Enabling accurate high-altitude aerosol profiling in polarized lidar via a well-calibrated spectral discriminator

High-altitude aerosols (typically >5 km) play a crucial role in forming ice-nucleating particles (INPs), which regulate Earth's energy budget. However, their retrieval is highly sensitive to signal perturbations due to relatively lower concentrations. By proposing a novel, to the best of our knowledge, uncertainty analysis model and simulating with different aerosol loadings, we demonstrate that, with a spectral discriminator (SD), polarized lidar can reduce low-concentration aerosol retrieval errors from >100% to <50%. Moreover, we reveal that if SD parameters are not properly calibrated, uncertainty may remain >100%. The efficacy of well-calibrated SD is demonstrated through a high-altitude profiling experiment, where uncertainty is reduced from 557.85% (without SD) and 351.23% (bad-calibrated) to 42.45% (well-calibrated). This work is expected to provide an approach for reducing uncertainty in polarized lidar-based INPs estimation.