the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieval of greenhouse gases from GOSAT and GOSAT-2 using the FOCAL algorithm
Stefan Noël
Maximilian Reuter
Michael Buchwitz
Jakob Borchardt
Michael Hilker
Oliver Schneising
Heinrich Bovensmann
John P. Burrows
Antonio Di Noia
Robert J. Parker
Hiroshi Suto
Yukio Yoshida
Matthias Buschmann
Nicholas M. Deutscher
Dietrich G. Feist
David W. T. Griffith
Frank Hase
Rigel Kivi
Cheng Liu
Isamu Morino
Justus Notholt
Young-Suk Oh
Hirofumi Ohyama
Christof Petri
David F. Pollard
Markus Rettinger
Coleen Roehl
Constantina Rousogenous
Mahesh Kumar Sha
Kei Shiomi
Kimberly Strong
Ralf Sussmann
Voltaire A. Velazco
Mihalis Vrekoussis
Thorsten Warneke
Related authors
Cities need accurate emission estimates for climate action. We compared five emission inventories over the Greater Tokyo Area using measurements from four sites and an atmospheric transport model. We found that measurements at the four sites show distinct features of the inventories in emission amount and spatial distribution. Site choice is important for evaluating urban emissions. These findings can help improve emission inventories, future urban observations and simulations.
The Greenhouse Gases Observing Satellite-2 (GOSAT-2) is a satellite dedicated to measuring concentrations of greenhouse gases from space. Since its launch, the increase of CH4 and CO2 concentrations in the atmosphere is clear. The datasets obtained from GOSAT-2 are used in the Copernicus atmospheric services to monitor the climate, in light of the Paris Agreement. Here we present robust datasets of these gases from GOSAT-2, including a novel machine learning approach to data quality filtering.
Evaluation of measurement data – Guide to the expression of uncertainty in measurementissued by the JCGM, the error concept and the uncertainty concept are the same. Arguments in favor of the contrary were found not to be compelling. Neither was any evidence presented that
errorsand
uncertaintiesdefine a different relation between the measured and true values, nor is a Bayesian concept beyond the mere subjective probability referred to.