Rapid Aeolus L2B HLOS Wind Retrieval via BP Neural Network

Wind field information is a key variable in atmospheric science and weather prediction, and spaceborne Doppler wind lidar provides unique global observations of the horizontal line-of-sight (HLOS) wind. This study develops a data-driven model that maps Aeolus Rayleigh-channel Level-1B (L1B) observables to the operational Level-2B (L2B) HLOS wind product. Using the two Rayleigh discriminator responses as inputs, we train a backpropagation (BP) neural network to learn the nonlinear relationship between Rayleigh-channel measurements and the collocated L2B HLOS winds. The proposed approach is intended as a computationally efficient emulation/approximation of the L2B HLOS output from L1B observations, rather than as an independently validated accuracy-improving retrieval. Model performance is evaluated by agreement with the L2B reference across samples spanning July 2019 to May 2020 and an altitude range of 0-20 km. The results show that the proposed model reproduces the main statistical characteristics and along-track HLOS patterns of the L2B product, providing a fast option for generating L2B-like HLOS estimates from Rayleigh-channel inputs.