Validation of Aeolus wind products above the Atlantic Ocean

Baars, Holger; Herzog, A. D.; Heese, Birgit; Ohneiser, Kevin; Hanbuch, Karsten; Hofer, Julian; Yin, Zhenping; Engelmann, Ronny; Wandinger, Ulla

doi:10.5194/amt-13-6007-2020

Cited by 57 publications

(77 citation statements)

References 34 publications

(62 reference statements)

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“…Detailed information and results have been published in Lux et al (2020) and Witschas et al (2020). Further, Aeolus wind observations are compared to the direct-detection Doppler lidar LIOvent at the Observatoire de Haute-Provence for a time period at the beginning of 2019 (Khaykin et al, 2020) and to wind profiles obtained from radiosonde launches on board the German RV Polarstern in autumn 2018 across the Atlantic Ocean (Baars et al, 2020). Airborne Doppler lidars have been used in several case studies of mesoscale phenomena, such as the French mistral (Drobinski et al, 2005), Alpine foehn (Reitebuch et al, 2003), the sea breeze in southern France (Bastin et al, 2005), or the Alpine mountain-plain circulation (Weissmann et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

et al. 2021

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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).

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Section: Introductionmentioning

confidence: 99%

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

et al. 2021

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“…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.…”

Section: Aeolus and Rs Datamentioning

confidence: 99%

“…of line-of-sight wind speeds between the airborne Doppler wind lidar and the Aeolus were −0.9 m/s and +1.6 m/s, respectively (Lux et al, 2020). 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.…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Guo

Gong

et al. 2021

Preprint

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Abstract. The European Space Agency (ESA) Earth Explorer Atmospheric Dynamics Mission Aeolus is the first satellite mission providing wind profile information on a global scale, and its wind products have been released on 12 May 2020. In this study, we verify and intercompare the wind observations from ESA’s satellite mission Aeolus and the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation atmospheric reanalyses (ERA5) with radiosonde (RS) observations over China, to allow a fitting application of Aeolus winds. Aeolus provides wind observations in aerosol-free (referred to as Rayleigh-clear winds) and cloudy atmospheres (Mie-cloudy winds). In terms of Aeolus and RS winds, the correlation coefficient (R) and mean difference of Rayleigh-clear (Mie-cloudy) vs RS winds are 0.90 (0.92) and 0.09±9.62 (−0.59±8.05) m/s, respectively. The vertical profiles of wind speed differences between Aeolus and RS winds are opposite to each other during ascending and descending orbits, indicating that the performance of Aeolus wind product is affected by the orbit phase. The comparison of ECMWF winds relative to Aeolus winds provides the R and mean difference of Rayleigh-clear (Mie-cloudy) winds, which are 0.95 (0.97) and −0.16±6.78 (−0.21±3.91) m/s, respectively. The Rayleigh-clear and Mie-cloudy winds are almost consistent with the ECMWF winds, likely due to the assimilation of Aeolus wind observations into the ECMWF winds. Moreover, we find that among the results of comparing Aeolus with RS and ECMWF winds, the wind speed difference of Rayleigh-clear winds is large in the height range of 0–1 km, especially during descending orbits. This indicates that the performance of low-altitude Rayleigh-clear wind products could be affected by the near-surface aerosols. In addition, the R and mean difference between ERA5 and RS zonal wind components are 0.89 and −1.46±6.33 m/s, respectively. The RS zonal winds tend to be larger than those from ERA5. The wind speed difference between RS and ERA5 zonal winds in low-lying area is low and insignificant, while it is relatively high and significant over the Qinghai-Tibet Plateau areas. Overall, the Aeolus winds over China are similar to the RS and ECMWF winds. The RS and ERA5 zonal winds are somewhat different over high altitude area, but these differences are acceptable for application of wind products. The findings give us sufficient confidence and information to apply Aeolus wind products in numerical weather prediction in China and in climate change research.

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“…After the launch of Aeolus, the special verification work for Aeolus was 35 carried out immediately. The main verification methods included radiosonde (Baars et al, 2020) and airborne lidar (Witschas et al, 2020;Lux et al, 2020), in which Aeolus' airborne prototype ALADIN (Atmospheric Laser Doppler Instrument) Airborne Demonstrator (A2D) was also used. In the following two years, researchers around the world completed regional verification of the Aeolus detection data using a variety of detection means, including satellites (Shin et al, 2020), ground-based lidar https://doi.org/10.5194/acp-2021-298 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Study on the seasonal variation of Aeolus detection performance over China using ERA5 and radiosonde data

Chen¹,

Cao²,

Xie³

et al. 2021

Preprint

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Abstract. Aeolus wind products have been available to ordinary users on May 12, 2020. In this paper, the Aeolus wind observations, L-band radiosonde (L-band RS) data and the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation atmospheric reanalyses (ERA5) are used to analyse the seasonality of Aeolus detection performance over China. Based on the Rayleigh-clear data and Mie-cloudy data, the data quality of the Aeolus effective detection data is verified, and the results show that the Aeolus data is in good agreement with the L-band RS data and the ERA5 data. The relative errors of Aeolus data in the four regions (Chifeng, Baoshan, Shapingba and Qingyuan) in China were calculated according to different months (July to December 2019, May to October 2020). The relative error of the Rayleigh-clear data in summer is significantly higher than that in winter, as the mean relative error parameter in July is 174 % higher than that in December. Besides, the distribution about the wind direction and the high-altitude clouds in different months (July and December) are analysed. The results show that the distribution of angle, between the horizontal wind direction of the atmosphere and the horizontal line of sight (HLOS), has a greater proportion in the high error interval (70°–110°) in summer, and this proportion is 8.14 % higher in July than in December. In addition, the cloud top height in summer is about 3–5 km higher than in winter, which may reduce the signal-to-noise ratio (SNR) of Aeolus. The results show that the detection performance of Aeolus is affected by seasonal factors, which may be caused by seasonal changes in wind direction and cloud distribution.

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Validation of Aeolus wind products above the Atlantic Ocean

Cited by 57 publications

References 34 publications

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

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

Study on the seasonal variation of Aeolus detection performance over China using ERA5 and radiosonde data

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