Aeolus - ESA'S Wind Lidar Mission, A Brief Status

Kanitz, Thomas; Wernham, Denny; Álvarez, E. González; Tzeremes, Georgios; Parrinello, Tommaso; Marshall, Jon; Brewster, John; Lecrenier, Olivier; Schillinger, Marc; Sanctis, V. De; D’Ottavi, A.; Reitebuch, Oliver; Weiler, Fabian; Lux, Oliver; Rennie, Michael; Isaksen, Lars

doi:10.1109/igarss39084.2020.9324686

Cited by 3 publications

(3 citation statements)

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“…The quality of the Aeolus wind data contained in the L2B product was considerably improved by the correction for large systematic errors which had strongly degraded the wind data quality in the initial phase of the mission (Kanitz et al, 2020;. While dark current signal anomalies on single (hot) pixels of the Aeolus detectors were successfully mitigated for by the implementation of dedicated calibration instrument modes (Weiler et al, 2021a), wind biases that were introduced by variations in the temperature distribution across the primary telescope mirror were Table 3.…”

Section: Aeolus Validation Using the Improved A2dmentioning

confidence: 99%

“…The Aeolus mission primarily aims at improving numerical weather prediction (NWP) by filling observational gaps in the global wind data coverage, particularly over the oceans, poles, tropics, and the Southern Hemisphere (Andersson, 2018;Stoffelen et al, 2005Stoffelen et al, , 2020Straume et al, 2020). This goal was already achieved within the first half of the intended mission lifetime of 3 years, with the successful assimilation of Aeolus winds into NWP models, after having identified and corrected for two large error sources that had diminished the wind data quality in the early phase of the mission (Kanitz et al, 2020;. Whereas the implementation of a dedicated calibration instrument mode allowed for the consideration of dark current signal fluctuations on the Aeolus detectors (Weiler et al, 2021a), wind biases that were caused by variations in the temperature distribution across the primary telescope mirror were considerably reduced by a correction scheme based on ECMWF (European Centre for Medium-Range Weather Forecasts) model-equivalent winds Weiler et al, 2021b;.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

et al. 2022

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Abstract. The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of ALADIN, the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented, which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 to about 1.5 m s−1, demonstrating the capability of such a direct detection DWL. Furthermore, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m s−1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds for ascending and descending orbits, as well as random errors of about 3 m s−1 (HLOS) for the Mie and close to 6 m s−1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality.

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Section: Aeolus Validation Using the Improved A2dmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation

et al. 2022

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“…Lux et al: Quality control and error assessment of the Aeolus L2B wind results from the JATAC by filling observational gaps in the global wind data coverage, especially over the oceans, at the poles and in the tropics (Andersson, 2018;Stoffelen et al, 2005Stoffelen et al, , 2020Straume et al, 2020). This objective was already reached in 2020 when several weather services started the operational assimilation of Aeolus wind observations, which became possible by the identification and corrections of two major error sources that degraded the wind data quality during the first year of the mission (Kanitz et al, 2020;. First, a dedicated calibration procedure was implemented to account for dark current fluctuations on the Aeolus detectors ("hot" pixels), thereby reducing large wind biases in certain altitude ranges (Weiler et al, 2021a).…”

Section: Introductionmentioning

confidence: 99%

Quality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic Campaign

Lux

Witschas²,

Geiß³

et al. 2022

Atmos. Meas. Tech.

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Abstract. Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s−1 with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s−1, Mie vs. DWL is 4.1 m s−1, Rayleigh vs. model is 7.8 m s−1, and Rayleigh vs. DWL is 8.2 m s−1) are elaborated in the text.

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