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

Liu, Boming; Guo, Jianping; Gong, Wei; Yong, Zhang; Shi, Lijuan; Ma, Yingying; Li, Jian; Guo, Xiaoyi; Stoffelen, Ad; Leeuw, Gerrit de; Xu, Xiangde

doi:10.5194/acp-2021-41

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…The main verification methods have included radiosonde (Baars et al, 2020) and airborne lidar (Lux et al, 2020;Witschas et al, 2020), in which Aeolus's prototype atmospheric laser Doppler instrument airborne demonstrator was used. Over the following 2 years, researchers worldwide completed regional verification of the Aeolus detection data using various detection methods, including satellites (Shin et al, 2020), groundbased lidar (Hauchecorne et al, 2020), ground-based wind profiler radar (Belova et al, 2021;Guo et al, 2021), and radiosonde (Liu et al, 2021). In addition, global Aeolus data verification studies using the numerical weather prediction (NWP) model have been undertaken (Rennie and Isaksen, 2019;Martin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data

et al. 2021

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Abstract. Aeolus wind products became available to the public on 12 May 2020. In this study, Aeolus wind observations, L-band radiosonde (RS) data, and the European Centre for Medium-Range Weather Forecasts fifth-generation atmospheric reanalysis (ERA5) data were used to analyze the seasonality of Aeolus wind product performance over China. Based on the Rayleigh-clear and Mie-cloudy data, the data quality of the Aeolus effective detection data was verified, and the results showed that the Aeolus data were in good agreement with the L-band RS and ERA5 data. The Aeolus data relative errors in the four regions (Chifeng, Baoshan, Shapingba, and Qingyuan) in China were calculated based on different months (July to December 2019 and May to October 2020). The relative error in the Rayleigh-clear data in summer was significantly higher than that in winter, with the mean relative error parameter in July 174 % higher than that in December. The mean random error increased by 0.97 m s−1 in July compared with December, which also supported this conclusion. In addition, the distribution of the wind direction and high-altitude clouds in different months (July and December) was analyzed. The results showed that the distribution of the angle between the horizontal wind direction of the atmosphere and the horizontal line of sight had a greater proportion in the high error interval (70–110∘) in summer, and this proportion was 8.14 % higher in July than in December. The cloud top height in summer was approximately 3–5 km higher than that in winter, which might decrease the signal-to-noise ratio of Aeolus. Therefore, the wind product performance of Aeolus was affected by seasonal factors, which might be caused by seasonal changes in wind direction and cloud distribution.

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Section: Introductionmentioning

confidence: 99%

Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data

et al. 2021

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“…The ERA5 launched by ECMWF is currently the most powerful global climate atmospheric reanalysis tool, which provides hourly data and uncertainty estimates of various atmospheric, land and ocean state parameters. Additionally, it has also been widely used for atmospheric parameter estimation [36][37][38]. Existing studies [36,[38][39][40] have shown the accuracy and applicability of wind speed and temperature in ERA5 data.…”

Section: Era5 Accuracy Analysismentioning

confidence: 99%

“…Additionally, it has also been widely used for atmospheric parameter estimation [36][37][38]. Existing studies [36,[38][39][40] have shown the accuracy and applicability of wind speed and temperature in ERA5 data. Hersbach H et al [41] as well showed the good performance of ERA5 data.…”

Section: Era5 Accuracy Analysismentioning

confidence: 99%

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

et al. 2021

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Directly establishing the relationship between satellite data and PM2.5 concentration through deep learning methods for PM2.5 concentration estimation is an important means for estimating regional PM2.5 concentration. However, due to the lack of consideration of uncertainty in deep learning methods, methods based on deep learning have certain overfitting problems in the process of PM2.5 estimation. In response to this problem, this paper designs a deep Bayesian PM2.5 estimation model that takes into account multiple scales. The model uses a Bayesian neural network to describe key parameters a priori, provide regularization effects to the neural network, perform posterior inference through parameters, and take into account the characteristics of data uncertainty, which is used to alleviate the problem of model overfitting and to improve the generalization ability of the model. In addition, different-scale Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite data and ERA5 reanalysis data were used as input to the model to strengthen the model’s perception of different-scale features of the atmosphere, as well as to further enhance the model’s PM2.5 estimation accuracy and generalization ability. Experiments with Anhui Province as the research area showed that the R2 of this method on the independent test set was 0.78, which was higher than that of the DNN, random forest, and BNN models that do not consider the impact of the surrounding environment; moreover, the RMSE was 19.45 μg·m−3, which was also lower than the three compared models. In the experiment of different seasons in 2019, compared with the other three models, the estimation accuracy was significantly reduced; however, the R2 of the model in this paper could still reach 0.66 or more. Thus, the model in this paper has a higher accuracy and better generalization ability.

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

Cited by 4 publications

References 31 publications

Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data

Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data

Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale

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

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