The photomultiplier tubes (PMTs) used to measure altitude-resolved 532 nm backscatter intensity by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) do not recover rapidly following exposure to very high light levels (e.g., from optically dense water clouds). Instead, they exhibit exponentially decaying “noise tails” that can cause substantial misestimates when determining layer base altitudes and deriving cloud extinction coefficients. Here we present a deconvolution correction algorithm to remove these noise tails in the CALIOP level 1 science data products. The corrected 532 nm attenuated backscatter profiles generated by the deconvolution algorithm are evaluated using the corresponding CALIOP observations at 1064 nm and coincident profiles measured at 532 nm by the NASA-Langley airborne high spectral resolution lidar. Results indicate that our deconvolution correction algorithm effectively removes the non-ideal PMT recovery effects on the CALIOP level 1 vertical profiles, showing excellent performance for surface and water cloud lidar returns when the attenuated backscatter coefficients are greater than 0.01 km⁻¹ sr⁻¹.

带有正交偏振的云气激光雷达（CALIOP）用来测量高度分辨的532 nm背向散射强度的光电倍增管（PMT）在暴露于非常高的光照水平（例如，来自光学密集的水云）后无法快速恢复。取而代之的是，它们表现出指数衰减的“噪声尾巴”，当确定层的基本高度并推导云的消光系数时，可能会导致严重的误估计。在这里，我们提出一种去卷积校正算法，以消除CALIOP 1级科学数据产品中的这些噪声尾巴。由反卷积算法生成的校正后的532 nm衰减后向散射剖面是使用1064 nm处的相应CALIOP观测值和NASA-Langley机载高光谱分辨率激光雷达在532 nm处测得的一致剖面进行评估的。^-1 sr ^-1。