Articles | Volume 11, issue 10
https://doi.org/10.5194/amt-11-5865-2018
https://doi.org/10.5194/amt-11-5865-2018
Research article
 | 
24 Oct 2018
Research article |  | 24 Oct 2018

Averaging bias correction for the future space-borne methane IPDA lidar mission MERLIN

Yoann Tellier, Clémence Pierangelo, Martin Wirth, Fabien Gibert, and Fabien Marnas

Related authors

Computation of longwave radiative flux and vertical heating rate with 4A-Flux v1.0 as an integral part of the radiative transfer code 4A/OP v1.5
Yoann Tellier, Cyril Crevoisier, Raymond Armante, Jean-Louis Dufresne, and Nicolas Meilhac
Geosci. Model Dev., 15, 5211–5231, https://doi.org/10.5194/gmd-15-5211-2022,https://doi.org/10.5194/gmd-15-5211-2022, 2022
Short summary

Related subject area

Subject: Gases | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images
Blanca Fuentes Andrade, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, Andreas Richter, Hartmut Boesch, and John P. Burrows
Atmos. Meas. Tech., 17, 1145–1173, https://doi.org/10.5194/amt-17-1145-2024,https://doi.org/10.5194/amt-17-1145-2024, 2024
Short summary
The GeoCarb greenhouse gas retrieval algorithm: simulations and sensitivity to sources of uncertainty
Gregory R. McGarragh, Christopher W. O'Dell, Sean M. R. Crowell, Peter Somkuti, Eric B. Burgh, and Berrien Moore III
Atmos. Meas. Tech., 17, 1091–1121, https://doi.org/10.5194/amt-17-1091-2024,https://doi.org/10.5194/amt-17-1091-2024, 2024
Short summary
Airborne lidar measurements of atmospheric CO2 column concentrations to cloud tops made during the 2017 ASCENDS/ABoVE campaign
Jianping Mao, James B. Abshire, S. Randy Kawa, Xiaoli Sun, and Haris Riris
Atmos. Meas. Tech., 17, 1061–1074, https://doi.org/10.5194/amt-17-1061-2024,https://doi.org/10.5194/amt-17-1061-2024, 2024
Short summary
Airborne observation with a low-cost hyperspectral instrument: retrieval of NO2 vertical column densities (VCDs) and the satellite sub-grid variability over industrial point sources
Jong-Uk Park, Hyun-Jae Kim, Jin-Soo Park, Jinsoo Choi, Sang Seo Park, Kangho Bae, Jong-Jae Lee, Chang-Keun Song, Soojin Park, Kyuseok Shim, Yeonsoo Cho, and Sang-Woo Kim
Atmos. Meas. Tech., 17, 197–217, https://doi.org/10.5194/amt-17-197-2024,https://doi.org/10.5194/amt-17-197-2024, 2024
Short summary
A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10
William R. Keely, Steffen Mauceri, Sean Crowell, and Christopher W. O'Dell
Atmos. Meas. Tech., 16, 5725–5748, https://doi.org/10.5194/amt-16-5725-2023,https://doi.org/10.5194/amt-16-5725-2023, 2023
Short summary

Cited articles

Bösenberg, J.: Ground-based differential absorption lidar for water-vapor and temperature profiling: methodology, Appl. Optics, 37, 3845–3860, https://doi.org/10.1364/AO.37.003845, 1998. 
Chéruy, F., Scott, N. A., Armante, R., Tournier, B., and Chedin, A.: Contribution to the development of radiative transfer models for high spectral resolution observations in the infrared, J. Quant. Spectrosc. Ra., 53, 597–611, https://doi.org/10.1016/0022-4073(95)00026-H, 1995. 
Chevallier, F., Chédin, A., Chéruy, F., and Morcrette, J.-J.: TIGR-like atmospheric-profile databases for accurate radiative-flux computation, Q. J. Roy. Meteor. Soc., 126, 777–785, https://doi.org/10.1002/qj.49712656319, 2000. 
Chevallier, F., Broquet, G., Pierangelo, C., and Crisp, D.: Probabilistic global maps of the CO2 column at daily and monthly scales from sparse satellite measurements, J. Geophys. Res., 122, 7614–7629, https://doi.org/10.1002/2017JD026453, 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.-Lasers O., 90, 593–608, https://doi.org/10.1007/s00340-007-2892-3, 2008. 
Download
Short summary
The French and German space agencies (CNES, DLR) are currently developing MERLIN, a satellite that will measure atmospheric concentration of methane, a powerful greenhouse gas. To reach the desired precision, horizontally averaging the measurements along the satellite track is performed but leads to a processing bias due to non-linear equations. This article studies the processing biases for several averaging schemes and bias correction algorithms and recommends a best approach to limit biases.