Inter-comparison of wind measurements in the atmospheric boundary layer and the lower troposphere with Aeolus and a ground-based coherent Doppler lidar network over China

Abstract

Abstract. After the successful launch of Aeolus, which is the first spaceborne wind lidar developed by the European Space Agency (ESA), on 22 August 2018, we deployed several ground-based coherent Doppler wind lidars (CDLs) to verify the wind observations from Aeolus. By the simultaneous wind measurements with CDLs at 17 stations over China, the Rayleigh-clear and Mie-cloudy horizontal-line-of-sight (HLOS) wind velocities from Aeolus in the atmospheric boundary layer and the lower troposphere are compared with those from CDLs. To ensure the quality of the measurement data from CDLs and Aeolus, strict quality controls are applied in this study. Overall, 52 simultaneous Mie-cloudy comparison pairs and 387 Rayleigh-clear comparison pairs from this campaign are acquired. All of the Aeolus-produced Level 2B (L2B) Mie-cloudy HLOS wind and Rayleigh-clear HLOS wind and CDL-produced HLOS wind are compared individually. For the inter-comparison result of Mie-cloudy HLOS wind and CDL-produced HLOS wind, the correlation coefficient, the standard deviation, the scaled mean absolute deviation (MAD) and the bias are 0.83, 3.15 m s−1, 2.64 m s−1 and −0.25 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 0.93, 0.92 and −0.33 m s−1. For the Rayleigh-clear HLOS wind, the correlation coefficient, the standard deviation, the scaled MAD and the bias are 0.62, 7.07 m s−1, 5.77 m s−1 and −1.15 m s−1, respectively, while the y=ax slope, the y=ax+b slope and the y=ax+b intercept are 1.00, 0.96 and −1.2 m s−1. It is found that the standard deviation, the scaled MAD and the bias on ascending tracks are lower than those on descending tracks. Moreover, to evaluate the accuracy of Aeolus HLOS wind measurements under different product baselines, the Aeolus L2B Mie-cloudy HLOS wind data and L2B Rayleigh-clear HLOS wind data under Baselines 07 and 08, Baselines 09 and 10, and Baseline 11 are compared against the CDL-retrieved HLOS wind data separately. From the comparison results, marked misfits between the wind data from Aeolus Baselines 07 and 08 and wind data from CDLs in the atmospheric boundary layer and the lower troposphere are found. With the continuous calibration and validation and product processor updates, the performances of Aeolus wind measurements under Baselines 09 and 10 and Baseline 11 are improved significantly. Considering the influence of turbulence and convection in the atmospheric boundary layers and the lower troposphere, higher values for the vertical velocity are common in this region. Hence, as a special note, the vertical velocity could impact the HLOS wind velocity retrieval from Aeolus.

Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia

“…So, the meteorological conditions during Mie wind measurements for the ascending and descending orbit phases appear quite different, which may imply different representativeness conditions and hence different random errors. The vertical distributions of wind differences indicate that Mie-cloudy winds are more precise compared to Rayleighclear winds below 1500 m for all data, which is consistent with the studies for China and Japan (Iwai et al, 2021;Wu et al, 2022). Higher random errors for Rayleigh-clear winds can partly be attributed to the smaller range bin thickness in the PBL.…”

Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia

“…For vertical distributions of wind difference, Mie-cloudy winds are more precise compared with Rayleigh-clear winds below 1,500 m for all data, which is consistent with the studies for China and Japan (Iwai et al, 2021;Wu et al, 2022). Below 750 m, large biases both for Rayleigh-clear and Mie-cloudy winds were found during descending orbits.…”

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

“…For the use of Aeolus observations in NWP models, a detailed characterization of the data quality as well as the minimization of systematic errors is crucial. Thus, several scientific and technical studies have been performed and published in the meanwhile, addressing the performance of ALADIN (Atmospheric LAser Doppler INstrument) on-board Aeolus and the quality of the wind data products (e.g., Bedka et al, 2021;Martin et al, 2021;Baars et al, 2020;Guo et al, 2021;Zuo et al, 2022;Wu et al, 2022;Chou et al, 2021;Belova et al, 2021).…”

“…The successful implementation of error corrections in the Aeolus L2B processor was also demonstrated by Guo et al (2021) and Zuo et al (2022) who used RWP measurements over China from April to July 2020 and over Australia from October 2020 until March 2021, respectively, to reveal a smaller mean systematic error of −0.6 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy), or 0.7 m s −1 for both Rayleigh-clear and Mie-cloudy winds. Besides that, Wu et al (2022) used ground-based heterodyne detection Doppler wind lidar measurements in the timeframe from January to December 2020 and determined systematic errors of −1.2 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy) and random errors of 5.8 m s −1 (Rayleigh-clear) and 2.6 m s −1 (Mie-cloudy), respectively. A summary of the validation results from different CalVal campaigns is given in Table 1, containing the time-period of the respective campaigns, the L2B processor version that was operational within this time period, the systematic error µ and random error σ of Rayleigh-clear and Mie-cloudy winds, as well as the reference instrument that was used.…”