Validation of Aeolus winds using radiosonde observations and NWP model equivalents

Abstract

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).

Airborne Lidar Observations of Wind, Water Vapor, and Aerosol Profiles During The NASA Aeolus Cal/Val Test Flight Campaign

“…This is further depicted in the profile comparison (Figure 16c , it should be noted here that the validated Aeolus L2B dataset is known to contain wind speed biases caused by an imperfect telescope temperature management along the orbit and from orbit to orbit, pending the top of the atmosphere total radiance variability. This has been shown from ECMWF model observation monitoring and from comparisons with ground-based and radiosonde observations (Martin et al 2020). These results differ from those reported by Witschas et al (2020) during WindVal III and AVATARE, which could be caused by a variety of factors including differences in Aeolus laser pulse energy and latitude, longitude, and time-dependent telescope temperature issues at the time of the campaigns, wind conditions being sampled, sample size, and criteria used to construct the Aeolus -airborne wind lidar match database.…”

“…Finally, comparisons of the Aeolus data quality with the ECMWF model background and collocated CAL/VAL observations from ground-based instrumentation (including wind profilers and radiosondes) showed that the variability of the Earth top-of-atmosphere total radiance along the Aeolus orbit cause thermal stress an deformations of the instrument telescope which could not be fully compensated by the implemented telescope thermal control. This has caused biases of the Aeolus L2B winds of several m/s varying along the orbit and from orbit to orbit (Martin et al 2020). An on-ground correction of the telescope temperature induced bias has been developed and was implemented in April 2020.…”

Intercomparison of wind observations from ESA's satellite mission Aeolus, ERA5 reanalysis and radiosonde over China

“…In addition, it should be noted that RS has the problem of time and space drift in measuring the vertical wind profile (Baars et al, 2020;Martin et al, 2020). As shown in Fig.…”

“…Moreover, the Aeolus wind products have been compared with RS data above the Atlantic Ocean, from which the systematic and statistical errors of the Rayleigh (Mie) winds were found less than 1.5 (1.0) and 3.3 (1.0) m/s, respectively (Baars et al, 2020). Martin et al (2020) compared the Aeolus winds with radiosonde (RS) observations and two numerical weather prediction model equivalents, confirming that the performance of Aeolus wind products can be easily affected by satellite flight direction, seasonal differences and geographic changes. In addition, Guo et al (2020) evaluated the performance of Aeolus wind products over China by comparing with data from the ground-based radar wind profiler network of China.…”

ALADIN laser frequency stability and its impact on the Aeolus wind error

“…On 9 January 2020, the operational assimilation of the Aeolus wind data started at the European Centre for Medium-Range Weather Forecasts (ECMWF), followed by the German, French, and British weather services Deutscher Wetterdienst (DWD), Météo France, and Met Office in May, June, and December 2020, respectively. Recent assessments of the significance of the Aeolus data for NWP have demonstrated statistically positive impact, especially in the tropics and at the poles, thus providing a useful contribution to the GOS (Rennie and Isaksen, 2020a;Rennie and Isaksen, 2020b;Martin et al, 2020). This was made possible by the identification of and correction for large systematic errors which had strongly degraded the wind data quality in the initial phase of the mission (Kanitz et al, 2020;.…”