Suppr超能文献

CALIPSO激光雷达在532纳米处的校准:第4版夜间算法

CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm.

作者信息

Kar Jayanta, Vaughan Mark A, Lee Kam-Pui, Tackett Jason L, Avery Melody A, Garnier Anne, Getzewich Brian J, Hunt William H, Josset Damien, Liu Zhaoyan, Lucker Patricia L, Magill Brian, Omar Ali H, Pelon Jacques, Rogers Raymond R, Toth Travis D, Trepte Charles R, Vernier Jean-Paul, Winker David M, Young Stuart A

机构信息

Science Systems and Applications Inc., Hampton, VA, USA.

NASA Langley Research Center, Hampton, VA, USA.

出版信息

Atmos Meas Tech. 2018 Mar;11(3):1459-1479. doi: 10.5194/amt-11-1459-2018. Epub 2018 Mar 14.

Abstract

Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures - i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime - depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30-34 km to 36-39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. As well, an enhanced strategy for filtering the radiation-induced noise from high energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved MERRA-2 model. An aerosol scattering ratio of 1.01 ± 0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2-3% lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne high spectral resolution lidar (HSRL) are reduced from 3.6% ± 2.2% in the version 3 data set to 1.6% ± 2.4 % in the version 4 release.

摘要

搭载在云-气溶胶激光雷达与红外探路者卫星观测(CALIPSO)卫星上的正交偏振云-气溶胶激光雷达(CALIOP)的数据产品,在对所有1级衰减后向散射测量实施新的(第4版)校准算法后,最近进行了更新。在这项工作中,我们介绍了第4版夜间532纳米平行通道校准的动机和实施情况。夜间532纳米校准是CALIOP数据最基本的校准,因为CALIOP的所有其他辐射校准程序——即532纳米白天校准以及夜间和白天的1064纳米校准——都直接或间接地依赖于532纳米夜间校准。通过将分子归一化高度从30 - 34千米提高到36 - 39千米,以大幅减少平流层气溶胶污染,532纳米夜间校准的精度得到了显著提高。由于在这些更高高度处分子数密度大大降低,从而信噪比(SNR)降低,现在使用来自多个相邻颗粒的数据对信号在更多样本上进行平均。此外,采用了一种增强策略来过滤来自高能粒子的辐射诱导噪声。此外,早期版本中使用的气象模型已被改进的MERRA - 2模型所取代。现在在校准高度明确使用1.01±0.01的气溶胶散射比。这些修改导致全球校准系数经过修订,平均比之前的数据版本低2 - 3%。此外,新的校准程序被证明消除了早期版本中存在的高海拔偏差,从而改善了平流层气溶胶的表示。还展示了使用机载激光雷达测量的验证结果。相对于兰利研究中心(LaRC)机载高光谱分辨率激光雷达(HSRL)进行的并置测量的偏差,从第3版数据集中的3.6%±2.2%降低到了第4版中的1.6%±2.4%。

相似文献

1
CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm.
Atmos Meas Tech. 2018 Mar;11(3):1459-1479. doi: 10.5194/amt-11-1459-2018. Epub 2018 Mar 14.
2
Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.
Atmos Meas Tech. 2019 Nov 28;12(11):6241-6258. doi: 10.5194/amt-12-6241-2019.
4
The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm.
Atmos Meas Tech. 2018;11(11):6107-6135. doi: 10.5194/amt-11-6107-2018.
8
CALIPSO lidar ratio retrieval over the ocean.
Opt Express. 2011 Sep 12;19(19):18696-706. doi: 10.1364/OE.19.018696.

引用本文的文献

1
Ice nucleation by volcanic ash greatly alters cirrus cloud properties.
Sci Adv. 2025 May 9;11(19):eads0572. doi: 10.1126/sciadv.ads0572.
2
3D volumetric tomography of clouds using machine learning for climate analysis.
Sci Rep. 2025 Mar 10;15(1):8270. doi: 10.1038/s41598-025-90169-y.
5
Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.
Atmos Meas Tech. 2019 Nov 28;12(11):6241-6258. doi: 10.5194/amt-12-6241-2019.
6
The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm.
Atmos Meas Tech. 2018;11(11):6107-6135. doi: 10.5194/amt-11-6107-2018.
7
Intercomparison of in-situ aircraft and satellite aerosol measurements in the stratosphere.
Sci Rep. 2019 Oct 30;9(1):15576. doi: 10.1038/s41598-019-52089-6.
8
Elevated aerosol layer over South Asia worsens the Indian droughts.
Sci Rep. 2019 Jul 16;9(1):10268. doi: 10.1038/s41598-019-46704-9.

本文引用的文献

1
The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).
J Clim. 2017 Jun 20;Volume 30(Iss 13):5419-5454. doi: 10.1175/JCLI-D-16-0758.1.
3
Observational constraints on mixed-phase clouds imply higher climate sensitivity.
Science. 2016 Apr 8;352(6282):224-7. doi: 10.1126/science.aad5300.
4
The persistently variable "background" stratospheric aerosol layer and global climate change.
Science. 2011 Aug 12;333(6044):866-70. doi: 10.1126/science.1206027. Epub 2011 Jul 21.
5
Methodology for error analysis and simulation of lidar aerosol measurements.
Appl Opt. 1979 Nov 15;18(22):3783-97. doi: 10.1364/AO.18.003783.
6
Airborne high spectral resolution lidar for profiling aerosol optical properties.
Appl Opt. 2008 Dec 20;47(36):6734-52. doi: 10.1364/ao.47.006734.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验