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.

Highlights

  • Atmospheric three-dimensional wind fields play a key role for the prediction of extreme events (Pu et 15 al., 2010; Guo et al, 2018; Stettner et al, 2019), a better understanding of air pollution dispersion (Liu et al, 2018; Yang et al, 2019; Shi et al, 2020; Su et al, 2020; Zhang et al, 2020) and complex aerosolcloud-precipitation interactions (Koren et al, 2005; Fan et al, 2009; Li et al, 2011; Guo et al, 2017; Liu et al, 2020a)

  • The mean difference of RS horizontal line-of-sight (HLOS) relative to Rayleigh-clear and Mie-cloudy HLOS are 0.09±9.62 and 0.59±8.05 m/s, respectively (Fig. 5d). These results indicate that the Aeolus 10 Rayleigh-clear and Mie-cloudy wind products over China are on average consistent with RS wind observations, but with large dispersion

  • For the Mie-cloudy winds, the average HLOS differences are negative during ascending orbits ( 2.18±4.17 m/s), whereas they are positive during descending orbits (2.75±5.13 m/s)

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Summary

Introduction

Atmospheric three-dimensional wind fields play a key role for the prediction of extreme events (Pu et 15 al., 2010; Guo et al, 2018; Stettner et al, 2019), a better understanding of air pollution dispersion (Liu et al, 2018; Yang et al, 2019; Shi et al, 2020; Su et al, 2020; Zhang et al, 2020) and complex aerosolcloud-precipitation interactions (Koren et al, 2005; Fan et al, 2009; Li et al, 2011; Guo et al, 2017; Liu et al, 2020a). The flight altitude and 5 repeat cycle of the Aeolus satellite are about 320 km and 7days, respectively (Witschas et al, 2020) It provides measurements of the wind vector component along the instrument's line-of-sight from the ground up to 30 km altitude (Rennie et al, 2020). It is worthwhile to further verify the performance of Aeolus wind products in China using the China’s RS observation network This 15 network comprises 120 RS sites homogeneously distributed across mainland China and provides atmospheric soundings of winds (Guo et al, 2016; 2019). The use of these data, together with ERA-5 reanalysis data, provides a unique opportunity to deepen our understanding of the performance of Aelous wind products over all mainland China.

Aeolus wind data
Radiosonde wind data
ERA5 wind data
Data matching procedures
Aeolus and RS data
Aeolus and ECMWF data For the comparison of Aeolus and ECMWF data, the
RS and ERA5 data 20
Statistical method
Overall intercomparison
Vertical distribution of wind differences
Horizontal distribution of wind differences
Summary and conclusions

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