Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6601-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-6601-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Oct 2021
Research article |
| 13 Oct 2021
| 13 Oct 2021
Atmospheric carbon dioxide measurement from aircraft and comparison with OCO-2 and CarbonTracker model data
Qin Wang
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and
Technology (NUIST), Nanjing 210044, China
Farhan Mustafa
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and
Technology (NUIST), Nanjing 210044, China
Lingbing Bu
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and
Technology (NUIST), Nanjing 210044, China
Shouzheng Zhu
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and
Technology (NUIST), Nanjing 210044, China
Key Laboratory of Space Laser Communication and Detection Technology,
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of
Sciences, Shanghai 201800, China
Jiqiao Liu
Key Laboratory of Space Laser Communication and Detection Technology,
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of
Sciences, Shanghai 201800, China
Weibiao Chen
Key Laboratory of Space Laser Communication and Detection Technology,
Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of
Sciences, Shanghai 201800, China
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Long-term lidar wind measurements can be biased by slow changes in the laser and frequency reference. We developed an internal tracking method that measures these changes during observations and corrects the resulting wind-speed errors. Tests showed stable detection of megahertz-scale drift and good agreement with independent measurements and theoretical values. The method improves the accuracy and stability of long-term wind observations while reducing reliance on external instruments.
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This study uses one year of observations from the DQ-1 spaceborne integrated path differential absorption (IPDA) lidar to evaluate the random errors of atmospheric carbon dioxide retrievals and their reduction through along-track averaging. We show that random errors vary with surface type and recommend different averaging numbers and a dynamic averaging strategy for land–sea boundaries. These results provide practical guidance for future spaceborne IPDA lidar carbon dioxide observations.
Yun Cao, Jian Shang, Lei Yang, Hongquan Yao, Chengbao Liu, Xiaopeng Zhu, Xinghui Zhao, Pan Huang, Jiqiao Liu, Jing Wang, and Zhiqing Zhang
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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We proposed the novel validation technique of the image navigation accuracy for FY-4A GIIRS using GLPS. The GLPS points at FY-4A satellite and continuously transmits laser signals. The laser signal is capable of propagating through the atmosphere and being captured by GIIRS. The laser tests demonstrate that EW deviations are within 0.35 pixel, and NS deviations are within 0.45 pixel. This validates that the navigation accuracy of GIIRS is adequate to support the needs of backend applications.
Jun Wang, Honghui Xu, Wei Xiao, Yuting Pang, Ning Hu, Jiaping Xu, Yibo Liu, Lingbing Bu, Chang Cao, Zhonghao Yang, Tianhao Wang, Lei Jia, Jinhui Wu, Mi Zhang, and Xuhui Lee
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We focused on CO2 and CH4 characteristics in urban atmospheric boundary layer (ABL). Results showed that CO2 and CH4 in the ABL were consistently higher than those in the free atmosphere. The inversion jump values of CO2 and CH4 over Beijing were both larger than those over Nanjing. Based on CH4 : CO2, we found that inventory might have overlooked the energy transition occurring in cities, while also demonstrating that one-dimensional slab model is inadequate for urban greenhouse gas budgets.
Pingyi Dong, Xingwen Jiang, Xingbing Zhao, Yuanchang Dong, Jiafeng Zheng, Chun Hu, Guolu Gao, Lei Liu, Shulei Li, and Lingbing Bu
Atmos. Meas. Tech., 19, 1407–1419, https://doi.org/10.5194/amt-19-1407-2026, https://doi.org/10.5194/amt-19-1407-2026, 2026
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A method is developed and validated for retrieving vertical profiles of the raindrop size distributions (DSD) parameters from a single-frequency Ka-band radar in this study. Some unique characteristics of the vertical profiles of DSD parameters in the eastern Tibetan Plateau are found. The empirical relationships for quantitative precipitation estimates and attenuation correction in the eastern Tibetan Plateau with Ka-band radar are derived.
Ziyu Bi, Jianbo Hu, Yuan Xie, Decang Bi, Xiaopeng Zhu, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 18, 7565–7580, https://doi.org/10.5194/amt-18-7565-2025, https://doi.org/10.5194/amt-18-7565-2025, 2025
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We developed an improved method to estimate the size and amount of aerosol particles using measurements from the world’s first spaceborne high spectral resolution lidar. Tests with different aerosol types show that the method can provide reliable results and improve the description of larger particles. This work demonstrates the potential of future satellite observations to deliver a clearer picture of global air pollution and its role in climate.
Xuanye Zhang, Hailong Yang, Lingbing Bu, Zengchang Fan, Wei Xiao, Binglong Chen, Lu Zhang, Sihan Liu, Zhongting Wang, Jiqiao Liu, Weibiao Chen, and Xuhui Lee
Atmos. Chem. Phys., 25, 6725–6740, https://doi.org/10.5194/acp-25-6725-2025, https://doi.org/10.5194/acp-25-6725-2025, 2025
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This study utilized the IPDA (integrated path differential absorption) lidar on board the DQ-1 satellite to monitor emissions from localized strong point sources and, for the first time, observed the diurnal variation in CO2 emissions from a high-latitude power plant. Overall, power plant CO2 emissions were largely consistent with local electricity consumption patterns, with most plants emitting less at night than during the day and with higher emissions in winter compared to spring and autumn.
Fanqian Meng, Junwu Tang, Guangyao Dai, Wenrui Long, Kangwen Sun, Zhiyu Zhang, Xiaoquan Song, Jiqiao Liu, Weibiao Chen, and Songhua Wu
Atmos. Meas. Tech., 18, 2021–2039, https://doi.org/10.5194/amt-18-2021-2025, https://doi.org/10.5194/amt-18-2021-2025, 2025
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This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter Aerosol and Carbon Detection Lidar (ACDL) on board DQ-1 by utilizing nighttime 532 nm multi-channel data. We analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results indicate that the uncertainty of the clear-air scattering ratio was within the anticipated range of 7.9 %.
Chenxing Zha, Lingbing Bu, Zhi Li, Qin Wang, Ahmad Mubarak, Pasindu Liyanage, Jiqiao Liu, and Weibiao Chen
Atmos. Meas. Tech., 17, 4425–4443, https://doi.org/10.5194/amt-17-4425-2024, https://doi.org/10.5194/amt-17-4425-2024, 2024
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China has launched the atmospheric environment monitoring satellite DQ-1, which consists of an advanced lidar system. Our research presents a retrieval algorithm of the DQ-1 lidar system, and the retrieval results are consistent with other datasets. We also use the DQ-1 dataset to investigate dust and volcanic aerosols. This research shows that the DQ-1 lidar system can accurately measure the Earth's atmosphere and has potential for scientific applications.
Kangwen Sun, Guangyao Dai, Songhua Wu, Oliver Reitebuch, Holger Baars, Jiqiao Liu, and Suping Zhang
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This paper investigates the correlation between marine aerosol optical properties and wind speeds over remote oceans using the spaceborne lidars ALADIN and CALIOP. Three remote ocean areas are selected. Pure marine aerosol optical properties at 355 nm are derived from ALADIN. The relationships between marine aerosol optical properties and wind speeds are analyzed within and above the marine atmospheric boundary layer, revealing the effect of wind speed on marine aerosols over remote oceans.
Guangyao Dai, Songhua Wu, Wenrui Long, Jiqiao Liu, Yuan Xie, Kangwen Sun, Fanqian Meng, Xiaoquan Song, Zhongwei Huang, and Weibiao Chen
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The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events. The heights of cloud top and bottom from these two measurements were well matched and comparable. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.
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Preprint archived
Short summary
Short summary
This paper presents a comprehensive calibration procedure for the first spaceborne high-spectral-resolution lidar with an iodine vapor absorption filter ACDL on board DQ-1 by utilizing nighttime 532 nm multi-channel data. And analyzed the error sources of the multi-channel calibration coefficients and assessed the results. The results shows that the ACDL polarization channel calibration is reliable and operates within the expected error range of approximately 5 %.
Farhan Mustafa, Lingbing Bu, Qin Wang, Na Yao, Muhammad Shahzaman, Muhammad Bilal, Rana Waqar Aslam, and Rashid Iqbal
Atmos. Meas. Tech., 14, 7277–7290, https://doi.org/10.5194/amt-14-7277-2021, https://doi.org/10.5194/amt-14-7277-2021, 2021
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A neural-network-based approach was suggested to estimate CO2 emissions using satellite-based net primary productivity (NPP) and XCO2 retrievals. XCO2 anomalies were calculated for each year using OCO-2 retrievals. A Generalized Regression Neural Network (GRNN) model was then built; NPP, XCO2 anomalies, and ODIAC CO2 emissions from 2015 to 2018 were used as a training dataset; and, finally, CO2 emissions were predicted for 2019 based on the NPP and XCO2 anomalies calculated for the same year.
Cited articles
Abshire, J., Ramanathan, A., Riris, H., Mao, J., Allan, G., Hasselbrack, W.,
Weaver, C., and Browell, E.: Airborne Measurements of CO2 Column
Concentration and Range Using a Pulsed Direct-Detection IPDA Lidar, Remote
Sens., 6, 443–469, https://doi.org/10.3390/rs6010443, 2013.
Abshire, J. B., Ramanathan, A. K., Riris, H., Allan, G. R., Sun, X., Hasselbrack, W. E., Mao, J., Wu, S., Chen, J., Numata, K., Kawa, S. R., Yang, M. Y. M., and DiGangi, J.: Airborne measurements of CO2 column concentrations made with a pulsed IPDA lidar using a multiple-wavelength-locked laser and HgCdTe APD detector, Atmos. Meas. Tech., 11, 2001–2025, https://doi.org/10.5194/amt-11-2001-2018, 2018.
Amediek, A., Ehret, G., Fix, A., Wirth, M., Büdenbender, C.,
Quatrevalet, M., Kiemle, C., and Gerbig, C.: CHARM-F – a new airborne
integrated-path differential-absorption lidar for carbon dioxide and methane
observations: measurement performance and quantification of strong point
source emissions, Appl. Opt., 56, 5182, https://doi.org/10.1364/AO.56.005182, 2017.
Araki, M., Morino, I., MacHida, T., Sawa, Y., Matsueda, H., Ohyama, H.,
Yokota, T., and Uchino, O.: CO2 column-averaged volume mixing ratio
derived over Tsukuba from measurements by commercial airlines, Atmos. Chem.
Phys., 10, 7659–7667, https://doi.org/10.5194/acp-10-7659-2010, 2010.
Babenhauserheide, A., Basu, S., Houweling, S., Peters, W., and Butz, A.: Comparing the CarbonTracker and TM5-4DVar data assimilation systems for CO2 surface flux inversions, Atmos. Chem. Phys., 15, 9747–9763, https://doi.org/10.5194/acp-15-9747-2015, 2015.
Baer, D. S., Paul, J. B., Gupta, M., and O'Keefe, A.: Sensitive absorption
measurements in the near-infrared region using off-axis
integrated-cavity-output spectroscopy, Appl. Phys. B, 75, 261–265,
https://doi.org/10.1007/s00340-002-0971-z, 2002.
Ballantyne, A. P., Alden, C. B., Miller, J. B., Trans, P. P., and White, J.
W. C.: Increase in observed net carbon dioxide uptake by land and oceans
during the past 50 years, Nature, 488, 70–73,
https://doi.org/10.1038/nature11299, 2012.
Crisp, D.: Measuring atmospheric carbon dioxide from space with the Orbiting Carbon Observatory-2 (OCO-2), In Earth Observing Systems, International Society for Optics and Photonics, Washington, DC, USA, Vol. 9607, 2015.
Crisp, D., Miller, C. E., and DeCola, P. L.: NASA Orbiting Carbon
Observatory: measuring the column averaged carbon dioxide mole fraction from
space, J. Appl. Remote Sens., 2, 1–14, https://doi.org/10.1117/1.2898457, 2008.
Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M., Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J., Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R., Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg, P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5, 687–707, https://doi.org/10.5194/amt-5-687-2012, 2012.
Dlugokencky, T. P. (Ed.): Trends in Atmospheric Carbon Dioxide, Noaa/Esrl,
available at: http://www.esrl.noaa.gov/gmd/ccgg/trends/global.ht
(last access: 3 May 2020), 2016.
Du, J., Zhu, Y., Li, S., Zhang, J., Sun, Y., Zang, H., Liu, D., Ma, X., Bi,
D., Liu, J., Zhu, X., and Chen, W.: Double-pulse 157 µm integrated path
differential absorption lidar ground validation for atmospheric carbon
dioxide measurement, Appl. Opt., 56, 7053, https://doi.org/10.1364/AO.56.007053,
2017.
Ehret, G., Kiemle, C., Wirth, M., Amediek, A., Fix, A., and Houweling, S.:
Space-borne remote sensing of CO2, CH4, and N2O by integrated
path differential absorption lidar: a sensitivity analysis, Appl. Phys. B,
90, 593–608, https://doi.org/10.1007/s00340-007-2892-3, 2008.
Gong, Y., Bu, L., Yang, B., and Mustafa, F.: High Repetition Rate
Mid-Infrared Differential Absorption Lidar for Atmospheric Pollution
Detection, Sensors, 20, 2211, https://doi.org/10.3390/s20082211, 2020.
Gordon, I. E., Rothman, L. S., Hill, C., Kochanov, R. V., Tan, Y., Bernath,
P. F., Birk, M., Boudon, V., Campargue, A., Chance, K. V., Drouin, B. J.,
Flaud, J.-M., Gamache, R. R., Hodges, J. T., Jacquemart, D., Perevalov, V.
I., Perrin, A., Shine, K. P., Smith, M.-A. H., Tennyson, J., Toon, G. C.,
Tran, H., Tyuterev, V. G., Barbe, A., Császár, A. G., Devi, V. M.,
Furtenbacher, T., Harrison, J. J., Hartmann, J.-M., Jolly, A., Johnson, T.
J., Karman, T., Kleiner, I., Kyuberis, A. A., Loos, J., Lyulin, O. M.,
Massie, S. T., Mikhailenko, S. N., Moazzen-Ahmadi, N., Müller, H. S. P.,
Naumenko, O. V., Nikitin, A. V., Polyansky, O. L., Rey, M., Rotger, M.,
Sharpe, S. W., Sung, K., Starikova, E., Tashkun, S. A., Auwera, J. Vander,
Wagner, G., Wilzewski, J., Wcisło, P., Yu, S., and Zak, E. J.: The
HITRAN2016 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 203, 3–69, https://doi.org/10.1016/j.jqsrt.2017.06.038, 2017.
Hedelius, J. K., Parker, H., Wunch, D., Roehl, C. M., Viatte, C., Newman,
S., Toon, G. C., Podolske, J. R., Hillyard, P. W., Iraci, L. T., Dubey, M.
K., and Wennberg, P. O.: Intercomparability of XCO2 and XCH4 from
the United States TCCON sites, Atmos. Meas. Tech., 10, 1481–1493,
https://doi.org/10.5194/amt-10-1481-2017, 2017.
Hungershoefer, K., Peylin, P., Chevallier, F., Rayner, P., Klonecki, A.,
Houweling, S., and Marshall, J.: Evaluation of various observing systems for
the global monitoring of CO2 surface fluxes, Atmos. Chem. Phys., 10,
10503–10520, https://doi.org/10.5194/acp-10-10503-2010, 2010.
Kawa, S. R., Mao, J., Abshire, J. B., Collatz, G. J., Sun, X., and Weaver, C.
J.: Simulation studies for a space-based CO2 lidar mission, Tellus B, 62, 759–769, https://doi.org/10.1111/j.1600-0889.2010.00486.x,
2010.
Kiel, M., O'Dell, C. W., Fisher, B., Eldering, A., Nassar, R., MacDonald, C. G., and Wennberg, P. O.: How bias correction goes wrong: measurement of X
Kong, Y., Chen, B., and Measho, S.: Spatio-temporal consistency evaluation of
XCO2 retrievals from GOSAT and OCO-2 based on TCCON and model data for
joint utilization in carbon cycle research, Atmosphere (Basel), 10,
1–23, https://doi.org/10.3390/atmos10070354, 2019.
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005.
Kulawik, S., Wunch, D., O'Dell, C., Frankenberg, C., Reuter, M., Oda, T., Chevallier, F., Sherlock, V., Buchwitz, M., Osterman, G., Miller, C. E., Wennberg, P. O., Griffith, D., Morino, I., Dubey, M. K., Deutscher, N. M., Notholt, J., Hase, F., Warneke, T., Sussmann, R., Robinson, J., Strong, K., Schneider, M., De Mazière, M., Shiomi, K., Feist, D. G., Iraci, L. T., and Wolf, J.: Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON, Atmos. Meas. Tech., 9, 683–709, https://doi.org/10.5194/amt-9-683-2016, 2016.
Lei, L., Zhong, H., He, Z., Cai, B., Yang, S., Wu, C., Zeng, Z., Liu, L., and
Zhang, B.: Assessment of atmospheric CO2 concentration enhancement
from anthropogenic emissions based on satellite observations, Kexue
Tongbao, Chin. Sci. Bull., 62, 2941–2950, https://doi.org/10.1360/N972016-01316,
2017.
Liang, A., Gong, W., Han, G., and Xiang, C.: Comparison of satellite-observed
XCO2 from GOSAT, OCO-2, and ground-based TCCON, Remote Sens., 9,
1–26, https://doi.org/10.3390/rs9101033, 2017.
Lindqvist, H., O'Dell, C. W., Basu, S., Boesch, H., Chevallier, F.,
Deutscher, N., Feng, L., Fisher, B., Hase, F., Inoue, M., Kivi, R., Morino,
I., Palmer, P. I., Parker, R., Schneider, M., Sussmann, R., and Yoshida, Y.:
Does GOSAT capture the true seasonal cycle of carbon dioxide?, Atmos. Chem.
Phys., 15, 13023–13040, https://doi.org/10.5194/acp-15-13023-2015, 2015.
Mao, J., Ramanathan, A., Abshire, J. B., Kawa, S. R., Riris, H., Allan, G.
R., Rodriguez, M., Hasselbrack, W. E., Sun, X., Numata, K., Chen, J., Choi,
Y., and Yang, M. Y. M.: Airborne lidar reflectance measurements at 1.57 µm
in support of the A-SCOPE mission for atmospheric CO2, Atmos. Meas.
Tech., 11, 127–140, https://doi.org/10.5194/amt-11-127-2018, 2018a.
Mao, J., Ramanathan, A., Abshire, J. B., Kawa, S. R., Riris, H., Allan, G.
R., Rodriguez, M., Hasselbrack, W. E., Sun, X., Numata, K., Chen, J., Choi,
Y., and Yang, M. Y. M.: Measurement of atmospheric CO2 column
concentrations to cloud tops with a pulsed multi-wavelength airborne lidar,
Atmos. Meas. Tech., 11, 127–140, https://doi.org/10.5194/amt-11-127-2018, 2018b.
Mendonca, J., Strong, K., Wunch, D., Toon, G. C., Long, D. A., Hodges, J. T., Sironneau, V. T., and Franklin, J. E.: Using a speed-dependent Voigt line shape to retrieve O2 from Total Carbon Column Observing Network solar spectra to improve measurements of XCO2, Atmos. Meas. Tech., 12, 35–50, https://doi.org/10.5194/amt-12-35-2019, 2019.
Mustafa, F., Bu, L., Wang, Q., Ali, M. A., Bilal, M., Shahzaman, M., and Qiu,
Z.: Multi-year comparison of CO2 concentration from NOAA carbon tracker
reanalysis model with data from GOSAT and OCO-2 over Asia, Remote Sens.,
12, 2498–2519, https://doi.org/10.3390/RS12152498, 2020.
Mustafa, F., Wang, H., Bu, L., Wang, Q., Shahzaman, M., Bilal, M., Zhou, M.,
Iqbal, R., Aslam, R. W., Ali, M. A., and Qiu, Z.: Validation of GOSAT and
OCO-2 against In Situ Aircraft Measurements and Comparison with
CarbonTracker and GEOS-Chem over Qinhuangdao, China, Remote Sens., 13,
899–914, https://doi.org/10.3390/rs13050899, 2021.
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C.,
Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie,
J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor,
T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval
algorithm – Part 1: Description and validation against synthetic observations,
Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012.
Paul, J. B., Lapson, L., and Anderson, J. G.: Ultrasensitive absorption
spectroscopy with a high-finesse optical cavity and off-axis alignment,
Appl. Opt., 40, 4904–4910, https://doi.org/10.1364/AO.40.004904, 2001.
Peters, W., Krol, M. C., Dlugokencky, E. J., Dentener, F. J., Bergamaschi, P.,
Dutton, G., Velthoven, P. V., Miller, J. B., Bruhwiler, L., and Tan, P. P.: Toward
regional-scale modeling using the two-way nested global model TM5:
Characterization of transport using SF6, J. Geophys. Res., 109, D19314,
https://doi.org/10.1029/2004JD005020, 2004.
Refaat, T. F., Singh, U. N., Yu, J., Petros, M., Ismail, S., Kavaya, M. J.,
and Davis, K. J.: Evaluation of an airborne triple-pulsed 2 µm IPDA
lidar for simultaneous and independent atmospheric water vapor and carbon
dioxide measurements, Appl. Opt., 54, 1387, https://doi.org/10.1364/AO.54.001387,
2015.
Refaat, T. F., Singh, U. N., Yu, J., Petros, M., Remus, R., and Ismail, S.:
Double-pulse 2-µm integrated path differential absorption lidar
airborne validation for atmospheric carbon dioxide measurement, Appl. Opt.,
55, 4232, https://doi.org/10.1364/AO.55.004232, 2016.
Santer, B. D., Painter, J. F., Bonfils, C., Mears, C. A., Solomon, S.,
Wigley, T. M. L., Gleckler, P. J., Schmidt, G. A., Doutriaux, C., Gillett,
N. P., Taylor, K. E., Thorne, P. W., and Wentz, F. J.: Human and natural
influences on the changing thermal structure of the atmosphere, P. Natl.
Acad. Sci. USA, 110, 17235–17240, https://doi.org/10.1073/pnas.1305332110,
2013.
Schultz, M. G., Akimoto, H., Bottenheim, J., Buchmann, B., Galbally, I. E.,
Gilge, S., Helmig, D., Koide, H., Lewis, A. C., Novelli, P. C.,
Plass-Dölmer, C., Ryerson, T. B., Steinbacher, M., Steinbrecher, R.,
Tarasova, O., Tørseth, K., Thouret, V., and Zellweger, C.: The global
atmosphere watch reactive gases measurement network, Elementa, 3, 1–23,
https://doi.org/10.12952/journal.elementa.000067, 2015.
Shan, Y., Guan, D., Zheng, H., Ou, J., Li, Y., Meng, J., Mi, Z., Liu, Z., and
Zhang, Q.: China CO2 emission accounts 1997–2015, Sci. Data, 5, 1–14, 1997.
Stocker, B. D., Roth, R., Joos, F., Spahni, R., Steinacher, M., Zaehle, S.,
Bouwman, L., Xu-Ri, and Prentice, I. C.: Multiple greenhouse-gas feedbacks
from the land biosphere under future climate change scenarios, Nat. Clim.
Change, 3, 666–672, https://doi.org/10.1038/nclimate1864, 2013.
Sun, X., Duan, M., Gao, Y., Han, R., Ji, D., Zhang, W., Chen, N., Xia, X.,
Liu, H., and Huo, Y.: In situ measurement of CO2 and CH4 from aircraft
over northeast China and comparison with OCO-2 data, Atmos. Meas. Tech.,
13, 3595–3607, https://doi.org/10.5194/amt-13-3595-2020, 2020.
Tellier, Y., Pierangelo, C., Wirth, M., Gibert, F., and Marnas, F.: Averaging bias correction for the future space-borne methane IPDA lidar mission MERLIN, Atmos. Meas. Tech., 11, 5865–5884, https://doi.org/10.5194/amt-11-5865-2018, 2018.
UNFCC: The United Nations Framework Convention on Climate Change, Review of European Community and International Environmental Law, 1, 270–277, 2006.
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, M.
C., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T.,
Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De
Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson,
J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold,
S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L.,
Podolske, J., Hillyard, P., Kawakami, S., Dubey, M. K., Parker, H. A.,
Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp,
D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2
(OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Tech., 10,
2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017.
Xie, J., Huang, X., Bu, L., Zhang, H., Mustafa, F., and Chu, C.: Detection of
water cloud microphysical properties using multi-scattering polarization
lidar, Curr. Opt. Photonics, 4, 174–185, https://doi.org/10.3807/COPP.2020.4.3.174,
2020.
Yang, S., Lei, L., Zeng, Z., He, Z., and Zhong, H.: An Assessment of
Anthropogenic CO2 Emissions by Satellite-Based Observations in China,
Sensors (Basel), 19, 1118, https://doi.org/10.3390/s19051118, 2019.
Tellier, Y., Pierangelo, C., Wirth, M., Gibert, F., and Marnas, F.: Averaging bias correction for the future space-borne methane IPDA lidar mission MERLIN, Atmos. Meas. Tech., 11, 5865–5884, https://doi.org/10.5194/amt-11-5865-2018, 2018.
Yokota, T., Yoshida, Y., Eguchi, N., Ota, Y., Tanaka, T., Watanabe, H., and
Maksyutov, S.: Global Concentrations of CO2 and CH4 Retrieved from
GOSAT: First Preliminary Results, Scientific Online Letters on the Atmosphere, 5, 160–163, 2009.
Yu, J., Petros, M., Singh, U. N., Refaat, T. F., Reithmaier, K., Remus, R.
G., and Johnson, W.: An Airborne 2-µm Double-Pulsed Direct-Detection
Lidar Instrument for Atmospheric CO2 Column Measurements, J. Atmos.
Ocean. Technol., 34, 385–400, https://doi.org/10.1175/JTECH-D-16-0112.1, 2017.
Zhang, X., Wang, F., Wang, W., Huang, F., Chen, B., Gao, L., Wang, S., Yan,
H., Ye, H., Si, F., Hong, J., Li, X., Cao, Q., Che, H., and Li, Z.: The
development and application of satellite remote sensing for atmospheric
compositions in China, Atmos. Res., 245, 105056,
https://doi.org/10.1016/j.atmosres.2020.105056, 2020.
Zhu, Y., Yang, J., Chen, X., Zhu, X., Zhang, J., Li, S., Sun, Y., Hou, X.,
Bi, D., Bu, L., Zhang, Y., Liu, J., and Chen, W.: Airborne Validation
Experiment of 1.57-µm Double-Pulse IPDA LIDAR for Atmospheric Carbon
Dioxide Measurement, Remote Sens., 12, 1999, https://doi.org/10.3390/rs12121999,
2020.
Short summary
In this work, an airborne experiment was carried out to validate a newly developed CO2 monitoring IPDA lidar against the in situ measurements obtained from a commercial CO2 monitoring instrument installed on an aircraft. The XCO2 values calculated with the IPDA lidar measurements were compared with the dry-air CO2 mole fraction measurements obtained from the in situ instruments, and the results showed a good agreement between the two datasets.
In this work, an airborne experiment was carried out to validate a newly developed CO2...
