The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data

Sun, Xiangyi; Zhang, R. W.; Marseille, Gert‐Jan; Stoffelen, Ad; Donovan, D. P.; Liu, Lei; Zhao, Junyong

doi:10.5194/amt-7-2695-2014

Cited by 22 publications

(21 citation statements)

References 34 publications

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“…In particular, the position of the top edges of optically thick clouds within one range gate has a significant influence on the wind data. According to Sun et al (2014) who investigated the performance of Aeolus in heterogeneous 25 atmospheric conditions using high-resolution radiosonde data, a non-uniform distribution of clouds and/or aerosols within a range bin introduces random errors in the Mie HLOS winds of several m·s -1 depending on the bin size and altitude.…”

Section: Statistical Comparison Of A2d and 2-µm Dwl Windsmentioning

confidence: 99%

Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus

Lux¹,

Lemmerz²,

Weiler³

et al. 2018

Preprint

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The paper also highlights the relevance of accurate ground detection for the Rayleigh and Mie response calibration and wind retrieval. Using a detection scheme developed for the NAWDEX campaign, the obtained ground return signals are exploited for the correction of systematic wind errors. Validation of the instrument performance and retrieval algorithms was conducted by comparison with DLR's coherent wind lidar which was operated in parallel, showing a systematic error of the A2D LOS winds of less than 0.5 m·s -1 and random errors from 1.5 m·s -1 (Mie) to 2.7 m·s -1 (Rayleigh). 20

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Section: Statistical Comparison Of A2d and 2-µm Dwl Windsmentioning

confidence: 99%

Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus

Lux¹,

Lemmerz²,

Weiler³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Considering 2-µm winds as the truth, higher LOS wind speeds (−10 m/s-0 m/s) are obviously underestimated by the A2D Mie channel (computed with calibration #2) whereas below -10 m/s the A2D Mie channel tended to measure higher (regarding the absolute value) wind speeds than the 2-µm LiDAR. One possible explanation is discussed by Sun et al [68] who elucidate the performance of Aeolus in heterogeneous atmospheric conditions, i.e., in cases where atmospheric dynamics and optical properties vary strongly within the sampling volume. The longest A2D wind measurement scene with 88 min at a stretch was obtained on 25 May 2015 ( Figure 13).…”

Section: Wind Measurementsmentioning

confidence: 99%

Calibrations and Wind Observations of an Airborne Direct-Detection Wind LiDAR Supporting ESA’s Aeolus Mission

et al. 2018

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The Aeolus satellite mission of the European Space Agency (ESA) has brought the first wind LiDAR to space to satisfy the long-existing need for global wind profile observations. Until the successful launch on 22 August 2018, pre-launch campaign activities supported the validation of the measurement principle, the instrument calibration, and the optimization of retrieval algorithms. Therefore, an airborne prototype instrument has been developed, the ALADIN Airborne Demonstrator (A2D), with ALADIN being the Atmospheric Laser Doppler Instrument of Aeolus. Two airborne campaigns were conducted over Greenland, Iceland and the Atlantic Ocean in September 2009 and May 2015, employing the A2D as the first worldwide airborne direct-detection Doppler Wind LiDAR (DWL) and a well-established coherent 2-µm wind LiDAR. Both wind LiDAR instruments were operated on the same aircraft measuring Mie backscatter from aerosols and clouds as well as Rayleigh backscatter from molecules in parallel. This paper particularly focuses on the instrument response calibration method of the A2D and its importance for accurate wind retrieval results. We provide a detailed description of the analysis of wind measurement data gathered during the two campaigns, introducing a dedicated aerial interpolation algorithm that takes into account the different resolution grids of the two LiDAR systems. A statistical comparison of line-of-sight (LOS) winds for the campaign in 2015 yielded estimations of the systematic and random (mean absolute deviation) errors of A2D observations of about 0.7 m/s and 2.1 m/s, respectively, for the Rayleigh, and 0.05 m/s and 2.3 m/s, respectively, for the Mie channel. In view of the launch of Aeolus, differences between the A2D and the satellite mission are highlighted along the way, identifying the particular assets and drawbacks.

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“…Using high-resolution atmospheric data as an input for the Aeolus simulator is essential for realistic simulation, especially in areas with large wind shear over short distances where Aeolus errors may be large, given the relatively thick vertical bins of typically 1 km (e.g. Houchi et al, 2010;Sun et al, 2014).…”

Section: Inputs For the Aeolus Simulatormentioning

confidence: 99%

The prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight wind profiles

Šavli

Kloe

Marseille

et al. 2019

Quart J Royal Meteoro Soc

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This article evaluates the prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight (HLOS) wind profiles at the expense of their accuracy. The evaluation is performed by combining a 10‐day atmosphere simulation by the ECMWF model at T3999 horizontal resolution with the CALIPSO observations of atmospheric composition as inputs to the Aeolus simulator. The validation shows that the ECMWF model represents the location and the vertical structure of the observed cloud systems well. At the nominal accumulation length of L ≈ 90 km (from the Aeolus measurement scale of ∼3 km), the Mie‐cloudy retrieval provides 1–4 times fewer observations than Rayleigh‐clear but the Mie‐cloudy HLOS winds have the highest quality with estimated error standard deviation of about 1 m/s in the troposphere and no bias. The experiments with reduced L reveal that neither the observation error standard deviation nor bias of the Mie‐cloudy winds are significantly affected when the accumulation length L varies in the range between 100 and 10 km. At the same time, the number of observations significantly increases as L reduces. This suggests that mesoscale NWP may profit from the Aeolus Mie‐cloudy HLOS profiles with the accumulation lengths as small as 10 km.

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The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data

Cited by 22 publications

References 34 publications

Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus

Airborne wind lidar observations over the North Atlantic in 2016 for the pre-launch validation of the satellite mission Aeolus

Calibrations and Wind Observations of an Airborne Direct-Detection Wind LiDAR Supporting ESA’s Aeolus Mission

The prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight wind profiles

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