Abstract. In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

Abstract. In August 2018, the first Doppler Wind Lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the northern hemisphere (23.5–65° N) show strong variations of the Aeolus winds bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the independent reference data sets. Due to lower representativeness, the random differences are larger for the validation using radiosonde observations compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, besides the estimation of the model and radiosonde observational error. The resulting Aeolus errors estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences depend on latitude. A latitude based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land-sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

On 22nd August 2018, the European Space Agency (ESA) launched the first direct detection Doppler wind lidar into space. Operating at 355 nm and acquiring signals with a dual channel receiver, it allows wind observations in clear air and particle-laden regions of the atmosphere. Furthermore, particle optical properties can be obtained using the High Spectral Resolution Technique Lidar (HSRL) technique. Measuring with 87 km horizontal and 0.25-2 km vertical resolution between ground and up to 30 km in the stratosphere, the global coverage of Aeolus observations shall fill gaps in the global observing system and thus help improving numerical weather prediction. Within this contribution, first results from the German initiative for experimental Aeolus validation are presented and discussed. Ground-based wind and aerosol measurements from tropospheric radar wind profilers, Doppler wind lidars, radiosondes, aerosol lidars and cloud radars are utilized for that purpose.

Aeolus is the first satellite mission to acquire vertical profiles of horizontal line‐of‐sight winds globally and thus fills an important gap in the Global Observing System, most notably in the Tropics. This study explores the impact of this dataset on analyses and forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) and Deutscher Wetterdienst (DWD), focusing specifically on the West African Monsoon (WAM) circulation during the boreal summers of 2019 and 2020. The WAM is notoriously challenging to forecast and is characterized by prominent and robust large‐scale circulation features such as the African Easterly Jet North (AEJ‐North) and Tropical Easterly Jet (TEJ). Assimilating Aeolus generally improves the prediction of zonal winds in both forecasting systems, especially for lead times above 24 h. These improvements are related to systematic differences in the representation of the two jets, with the AEJ‐North weakened at its southern flank in the western Sahel in the ECMWF analysis, while no obvious systematic differences are seen in the DWD analysis. In addition, the TEJ core is weakened in the ECMWF analysis and strengthened on its southern edge in the DWD analysis. The regions where the influence of Aeolus on the analysis is greatest correspond to the Intertropical Convergence Zone (ITCZ) region for ECMWF and generally the upper troposphere for DWD. In addition, we show the presence of an altitude‐ and orbit‐dependent bias in the Rayleigh‐clear channel, which causes the zonal winds to speed up and slow down diurnally. Applying a temperature‐dependent bias correction to this channel contributes to a more accurate representation of the diurnal cycle and improved prediction of the WAM winds. These improvements are encouraging for future investigations of the influence of Aeolus data on African Easterly Waves and associated Mesoscale Convective Systems.

Abstract. Global wind profiles from the Aeolus satellite mission provide an important source of wind information for Numerical Weather Prediction (NWP). Data assimilation experiments show large mean changes in the analysis and a significant reduction of forecast errors. At Deutscher Wetterdienst (DWD), an Observing Systems Experiment (OSE) for three months, from July 2020 to October 2020, has been performed to evaluate the impact of the Aeolus horizontal line-of-sight (HLOS) wind observations in the operational data assimilation system of the ICOsahedral Nonhydrostatic (ICON) global model. To better understand the underlying dynamics leading to the overall beneficial impact, specific time periods and regions with particularly high impact of Aeolus are investigated. In this study, we illustrate three examples of atmospheric phenomena that constitute dynamical scenarios for significant forecast error reduction through the assimilation of Aeolus: The phase shift of large-scale tropical circulation systems, namely the quasi-biennial oscillation (QBO) and the El Niño–Southern Oscillation (ENSO), and the interaction of tropical cyclones undergoing extratropical transition (ET) with the midlatitude waveguide.