Derivation and validation of a refined dust product from Aeolus (L2A+)

Abstract. The missing cross-channel of the lidar system aboard Aeolus (Atmospheric Laser Doppler Instrument; ALADIN) prevents the generation of accurate optical products when depolarizing atmospheric layers are probed. The absence of the cross-polar component also limits ALADIN's ability to distinguish between different aerosol and cloud types, in its retrievals. To address these limitations, an enhanced Aeolus aerosol product, 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 enhanced aerosol product is derived through a series of intermediate processing steps that integrate spaceborne retrievals/products from multiple sensors, reanalysis numerical outputs, and reference ground-based measurements. Both the primary (L2A), and enhanced (L2A+) Aeolus optical products, in terms of profiles of backscatter coefficient at 355 nm are retrieved using four different algorithms, the Standard Correct Algorithm (SCA), the Standard Correct Algorithm at the middle-bin vertical scale (SCA-MB), the Maximum Likelihood Estimation (MLE), and AEL-PRO. These products are validated against ground-based reference observations obtained from the eVe and Polly^XT 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 September 3, 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-Polly^XT comparisons across all retrieval algorithms and is marked by higher regression slopes and lower bias scores. Specifically, among the algorithms, AEL-PRO and MLE L2A+ products show significant improvements in alignment with eVe lidar observations, with bias reductions from -0.46 to -0.17 Mm⁻¹sr⁻¹ (MLE) and -0.43 to -0.04 Mm⁻¹sr⁻¹ (AEL-PRO). They also achieve lower RMSE values (0.87 Mm⁻¹sr⁻¹ for MLE and 0.62 Mm⁻¹sr⁻¹ for AEL-PRO) and better regression slopes, increasing from 0.39 to 0.65 (MLE) and 0.53 to 0.87 (AEL-PRO). Similarly, L2A+ adjustments reduce biases and improve regression slopes in Aeolus-Polly^XT comparisons, especially for SCA-MB and MLE algorithms. 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).