Abstract. Originally designed as a wind lidar without depolarization measurement capability, ALADIN (Atmospheric Laser Doppler Instrument) detects only the co-polar component of the backscattered signal, limiting the accuracy of optical products in the presence of depolarizing atmospheric layers. The absence of the cross-polar component also prohibits ALADIN's ability to distinguish between different aerosol and cloud types, in its retrievals. To address these limitations, an enhanced Aeolus aerosol product (L2A+), with a focus on dust, has been developed in the present study to support aerosol data assimilation in dust transport models and improve Numerical Weather Prediction (NWP). The L2A+ product is generated through a series of processing steps that integrate multi-sensor satellite retrievals for cloud screening and aerosol layer characterization, CAMS reanalysis outputs to classify aerosol types, distinguish dust from non-dust fractions, and provide the missing depolarization ratio values required for the Polarization Lidar Photometer Networking (POLIPHON) technique, along with ground-based lidar measurements used for performance assessment. Both the primary (L2A) and enhanced (L2A+) Aeolus pure-dust backscatter coefficient profiles at 355 nm are retrieved using four different algorithms: the Standard Correct Algorithm (SCA), the Standard Correct Algorithm with middle-bin vertical scaling (SCA-MB), the Maximum Likelihood Estimation (MLE), and AEL-PRO. These products are validated against ground-based reference observations obtained from the eVe and PollyXT lidar systems, operated as part of the ASKOS/JATAC experimental campaign in Mindelo, Cabo Verde. The approach is detailed on the basis of an indicative Aeolus overpass in the proximity of Mindelo on 3 September 2021, discussing ALADIN's sources of underestimation in terms of L2A backscatter coefficient at 355 nm profiles in the presence of desert dust particles across all four retrieval algorithms and the induced improvements achieved by accounting for the missing cross-polar component. A statistical evaluation of all Aeolus overpasses during the entire ASKOS/JATAC campaign in the Cabo Verde/Mindelo region confirms the enhanced performance of the upgraded L2A+ product compared to the original L2A product. This improvement is evident in both Aeolus-eVe and Aeolus-PollyXT comparisons across all retrieval algorithms and is marked by higher correlation coefficients and regression slopes along with lower biases and RMSE scores. Specifically, among the algorithms, the AEL-PRO and MLE L2A+ products show significant improvements in alignment with eVe lidar observations. The correlation coefficients increased from 0.59 to 0.67 for MLE and from 0.55 to 0.67 for AEL-PRO, while biases decreased from −0.99 to −0.85 Mm-1sr-1 for MLE and from −0.79 to −0.58 Mm-1sr-1 for AEL-PRO. They also achieve lower RMSE values (1.17 Mm-1sr-1 for MLE and 0.94 Mm-1sr-1 for AEL-PRO) and better regression slopes, increasing from 0.28 to 0.34 (MLE) and 0.27 to 0.39 (AEL-PRO). Similarly, L2A+ adjustments reduce biases and improve correlation coefficients, and regression slopes in Aeolus-PollyXT comparisons, among all four retrieval algorithms. Particularly, the AEL-PRO algorithm shows the strongest improvement in correlation, slope, and error metrics. All these advancements establish the enhanced L2A+ dust product as a strong candidate for aerosol data assimilation, supporting improved dust transport modeling and further enhancing Numerical Weather Prediction (NWP).
Rizos et al. (Wed,) studied this question.