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Vista previaFecha de publicaciónTítuloAutor(es)
acp-2016-699.pdf.jpg2016Ten years of atmospheric methane from ground-based NDACC FTIR observations [Discussion paper]Bader, Whitney; Bovy, Benoît; Conway, Stephanie, ...
CompacLow_AIP1531_368_2013.pdf.jpg2013A compact and low resolution spectrometer for the inversion of water vapor total column amountsSepúlveda Hernández, Eliezer; Schneider, Matthias; Hase, Frank, ...
amt-8-1555-2015-1.pdf.jpg2015Using XCO2 retrievals for assessing the long-term consistency of NDACC/FTIR data setsBarthlott, Sabine; Schneider, Matthias; Hase, Frank, ...
amt-10-507-2017.pdf.jpg2017MUSICA MetOp/IASI {H2O,δD} pair retrieval simulations for validating tropospheric moisture pathways in atmospheric modelsSchneider, Matthias; Borger, Christian; Wiegele, Andreas, ...
essd-14-325-2022.pdf.jpg2022An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithmTaylor, Thomas E.; O’Dell, Christopher W.; Crisp, David, ...
amt-9-4471-2016.pdf.jpg2016Intercomparison of stratospheric nitrogen dioxide columns retrieved from ground-based DOAS and FTIR and satellite DOAS instruments over the subtropical Izana stationRobles González, Cristina; Navarro Comas, Mónica; Puentedura, Olga, ...
acp-17-2255-2017.pdf.jpg2017The recent increase of atmospheric methane from 10 years of ground-based NDACC FTIR observations since 2005Bader, Whitney; Bovy, Benoît; Conway, Stephanie, ...
amtd-8-13729-2015.pdf.jpg2015Consistency and quality assessment of the Metop-A/IASI and Metop-B/IASI operational trace gas products (O3, CO, N2O, CH4 and CO2) in the Subtropical North Atlantic [Discussion paper]García Rodríguez, Omaira Elena; Sepúlveda Hernández, Eliezer; Schneider, Matthias, ...
amt-9-2845-2016.pdf.jpg2016Accomplishments of the MUSICA project to provide accurate, long-term, global and high-resolution observations of tropospheric {H2O,δD} pairs – a reviewSchneider, Matthias; Wiegele, Andreas; Barthlott, Sabine, ...
amt-16-2237-2023.pdf.jpg2023Estimation of NO2 emission strengths over Riyadh and Madrid from space from a combination of wind-assigned anomalies and a machine learning techniqueTu, Qiansi; Hase, Frank; Chen, Zihan, ...
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