Development of China’s first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration

1.
State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China
2.
ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China
3.
Shanghai Institute of Satellite Engineering, Shanghai, 201109, China
4.
National Satellite Meteorological Center, China Meteorological Administration, Beijing, 100081, China
5.
Shanghai Academy of Spaceflight Technology, Shanghai, 201109, China
6.
Leibniz Institute for Tropospheric Research (TROPOS), 04318, Leipzig, Germany
7.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
8.
Collaborative Innovation Center On Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044, China
9.
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Science, Shanghai, 201800, China
10.
Intelligent Optics & Photonics Research Center, Jiaxing Research Institute Zhejiang University, Jiaxing, 314000, China
11.
Jiaxing Key Laboratory of Photonic Sensing & Intelligent Imaging, Jiaxing, 314000, China

Funds: This work was supported by the Excellent Young Scientist Program of Zhejiang Provincial Natural Science Foundation of China (LR19D050001); State Key Laboratory of Modern Optical Instrumentation Innovation Program (MOI2021ZD01); A Project Supported by Scientific Research Fund of Zhejiang University(XY2021050).

摘要

- /
- /
- /
- /
Abstract: Aerosols and clouds greatly affect the Earth’s radiation budget and global climate. Light detection and ranging (lidar) has been recognized as a promising active remote sensing technique for the vertical observations of aerosols and clouds. China launched its first space-borne aerosol-cloud high-spectral-resolution lidar (ACHSRL) on April 16, 2022, which is capable for high accuracy profiling of aerosols and clouds around the globe. This study presents a retrieval algorithm for aerosol and cloud optical properties from ACHSRL which were compared with the end-to-end Monte-Carlo simulations and validated with the data from an airborne flight with the ACHSRL prototype (A2P) instrument. Using imaging denoising, threshold discrimination, and iterative reconstruction methods, this algorithm was developed for calibration, feature detection, and extinction coefficient (EC) retrievals. The simulation results show that 95.4% of the backscatter coefficient (BSC) have an error less than 12% while 95.4% of EC have an error less than 24%. Cirrus and marine and urban aerosols were identified based on the airborne measurements over different surface types. Then, comparisons were made with U.S. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiles, Moderate-resolution Imaging Spectroradiometer (MODIS), and the ground-based sun photometers. High correlations (R > 0.79) were found between BSC (EC) profiles of A2P and CALIOP over forest and town cover, while the correlation coefficients are 0.57 for BSC and 0.58 for EC over ocean cover; the aerosol optical depth retrievals have correlation coefficient of 0.71 with MODIS data and show spatial variations consistent with those from the sun photometers. The algorithm developed for ACHSRL in this study can be directly employed for future space-borne high-spectral-resolution lidar (HSRL) and its data products will also supplement CALIOP data coverage for global observations of aerosol and cloud properties.
- Aerosol /
- Cloud /
- Retrieval /
- Space-borne lidar /
- Airborne campaign

HTML全文

参考文献(58)

[1]	Carslaw KS, Lee LA, Reddington CL, Pringle KJ, Rap A, Forster PM, et al. Large contribution of natural aerosols to uncertainty in indirect forcing. Nature. 2013;503:67–71.
[2]	Rosenfeld D, Lohmann U, Raga GB, O’Dowd CD, Kulmala M, Fuzzi S, et al. Flood or drought: How do aerosols affect precipitation? Science. 2008;321:1309–13.
[3]	Rosenfeld D, Sherwood S, Wood R, Donner L. Climate Effects of Aerosol-Cloud Interactions. Science. 2014;343:379–80.
[4]	Sun H, Wang S, Hu X, Liu H, Zhou X, Huang J, et al. Detection of surface defects and subsurface defects of polished optics with multisensor image fusion. PhotoniX. 2022;3:6.
[5]	Miles RB, Lempert WR, Forkey JN. Laser Rayleigh scattering. Meas Sci Technol. 2001;12:R33–51.
[6]	She CY. Spectral structure of laser light scattering revisited: bandwidths of nonresonant scattering lidars. Appl Opt. 2001;40:4875–84.
[7]	Mattis I, D’Amico G, Baars H, Amodeo A, Madonna F, Iarlori M. EARLINET Single Calculus Chain - technical - Part 2: Calculation of optical products. Atmos Meas Tech. 2016;9:3009–29.
[8]	Hair JW, Hostetler CA, Cook AL, Harper DB, Ferrare RA, Mack TL, et al. Airborne High Spectral Resolution Lidar for profiling aerosol optical properties. Appl Opt. 2008;47:6734–52.
[9]	Winker DM, Hunt WH, McGill MJ. Initial performance assessment of CALIOP. Geophys Res Lett. 2007;34:L19803.
[10]	Wang N, Zhang K, Shen X, Wang Y, Li J, Li C, et al. Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction. Proc Natl Acad Sci USA. 2022;119:e2110756119.
[11]	Behrenfeld MJ, Hu YX, O’Malley RT, Boss ES, Hostetler CA, Siegel DA, et al. Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nat Geosci. 2017;10:118–22.
[12]	Bissonnette LR, Bruscaglioni P, Ismaelli A, Zaccanti G, Cohen A, Benayahu Y, et al. LIDAR multiple scattering from clouds. Appl Phys B Laser Optics. 1995;60:355–62.
[13]	Liu D, Zheng Z, Chen W, Wang Z, Li W, Ke J, et al. Performance estimation of space-borne high-spectral-resolution lidar for cloud and aerosol optical properties at 532 nm. Opt Express. 2019;27:A481–94.
[14]	Han G, Xu H, Gong W, Liu J, Du J, Ma X, et al. Feasibility Study on Measuring Atmospheric CO2 in Urban Areas Using Spaceborne CO2-IPDA LIDAR. Remote Sensing. 2018;10:985.
[15]	Han G, Ma X, Liang A, Zhang T, Zhao Y, Zhang M, et al. Performance Evaluation for China’s Planned CO2-IPDA. Remote Sensing. 2017;9:768.
[16]	Wang S, Ke J, Chen S, Zheng Z, Cheng C, Tong B, et al. Performance Evaluation of Spaceborne Integrated Path Differential Absorption Lidar for Carbon Dioxide Detection at 1572 nm. Remote Sensing. 2020;12:2570.
[17]	Winker DM, Vaughan MA, Omar A, Hu Y, Powell KA, Liu Z, et al. Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms. J Atmos Oceanic Tech. 2009;26:2310–23.
[18]	Schuster GL, Vaughan M, MacDonnell D, Su W, Winker D, Dubovik O, et al. Comparison of CALIPSO aerosol optical depth retrievals to AERONET measurements, and a climatology for the lidar ratio of dust. Atmos Chem Phys. 2012;12:7431–52.
[19]	Illingworth AJ, Barker HW, Beljaars A, Ceccaldi M, Chepfer H, Clerbaux N, et al. THE EARTHCARE SATELLITE The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation. Bull Am Meteor Soc. 2015;96:1311–32.
[20]	Liu D, Donovan DP, van Zadelhoff GJ, Williams JE, Wandinger U, Haarig M, et al. Development of ATLID Retrieval Algorithms. EPJ Web of Conf. 2020;237:01005.
[21]	Nicolae D, Donovan D, Zadelhoff G-Jv, Daou D, Wandinger U, Makoto A, et al. Earthcare atlid extinction and backscatter retrieval algorithms. EPJ Web of Conf. 2018;176:02022.
[22]	Zhang Y, Liu D, Shen X, Bai J, Liu Q, Cheng Z, et al. Design of iodine absorption cell for high-spectral-resolution lidar. Opt Express. 2017;25:15913–26.
[23]	Shen X, Wang N, Veselovskii I, Xiao D, Zhong T, Liu C, et al. Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Calibration of overlap function. J Quant Spectrosc Radiat Transfer. 2020;257:107338.
[24]	Xiao D, Wang N, Shen X, Landulfo E, Zhong T, Liu D. Development of ZJU High-Spectral-Resolution Lidar for Aerosol and Cloud: Extinction Retrieval. Remote Sensing. 2020;12:3047.
[25]	Wang N, Shen X, Xiao D, Veselovskii I, Zhao C, Chen F, et al. Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Feature detection and classification. J Quant Spectrosc Radiat Transfer. 2021;261:107513.
[26]	Zhang Y, Liu D, Zheng Z, Liu Z, Hu D, Qi B, et al. Effects of auxiliary atmospheric state parameters on the aerosol optical properties retrieval errors of high-spectral-resolution lidar. Appl Opt. 2018;57:2627–37.
[27]	Dong J, Liu J, Bi D, Ma X, Zhu X, Zhu X, et al. Optimal iodine absorption line applied for spaceborne high spectral resolution lidar. Appl Opt. 2018;57:5413–9.
[28]	Xiao Y, Binglong C, Min M, Xingying Z, Lilin Y, Yiming Z, et al. Simulating return signals of a spaceborne high-spectral resolution lidar channel at 532 nm. Optics Communications. 2018;417:89–96.
[29]	Zheng Z, Chen W, Zhang Y, Chen S, Liu D. Denoising the space-borne high-spectral-resolution lidar signal with block-matching and 3D filtering. Appl Opt. 2020;59:2820–8.
[30]	Mao F, Zhao M, Gong W, Chen L, Liang Z. Layer detection algorithm for CALIPSO observation based on automatic segmentation with a minimum cost function. J Quant Spectrosc Radiat Transfer. 2021;261:107498.
[31]	Mao F, Liang Z, Pan Z, Gong W, Sun J, Zhang T, et al. A simple multiscale layer detection algorithm for CALIPSO measurements. Remote Sens Environ. 2021;266:112687.
[32]	Shi T, Han G, Xin M, Gong W, Chen W, Liu J, et al. Quantifying CO2 Uptakes Over Oceans Using LIDAR: A Tentative Experiment in Bohai Bay. Geophys Res Lett. 2021;48:L091160.
[33]	Wang Q, Bu L, Tian L, Xu J, Zhu S, Liu J. Validation of an airborne high spectral resolution Lidar and its measurement for aerosol optical properties over Qinhuangdao. China Optics Express. 2020;28:24471–88.
[34]	Zhu Y, Yang J, Chen X, Zhu X, Zhang J, Li S, et al. Airborne Validation Experiment of 1.57-μm Double-Pulse IPDA LIDAR for Atmospheric Carbon Dioxide Measurement. Remote Sensing. 2020;12:1999.
[35]	Jia L, Zheng W, Huang F. Vacuum-ultraviolet photodetectors. PhotoniX. 2020;1:22.
[36]	Li Y, Zheng W, Huang F. All-silicon photovoltaic detectors with deep ultraviolet selectivity. PhotoniX. 2020;1:15.
[37]	Liu D, Yang Y, Cheng Z, Huang H, Zhang B, Ling T, et al. Retrieval and analysis of a polarized high-spectral-resolution lidar for profiling aerosol optical properties. Opt Express. 2013;21:13084–93.
[38]	Pornsawad P, D’Amico G, Bckmann C, Amodeo A, Pappalardo G. Retrieval of aerosol extinction coefficient profiles from Raman lidar data by inversion method. Appl Opt. 2012;51:2035–44.
[39]	Grigorov I, Kolarov G, Dreischuh TN, Daskalova AT. Rayleigh-fit approach applied to improve the removal of background noise from lidar data. Proc SPIE - Int Soc Opt Eng. 2013;8770:10.
[40]	Rocadenbosch F, Reba MM, Sicard M, Comerón A. Practical analytical backscatter error bars for elastic one-component lidar inversion algorithm. Appl Opt. 2010;49:3380–93.
[41]	Thorsen TJ, Fu Q, Newsom RK, Turner DD, Comstock JM. Automated Retrieval of Cloud and Aerosol Properties from the ARM Raman Lidar. Part I: Feature Detection. J Atmos Ocean Technol. 2015;32:150904105051007.
[42]	Liu Z, Hunt W, Vaughan M, Hostetler C, McGill M, Powell K, et al. Estimating random errors due to shot noise in backscatter lidar observations. Appl Opt. 2006;45:4437–47.
[43]	Liu Z, Sugimoto N. Simulation study for cloud detection with space lidars by use of analog detection photomultiplier tubes. Appl Opt. 2002;41:1750–9.
[44]	Harmany ZT, Marcia RF, Willett RM. This is SPIRAL-TAP: sparse poisson intensity reconstruction ALgorithms—theory and practice. IEEE Trans Image Process. 2012;21:1084–96.
[45]	Rogers RR, Hostetler CA, Hair JW, Ferrare RA, Liu Z, Obland MD, et al. Assessment of the CALIPSO Lidar 532 nm attenuated backscatter calibration using the NASA LaRC airborne high spectral resolution lidar. Atmos Chem Phys. 2011;11:1295–311.
[46]	Grigas T, Hervo M, Gimmestad G, Forrister H, Schneider P, Preißler J, et al. CALIOP near-real-time backscatter products compared to EARLINET data. Atmos Chem Phys. 2015;15:12179–91.
[47]	do Carmo JP, de Villele G, Wallace K, Lefebvre A, Ghose K, Kanitz T, et al. ATmospheric LIDar (ATLID): Pre-Launch Testing and Calibration of the European Space Agency Instrument That Will Measure Aerosols and Thin Clouds in the Atmosphere. Atmosphere. 2021;12:76.
[48]	Powell KA, Hunt WH, Winker DM. Simulations of CALIPSO Lidar Data, Quebec City, Quebec. 2002.
[49]	Wu Y, de Graaf M, Menenti M. The Sensitivity of AOD Retrieval to Aerosol Type and Vertical Distribution over Land with MODIS Data. Remote Sensing. 2016;8:765.
[50]	Shi T, Han G, Xin M, Gong W, Chen W, Liu J, et al. Quantifying CO2 Uptakes Over Oceans Using LIDAR: A Tentative Experiment in Bohai Bay. Geophys Res Lett. 2021;48:e2020GL091160.
[51]	Team MCS (2017) MODIS 250m Calibrated Radiances Product, https://doi.org/10.5067/MODIS/MOD02QKM.061, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA.
[52]	Mark F, Sulla-Menashe D (2019) MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, https://doi.org/10.5067/MODIS/MCD12Q1.006, NASA LP DAAC.
[53]	Lyapustin A, Wang Y (2018) MCD19A2 MODIS/Terra+Aqua Land Aerosol Optical Depth Daily L2G Global 1km SIN Grid V006, https://doi.org/10.5067/MODIS/MCD19A2.006, NASA EOSDIS Land Processes DAAC.
[54]	Vaughan M, Young S, Winker D, Powell K, Omar A, Liu Z, et al. Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products. SPIE. 2004;5575:16–30.
[55]	Burton SP, Ferrare RA, Hostetler CA, Hair JW, Rogers RR, Obland MD, et al. Aerosol classification using airborne High Spectral Resolution Lidar measurements – methodology and examples. Atmos Meas Techn. 2012;5:73–98.
[56]	Groß S, Esselborn M, Weinzierl B, Wirth M, Fix A, Petzold A. Aerosol classification by airborne high spectral resolution lidar observations. Atmos Chem Phys. 2013;13:2487–505.
[57]	Baroni T, Pandey P, Preissler J, Gimmestad G, O’Dowd C. Comparison of Backscatter Coefficient at 1064 nm from CALIPSO and Ground-Based Ceilometers over Coastal and Non-Coastal Regions. Atmosphere. 2020;11:1190.
[58]	Amiridis V, Marinou E, Tsekeri A, Wandinger U, Schwarz A, Giannakaki E, et al. LIVAS: a 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET. Atmos Chem Phys. 2015;15:7127–53.