhydrostatic (ICON) model of Deutscher Wetterdienst (DWD) and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecast (ECMWF). This enables a comprehensive estimation and characterization of the systematic and random errors of the Aeolus wind profile observations. Approaches to correct for the analyzed complex systematic errors that can be used in the context of quality control in data assimilation systems are being tested. Furthermore, to obtain a meaningful estimation of the Aeolus instrumental error, the representativeness errors for the comparisons are determined based on high-resolution regional model simulations. The results show the importance of accounting for representativeness errors for the mission's calibration and validation activities and provide an estimate of the vertical structure of the Aeolus Rayleigh and Mie wind instrumental error that can be used for the assigned observation error in data assimilation. The second part of this thesis examines how numerical weather forecasting benefits from the assimilation of the novel DWL observations from the Aeolus satellite.