scholarly journals Evaluation of CO2 Greenhouse Gas Estimation Algorithms Based on GOSAT Satellite Data and Ground-based Observation Stations

2020 ◽  
Vol 12 (3) ◽  
pp. 23-36
Author(s):  
Samira Karbasi ◽  
Hossein Malakooti ◽  
Mehdi Rahnama ◽  
Majid Azadi
Keyword(s):  
Greenhouse Gas ◽  
Satellite Data ◽  
Estimation Algorithms ◽  
Gosat Satellite

scholarly journals Comparisons of CH<sub>4</sub> ground-based FTIR measurements near Saint Petersburg with GOSAT observations

2014 ◽  
Vol 7 (4) ◽  
pp. 1003-1010 ◽  
Author(s):  
N. M. Gavrilov ◽  
M. V. Makarova ◽  
A. V. Poberovskii ◽  
Yu. M. Timofeyev
Keyword(s):  
Fourier Transform ◽  
Greenhouse Gases ◽  
Satellite Data ◽  
Fourier Transform Infrared ◽  
Similar Data ◽  
Saint Petersburg ◽  
Spectroscopic Observations ◽  
Atmospheric Column ◽  
Standard Deviations ◽  
Gosat Satellite

Abstract. Atmospheric column-average methane mole fractions measured with ground-based Fourier-transform spectroscopy near Saint Petersburg, Russia (59.9° N, 29.8° E, 20 m a.s.l.) are compared with similar data obtained with the Japanese GOSAT (Greenhouse gases Observing SATellite) in the years 2009–2012. Average CH4 mole fractions for the GOSAT data version V01.xx are −15.0 ± 5.4 ppb less than the corresponding values obtained from ground-based measurements (with the standard deviations of biases at 13.0 ± 4.2 ppb). For the GOSAT data version V02.xx, the average values of the differences are −1.9 ± 1.8 ppb with standard deviations of 14.5 ± 1.3 ppb. This verifies that FTIR (Fourier transform infrared) spectroscopic observations near Saint Petersburg have similar biases with GOSAT satellite data as FTIR measurements at other ground-based networks and aircraft CH4 estimations.

scholarly journals Estimation of CO2 over the Ukraine based on GOSAT satellite data

2021 ◽  
Author(s):  
L. A. Yelistratova ◽  
A. A. Apostolov ◽  
I. F. Romanciuc
Keyword(s):  
Satellite Data ◽  
Gosat Satellite

Analysis of Total Columns of Greenhouse Gas Based on Direct Observation and Comparison with Satellite Data in Hefei

2020 ◽  
Vol 49 (3) ◽  
pp. 301002-301002
Author(s):  
刘丹丹 Dan-dan LIU ◽  
黄印博 Yin-bo HUANG ◽  
曹振松 Zhen-song CAO ◽  
卢兴吉 Xing-ji LU ◽  
孙宇松 Yu-song SUN ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Direct Observation ◽  
Satellite Data

scholarly journals The challenges of using satellite data sets to assess historical land use change and associated greenhouse gas emissions: a case study of three Indonesian provinces

2018 ◽  
Vol 9 (4) ◽  
pp. 399-413 ◽  
Author(s):  
Sybrand van Beijma ◽  
Julia Chatterton ◽  
Susan Page ◽  
Chris Rawlings ◽  
Richard Tiffin ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Satellite Data ◽  
Data Sets ◽  
Gas Emissions ◽  
Historical Land Use

Greenhouse gas column observations from a portable spectrometer in Uganda

2021 ◽  
Author(s):  
Neil Humpage ◽  
Hartmut Boesch ◽  
William Okello ◽  
Florian Dietrich ◽  
Jia Chen ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Greenhouse Gas ◽  
East Africa ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Methane Emissions ◽  
Geographical Region ◽  
Natural Ecosystems ◽  
Tropical Africa

&lt;p&gt;The natural ecosystems of tropical Africa represent a significant store of carbon, and play an important but uncertain role in the atmospheric budgets of carbon dioxide and methane. Recent studies using satellite data have concluded that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for a third of global methane growth (Lunt et al 2019), and that the tropical Africa region dominates net carbon emission across the tropics (Palmer et al 2019). The conclusions of such studies are based on the accuracy of various satellite datasets and atmospheric transport models, over a geographical region where there are few independent observations available to check the robustness and validity of these datasets.&lt;/p&gt;&lt;p&gt;Here we present the first ground-based observations of greenhouse gas (GHG) column concentrations over tropical East Africa, obtained using the University of Leicester EM27/SUN spectrometer during its deployment at the National Fisheries Resources Research Institute (NaFIRRI) in Jinja, Uganda. During the deployment we were able to operate the instrument remotely, using an automated weatherproof enclosure designed by the Technical University of Munich (Heinle and Chen 2018, Dietrich et al 2020). The instrument ran near-continuously for a three month period in early 2020, observing total atmospheric column concentrations of carbon dioxide and methane, along with other gases of interest including water vapour and carbon monoxide. We describe the data obtained during this period, processed using tools developed under the COCCON project (COllaborative Carbon Column Observing Network, Frey et al 2019), and demonstrate the value of performing GHG column measurements over tropical East Africa. We then evaluate the performance of CO&lt;sub&gt;2&lt;/sub&gt; observations from OCO-2 and CH&lt;sub&gt;4&lt;/sub&gt; from Sentinel 5P TROPOMI - datasets previously used in the studies of Palmer et al 2019 and Lunt et al 2019 respectively - and interpret the comparison with the ground-based observations in the light of data from the GEOS-Chem atmospheric chemistry transport model and the CAMS (Copernicus Atmospheric Monitoring Service) reanalyses.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;REFERENCES: &lt;/strong&gt;Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721&amp;#8211;14740, https://doi.org/10.5194/acp-19-14721-2019, 2019.&lt;/p&gt;&lt;p&gt;Palmer, P.I., Feng, L., Baker, D., Chevallier, F., Boesch, H., and Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat Commun 10, 3344, https://doi.org/10.1038/s41467-019-11097-w, 2019.&lt;/p&gt;&lt;p&gt;Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173&amp;#8211;2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.&lt;/p&gt;&lt;p&gt;Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discussions, 2020, 1&amp;#8211;24, https://doi.org/10.5194/amt-2020-300, 2020.&lt;/p&gt;&lt;p&gt;Frey, M. et al.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stabilityand ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513&amp;#8211;1530, https://doi.org/10.5194/amt-12-1513-2019, 2019&lt;/p&gt;

scholarly journals Quantifying sources of Brazil's CH<sub>4</sub> emissions between 2010 and 2018 from satellite data

2020 ◽  
Author(s):  
Rachel L. Tunnicliffe ◽  
Anita L. Ganesan ◽  
Robert J. Parker ◽  
Hartmut Boesch ◽  
Nicola Gedney ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Satellite Data ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Satellite Measurements ◽  
Ch4 Emissions ◽  
Surface Data ◽  
Sensitivity Studies ◽  
Using Data ◽  
Gosat Satellite

Abstract. Brazil's CH4 emissions over the period 2010–2018 were derived for the three main sectors of activity: anthropogenic, wetland and biomass burning. Our inverse modelling estimates were derived from GOSAT satellite measurements of XCH4 combined with surface data from Ragged Point, Barbados and the high-resolution regional atmospheric transport model NAME. We find that Brazil's mean emissions over 2010–2018 are 33.6 ± 3.6 Tg/yr, which are comprised of 19.0 ± 2.6 Tg/yr from anthropogenic (primarily related to agriculture and waste), 13.0 ± 1.9 Tg/yr from wetlands and 1.7 ± 0.3 Tg/yr from biomass burning sources. In addition, between the 2011–2013 and 2014–2018 periods, Brazil's mean emissions rose by 6.9 ± 5.3 Tg/yr and this increase may have contributed to the accelerated global methane growth rate observed during the latter period. We find that wetland emissions from the Western Amazon increased during the start of the 2015–16 El Nino by 3.7 ± 2.7 Tg/yr and this is likely driven by increased surface temperatures. We also find that our estimates of anthropogenic emissions are consistent with those reported by Brazil to the United Framework Convention on Climate Change. We show that satellite data is beneficial for constraining national-scale CH4 emissions, and, through a series of sensitivity studies and validation experiments using data not assimilated in the inversion, we demonstrate that calibrated ground-based data are important to include alongside satellite data in a regional inversion, and that inversions must account for any offsets between the two data streams and their representations by models.

scholarly journals Comparisons of CH<sub>4</sub> satellite GOSAT and ground-based FTIR measurements near Saint-Petersburg (59.9° N, 29.8° E)

2013 ◽  
Vol 6 (4) ◽  
pp. 7041-7062 ◽  
Author(s):  
N. M. Gavrilov ◽  
M. V. Makarova ◽  
A. V. Poberovskii ◽  
Yu. M. Timofeyev
Keyword(s):  
Fourier Transform ◽  
Standard Deviation ◽  
Satellite Data ◽  
Fourier Transform Spectroscopy ◽  
Similar Data ◽  
Atmospheric Methane ◽  
Average Difference ◽  
Saint Petersburg ◽  
Spectroscopic Observations ◽  
Gosat Satellite

Abstract. Atmospheric methane column-mean mole fractions measured with ground-based Fourier-transform spectroscopy near Saint-Petersburg, Russia (59.9° N, 29.8° E) are compared with similar data obtained with the Japanese GOSAT satellite in years 2009–2012. Average CH4 mole fractions for the GOSAT data version V01.xx are by −13 ppb less than the corresponding values obtained from ground-based measurements on the same date (with standard deviation ~ 26 ppb). For the GOSAT data version V02.xx the average difference is ~ 4 ppb and standard deviation ~ 15 ppb. This shows that FTIR spectroscopic observations near Saint-Petersburg could agree with GOSAT satellite data.

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