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

Chen, Siying; Cao, Rongzheng; Xie, Yadong; Zhang, Yinchao; Tan, Wangshu; Chen, He; Guo, Ping; Zhao, Peitao

doi:10.5194/acp-2021-298

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…This product consists of 10 sources with a time resolution of 3 hours (Hersbach et al 2020). Chen et al (2021) showed that ERA5 data on wind speed are reliable in most areas of Guangdong and the area near the coast of Guangdong. Yang et al (2019) found that the accuracy of precipitable water data provided by ERA5 is better than that of ERA-interim, and both reanalysis data fully re ect the annual variability of precipitable water, but the daily variability deviation of ERA5 is generally smaller than that of ERA-interim.…”

Section: Methodsmentioning

confidence: 99%

Comparing water vapor transport during precipitation processes in the pre-flood period of South China and precipitation processes of typhoons in the post-flood period of South China: A case study

Ling¹

2022

Preprint

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The typical precipitation processes in the pre-flood of South China and precipitation processes of typhoons in the post-flood period of South China have different water vapor transport characteristics. In this research, the water vapor transport characteristics of the representative precipitation cases in the two circumstances were compared using ERA5 reanalysis data and GPM IMERG Late Run precipitation product. The water vapor transport channel of the precipitation process of the typhoon was stronger and higher than that in the pre-flood period of South China, and the distribution of water vapor flux convergence was also higher. The maximum value of water vapor flux of pre-flood case was related to the wind speed and specific humidity both in the boundary layer (950 hPa). For the typhoon case, the maximum value of water vapor flux was related to the wind speed in the boundary layer, whereas it was related to the specific humidity in the middle and low layer (700 hPa). These distinctions can help us quickly find the probably locations and impact factors of the maximum values of water vapor flux during precipitation processes in the pre-flood of South China and precipitation processes of typhoons in the post-flood period of South China on different pressure surfaces to the greatest extent and predict the intensities of different water vapor transport channels.

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Section: Methodsmentioning

confidence: 99%

Comparing water vapor transport during precipitation processes in the pre-flood period of South China and precipitation processes of typhoons in the post-flood period of South China: A case study

Ling¹

2022

Preprint

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“…In particular, the switch-over from the first flight model laser (FM-A) to the redundant laser FM-B just after AVATARE in June 2019 marked a caesura, as the FM-B delivered much higher emit energy and hence ensured higher signal-to-noise ratio of the backscatter return compared to the end of the FM-A period (Lux et al 2020b;Lux et al, 2021). Moreover, the satellite instrument was found to be more susceptible to changes in the thermal environment than expected before launch showing orbital and seasonal variations in the beam alignment, and hence in the wind bias (Martin et al, 2021;Chen et al, 2021). This issue, together with the influence of the seasonally varying solar background being incident on the instrument spectrometers and affecting the wind measurement precision, could be investigated based on the comparative wind measurements from the AVATARE and AVATARI campaigns.…”

Section: Overview Of Validation Campaigns and Datasetsmentioning

confidence: 99%

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

Lux

Lemmerz

Weiler

et al. 2021

Preprint

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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 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 the Atmospheric LAser Doppler INstrument (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 Doppler wind lidar 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 m∙s−1 to about 1.5 m∙s−1, demonstrating the capability of such a direct detection DWL. Moreover, 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 L2B Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) 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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“…The availability of the Aeolus dataset provides the unique opportunity to directly assess the performance of AMVs derived from different retrieval channels relative to a global reference wind profile dataset observed by a single unit. Such a direct comparison has not previously been possible due to the sparse spatial coverage of other available reference datasets, e.g., rawinsonde winds (e.g., Chen et al, 2021;Liu, B. et al, 2021;Martin et al, 2021). Further, Aeolus observations are made at a set of fixed vertical levels that represent the averages of accumulated measurements within vertical range bins.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

Lukens

Ide

Garrett

et al. 2021

Preprint

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Abstract. The need for highly accurate atmospheric wind observations is a high priority in the science community, and in particular numerical weather prediction (NWP). To address this requirement, this study leverages Aeolus wind LIDAR Level-2B data provided by the European Space Agency (ESA) to better characterize atmospheric motion vector (AMV) bias and uncertainty, with the eventual goal of potentially improving AMV algorithms. AMV products from geostationary (GEO) and low-Earth polar orbiting (LEO) satellites are compared with reprocessed Aeolus horizontal line-of-sight (HLOS) global winds observed in August and September 2019. Winds from two of the four Aeolus observing modes are utilized for comparison with AMVs: Rayleigh-clear (derived from the molecular scattering signal) and Mie-cloudy (derived from particle scattering). For the most direct comparison, quality controlled (QC’d) Aeolus winds are collocated with quality controlled AMVs in space and time, and the AMVs are projected onto the Aeolus HLOS direction. Mean collocation differences (MCD) and standard deviation (SD) of those differences (SDCD) are determined from comparisons based on a number of conditions, and their relation to known AMV bias and uncertainty estimates is discussed. GOES-16 and LEO AMV characterizations based on Aeolus winds are described in more detail. Overall, QC’d AMVs correspond well with QC’d Aeolus HLOS wind velocities (HLOSV) for both Rayleigh-clear and Mie-cloudy observing modes, despite remaining biases in Aeolus winds after reprocessing. Comparisons with Aeolus HLOSV are consistent with known AMV bias and uncertainty in the tropics, NH extratropics, and in the Arctic, and at mid- to upper-levels in both clear and cloudy scenes. SH comparisons generally exhibit larger than expected SDCD, which could be attributed to height assignment errors in regions of high winds and enhanced vertical wind shear. GOES-16 water vapor clear-sky AMVs perform best relative to Rayleigh-clear winds, with small MCD (-0.6 m s-1 to 0.1 m s-1) and SDCD (5.4–5.6 m s-1) in the NH and tropics that fall within the accepted range of AMV error values relative to radiosonde winds. Compared to Mie-cloudy winds, AMVs exhibit similar MCD and smaller SDCD (~4.4–4.8 m s-1) throughout the troposphere. In polar regions, Mie-cloudy comparisons have smaller SDCD (5.2 m s-1 in the Arctic, 6.7 m s-1 in the Antarctic) relative to Rayleigh-clear comparisons, which are larger by 1–2 m s-1. The level of agreement between AMVs and Aeolus winds varies per combination of conditions including the Aeolus observing mode coupled with AMV derivation method, geographic region, and height of the collocated winds. It is advised that these stratifications be considered in future comparison studies and impact assessments involving 3D winds. Additional bias corrections to the Aeolus dataset are anticipated to further refine the results.

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

Cited by 3 publications

References 15 publications

Comparing water vapor transport during precipitation processes in the pre-flood period of South China and precipitation processes of typhoons in the post-flood period of South China: A case study

Comparing water vapor transport during precipitation processes in the pre-flood period of South China and precipitation processes of typhoons in the post-flood period of South China: A case study

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

Exploiting Aeolus Level-2B Winds to Better Characterize Atmospheric Motion Vector Bias and Uncertainty

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