Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6889-2020
© Author(s) 2020. 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-13-6889-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2020
Research article |
| 18 Dec 2020
| 18 Dec 2020
Retrieved wind speed from the Orbiting Carbon Observatory-2
Robert R. Nelson
CORRESPONDING AUTHOR
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Annmarie Eldering
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
David Crisp
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Aronne J. Merrelli
Space Science and Engineering Center, University of Wisconsin–Madison, Madison, WI, USA
Christopher W. O'Dell
Cooperative Institute for Research in the Atmosphere, Fort Collins, CO, USA
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John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
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Joseph Mendonca, Ray Nassar, Christopher W. O'Dell, Rigel Kivi, Isamu Morino, Justus Notholt, Christof Petri, Kimberly Strong, and Debra Wunch
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Machine learning has become an important tool for pattern recognition in many applications. In this study, we used a neural network to improve the data quality of OCO-2 measurements made at northern high latitudes. The neural network was trained and used as a binary classifier to filter out bad OCO-2 measurements in order to increase the accuracy and precision of OCO-2 XCO2 measurements in the Boreal and Arctic regions.
Astrid Müller, Hiroshi Tanimoto, Takafumi Sugita, Toshinobu Machida, Shin-ichiro Nakaoka, Prabir K. Patra, Joshua Laughner, and David Crisp
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Over oceans, high uncertainties in satellite CO2 retrievals exist due to limited reference data. We combine commercial ship and aircraft observations and, with the aid of model calculations, obtain column-averaged mixing ratios of CO2 (XCO2) data over the Pacific Ocean. This new dataset has great potential as a robust reference for XCO2 measured from space and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.
Michael Buchwitz, Maximilian Reuter, Stefan Noël, Klaus Bramstedt, Oliver Schneising, Michael Hilker, Blanca Fuentes Andrade, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hartmut Boesch, Lianghai Wu, Jochen Landgraf, Ilse Aben, Christian Retscher, Christopher W. O'Dell, and David Crisp
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The COVID-19 pandemic resulted in reduced anthropogenic carbon dioxide (CO2) emissions during 2020 in large parts of the world. We have used a small ensemble of satellite retrievals of column-averaged CO2 (XCO2) to find out if a regional-scale reduction of atmospheric CO2 can be detected from space. We focus on East China and show that it is challenging to reliably detect and to accurately quantify the emission reduction, which only results in regional XCO2 reductions of about 0.1–0.2 ppm.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Christopher O'Dell, K. Sebastian Schmidt, Hong Chen, and David Baker
Atmos. Meas. Tech., 14, 1475–1499, https://doi.org/10.5194/amt-14-1475-2021, https://doi.org/10.5194/amt-14-1475-2021, 2021
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The OCO-2 science team is working to retrieve CO2 measurements that can be used by the carbon cycle community to calculate regional sources and sinks of CO2. The retrieved data, however, are in need of improvements in accuracy. This paper discusses several ways in which 3D cloud metrics (such as the distance of a measurement to the nearest cloud) can be used to account for cloud effects in the OCO-2 CO2 data files.
Nicole Jacobs, William R. Simpson, Debra Wunch, Christopher W. O'Dell, Gregory B. Osterman, Frank Hase, Thomas Blumenstock, Qiansi Tu, Matthias Frey, Manvendra K. Dubey, Harrison A. Parker, Rigel Kivi, and Pauli Heikkinen
Atmos. Meas. Tech., 13, 5033–5063, https://doi.org/10.5194/amt-13-5033-2020, https://doi.org/10.5194/amt-13-5033-2020, 2020
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The boreal forest is the largest seasonally varying biospheric CO2-exchange region on Earth. This region is also undergoing amplified climate warming, leading to concerns about the potential for altered regional carbon exchange. Satellite missions, such as the Orbiting Carbon Observatory-2 (OCO-2) project, can measure CO2 abundance over the boreal forest but need validation for the assurance of accuracy. Therefore, we carried out a ground-based validation of OCO-2 CO2 data at three locations.
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Short summary
Measurements of surface wind speed over oceans are scientifically useful. Here we show that the Orbiting Carbon Observatory-2 (OCO-2), originally designed to measure carbon dioxide using reflected sunlight, can also accurately and precisely measure wind speed. OCO-2's high spatial resolution means that it can observe close to coastlines and therefore be used to study coastal wind processes and inform related economic sectors.
Measurements of surface wind speed over oceans are scientifically useful. Here we show that the...
