Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China

Guo, Jianping; Liu, Boming; Gong, Wei; Shi, Lijuan; Zhang, Yong; Ma, Yingying; Zhang, Jian; Chen, Tianmeng; Bai, Kaixu; Stoffelen, Ad; Leeuw, G. de; Xu, Xiangde

doi:10.5194/acp-21-2945-2021

Cited by 67 publications

(69 citation statements)

References 52 publications

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“…As with the Rayleigh-clear winds, the absolute bias is slightly larger for the ascending orbit than for the descending orbit. The small bias, slope close to 1, and high correlation coefficient agree well with those reported by Guo et al (2021). The scaled MADs are relatively large (5.56-5.66 m s -1 ), but the values are smaller than those of the Rayleigh-clear winds.…”

Section: Overall Intercomparisonsupporting

confidence: 88%

“…This suggests that the correction scheme against the scattering ratio was improved in the L2B processor (see Section 5.1.4). However, this result is different from that in the other validation studies conducted during the baseline 2B10 period (Guo et al, 2021). For the ascending (descending) orbit, the minimum (maximum) bias is -2.34 (0.56) m s -1 in the altitude range of 5-6 (6-7) km.…”

Section: Vertical Distribution Of Wind Differencescontrasting

confidence: 80%

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Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

Iwai

Arai

Oshiro

et al. 2021

Preprint

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Abstract. The first space-based Doppler wind lidar (DWL) onboard the Aeolus satellite was launched by the European Space Agency (ESA) on 22 August 2018 to obtain global profiles of horizontal line-of-sight (HLOS) wind speed. In this study, the Raleigh-clear and Mie-cloudy winds for periods of baseline 2B02 (from 1 October to 18 December 2018) and 2B10 (from 28 June to 31 December 2019 and from 20 April to 8 October 2020) were validated using 33 wind profilers (WPRs) installed all over Japan, two ground-based coherent Doppler wind lidars (CDWLs), and 18 GPS-radiosondes (GPS-RSs). In particular, vertical and seasonal analyses were performed and discussed using WPR data. During the baseline 2B02 period, a positive bias was found to be in the ranges of 0.46–1.69 m s−1 for Rayleigh-clear winds and 1.63–2.42 m s−1 for Mie-cloudy winds using the three independent reference instruments. The biases of Rayleigh-clear and Mie-cloudy winds were in the ranges of −0.82−+0.45 m s−1 and −0.71−+0.16 m s−1 during the baseline 2B10 period, respectively. The systematic error for the baseline 2B10 was improved as compared with that for the baseline 2B02. The vertical analysis using WPR data showed that the systematic error was slightly positive in all altitude ranges up to 11 km during the baseline 2B02 period. During the baseline 2B10 period, the systematic errors of Rayleigh-clear and Mie-cloudy winds were improved in all altitude ranges up to 11 km as compared with the baseline 2B02. Immediately after the launch of Aeolus, both Rayleigh-clear and Mie-cloudy biases were small. Within the baseline 2B02, the Rayleigh-clear and Mie-cloudy biases showed a positive trend. For the baseline 2B10, the Rayleigh-clear wind bias was generally negative at all months except August 2020, and Mie-cloudy wind bias gradually fluctuated. The systematic error was close to zero with time in 2020 and did not show a marked seasonal trend. The dependence of the Rayleigh-clear wind bias on the scattering ratio was investigated, showing that the scattering ratio had a minimal effect on the systematic error of the Rayleigh-clear winds during the baseline 2B02 period. On the other hand, during the baseline 2B10 period, there was no significant bias dependence on the scattering ratio. Without the estimated representativeness error associated with the comparisons using WPR observations, the Aeolus random error was determined to be 6.71 (5.12) and 6.42 (4.80) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. The main reason for the large random errors is probably related to the large representativeness error due to the large sampling volume of the WPRs. Using the CDWLs, the Aeolus random error estimates were in the range of 4.49–5.31 (2.93–3.19) and 4.81–5.21 (3.30–3.37) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively. By taking the GPS-RS representativeness error into account, the Aeolus random error was determined to be 4.01 (3.24) and 3.02 (2.89) m s−1 for Rayleigh-clear (Mie-cloudy) winds during the baseline 2B02 and 2B10 periods, respectively.

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Section: Overall Intercomparisonsupporting

confidence: 88%

Section: Vertical Distribution Of Wind Differencescontrasting

confidence: 80%

Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

Iwai

Arai

Oshiro

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…For the reprocessing, this issue is solved by using data from the same day to derive the MLR coefficients. corrected Aeolus winds is not zero (Martin et al, 2020;Belova et al, 2021;Guo et al, 2021). This also becomes clear when looking closely at the bottom panel of Figure 14 where the bias of the corrected O-B values, despite showing some residual effects, is in the range between -3 m/s and 3 m/s, depending on the geolocation.…”

Section: Case Studymentioning

confidence: 70%

Correction of wind bias for the lidar on-board Aeolus using telescope temperatures

Weiler¹,

Rennie²,

Kanitz³

et al. 2021

Preprint

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Abstract. The European Space Agency satellite Aeolus provides continuous profiles of the horizontal line-of-sight wind component at a global scale. It was successfully launched into space in August 2018 with the goal to improve numerical weather prediction (NWP). Aeolus data has already been successfully assimilated into several NWP models and has already helped to significantly improve the quality of weather forecasts. To achieve this major milestone the identification and correction of several systematic error sources was necessary. One of them is related to small temperatures fluctuations across the 1.5 m diameter primary mirror of the telescope which cause varying wind biases along the orbit of up to 8 m/s. This paper presents a detailed overview of the influence of the telescope temperature variations on the Aeolus wind products and describes the approach to correct for this systematic error source in the operational near-real-time (NRT) processing. It was shown that the telescope temperature variations along the orbit are due to changes of the top-of-atmosphere short- and long-wave radiation of the Earth and the response of the telescope’s thermal control system to that. To correct for this effect ECMWF model-equivalent winds are used as bias reference to describe the wind bias in a multiple linear regression model as a function of various temperature sensors located on the primary telescope mirror. This correction scheme has been in operational use at ECMWF since April 2020 and is capable of reducing a large part of the telescope-induced wind bias. In cases where the influence of the temperature variations is particularly strong it was shown that the bias correction can improve the orbital bias variation by up to 53 %. Moreover, it was demonstrated that the approach of using ECMWF model-equivalent winds is justified by the fact that the global bias of models u-component winds w.r.t to radiosondes is smaller than 0.3 m/s. However, this paper also presents the alternative of using Aeolus ground return winds which serve as zero wind reference in the multiple linear regression model. The results show that the approach based on ground return winds only performs 10.8 % worse than the ECMWF model-based approach and thus has good potential for future applications for upcoming reprocessing campaigns or even in the NRT processing of Aeolus wind products.

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“…In addition, global Aeolus data verification work using the NWP model has also been carried out (Martin et al, 2021;Rennie and Isaksen, 2019) . These verifications not only deepened our understanding of Aeolus data quality, but also discovered some factors that affect Aeolus data quality during the verification process, such as solar background radiation (Zhang et al, 2020), satellite flight direction (Guo et al, 2021), seasonal changes (Martin et al, 2021), etc. ECWMF also revised the data processing algorithm in time, such as the temperature gradient correction algorithm for M1, which is the main 45 mirror of the Aeolus telescope, to solve the problem of seasonal changes (Rennie and Isaksen, 2019).…”

Section: Introductionmentioning

confidence: 91%

“…CC BY 4.0 License. (Hauchecorne et al, 2020), ground-based wind profiler radar (Belova et al, 2021;Guo et al, 2021), and radiosonde (Liu et 40 al., 2021). In addition, global Aeolus data verification work using the NWP model has also been carried out (Martin et al, 2021;Rennie and Isaksen, 2019) .…”

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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Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China

Cited by 67 publications

References 52 publications

Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

Validation of Aeolus Level 2B wind products using wind profilers, ground-based Doppler wind lidars, and radiosondes in Japan

Correction of wind bias for the lidar on-board Aeolus using telescope temperatures

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

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