Articles | Volume 6, issue 10
https://doi.org/10.5194/amt-6-2851-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-6-2851-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Semi-autonomous sounding selection for OCO-2
L. Mandrake
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
C. Frankenberg
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
C. W. O'Dell
Colorado State University, Fort Collins, CO, USA
G. Osterman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
P. Wennberg
California Institute of Technology, Pasadena, CA, USA
California Institute of Technology, Pasadena, CA, USA
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- High‐accuracy measurements of total column water vapor from the Orbiting Carbon Observatory‐2 R. Nelson et al. 10.1002/2016GL071200
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- OCO-3 early mission operations and initial (vEarly) XCO2 and SIF retrievals T. Taylor et al. 10.1016/j.rse.2020.112032
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- The Orbiting Carbon Observatory (OCO-2) tracks 2–3 peta-gram increase in carbon release to the atmosphere during the 2014–2016 El Niño P. Patra et al. 10.1038/s41598-017-13459-0
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- Estimating bias in the OCO-2 retrieval algorithm caused by 3-D radiation scattering from unresolved boundary layer clouds A. Merrelli et al. 10.5194/amt-8-1641-2015
- The Orbiting Carbon Observatory-2: first 18 months of science data products A. Eldering et al. 10.5194/amt-10-549-2017
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- XCO<sub>2</sub> retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm S. Noël et al. 10.5194/amt-14-3837-2021
- Evaluation and attribution of OCO-2 XCO<sub>2</sub> uncertainties J. Worden et al. 10.5194/amt-10-2759-2017
- Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes J. Mendonca et al. 10.5194/amt-14-7511-2021
- Observational evidence of 3‐D cloud effects in OCO‐2 CO2 retrievals S. Massie et al. 10.1002/2016JD026111
- A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) D. Wu et al. 10.5194/gmd-11-4843-2018
- Cloud Detection of SuperView-1 Remote Sensing Images Based on Genetic Reinforcement Learning X. Li et al. 10.3390/rs12193190
- The Significance of Fast Radiative Transfer for Hyperspectral SWIR XCO2 Retrievals P. Somkuti et al. 10.3390/atmos11111219
- The potential of clear-sky carbon dioxide satellite retrievals R. Nelson et al. 10.5194/amt-9-1671-2016
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