Aeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line-of-sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level-2B (L2B) wind statistics have been assessed using the European Centre for Medium-range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0-7.0 m⋅s −1 for the Rayleigh-clear and 2.8-3.6 m⋅s −1 for the Mie-cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms.Complex systematic HLOS wind error variations on time-scales less than one orbit were identified, most strongly affecting the Rayleigh-clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020.In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short-range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root-mean-square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere.Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh-clear and Mie-cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh-clear and Mie-cloudy winds providing similar overall short-range impact in 2020.

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).

Monitoring of global positioning system radio occultation (GPSRO) data was performed. By comparisons between coincident GRAS and FORMOSAT-3/COSMIC occultations and coincident GPSRO refractivity and Vaisala RS92 radiosondederived refractivity data, we show the consistency of GPSRO data from independent missions (and differing processing methods) and with independent in situ observation data. It was found that the bending angle and refractivity data from COSMIC is positively biased relative to that of GRAS by 0.2-0.5% in the stratosphere, which is where the GRAS refractivity agreed better with the RS92 radiosonde-derived refractivity than COSMIC.Forecast impact experiments using GPSRO refractivity and bending angle measurements were conducted (using 1D operators). It is shown that refractivity data (from FORMOSAT-3/COSMIC, GRAS, CHAMP and GRACE-A) provided a large positive impact, with the greatest improvements in the Southern Hemisphere extratropics, where r.m.s. fit to radiosondes improved by up to 10% at 250 hPa. A refractivity to bending angle assimilation comparison was made. We found that bending angle assimilation (1D operator) generally provides a greater positive impact than refractivity, with a notable improvement in the Northern Hemisphere extratropics, where a ∼1% improvement in r.m.s. fit to radiosonde temperatures, geopotential heights and winds in the lower stratosphere was seen. c Crown

In preparation for the Aeolus Doppler Wind Lidar satellite mission, single‐component wind information from conventional observations was assimilated into the ECMWF data assimilation system. Various Observing System Experiments have been designed and performed in order to estimate the impact of such information in numerical weather prediction. The evaluation used various adjoint diagnostic tools and traditional statistical verification methods. The importance of assimilating wind observations as either single component or full vector wind is evaluated. Comparisons between the assimilation of wind and mass observations were also made. Wind observations can lead to significant improvement in the upper troposphere, lower stratosphere and in the Tropics. Mass data are more valuable in the midlatitudes, particularly in the lower part of the atmosphere. The comparison of additional mass and wind observations in the Global Observing System is useful to understand the importance of future wind observations. The investigation also highlighted that the impact of zonal wind observations is larger than the meridional wind (Aeolus will mostly measure wind components near the zonal direction). Root mean‐squared errors of temperature and wind forecasts when the single wind component (zonal) is assimilated show around 35% degradation up to day 2 forecasts and around 20% after day 2 as compared to assimilating full vector wind. Therefore the single (zonal) wind components can provide a large fraction of the vector wind impact particularly for the medium‐range forecast, which is promising for Aeolus.

Soon after its successful launch in August 2018, the spaceborne wind lidar ALADIN (Atmospheric LAser Doppler INstrument) on-board ESA’s Earth Explorer satellite Aeolus has demonstrated to provide atmospheric wind profiles on a global scale. Being the first ever Doppler Wind Lidar (DWL) instrument in space, ALADIN contributes to the improvement in numerical weather prediction (NWP) by measuring one component of the horizontal wind vector. The performance of the ALADIN instrument was assessed by a team from ESA, DLR, industry, and NWP centers during the first months of operation. The current knowledge about the main contributors to the random and systematic errors from the instrument will be discussed. First validation results from an airborne campaign with two wind lidars on-board the DLR Falcon aircraft will be shown.