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Vista previaFecha de publicaciónTítuloAutor(es)
essd-2021-87.pdf.jpg2021The MUSICA IASI {H2O, δD} pair productDiekmann, Christopher; Schneider, Matthias; Ertl, Benjamin, ...
amt-2021-36.pdf.jpg2021Validation of methane and carbon monoxide from Sentinel-5 Precursor using TCCON and NDACC-IRWG stationsSha, Mahesh Kumar; Langerock, Bavo; Blavier, Jean-François L., ...
ACP_Tu_2022_compressed.pdf.jpg2022Quantification of CH4 emissions from waste disposal sites near the city of Madrid using ground- and space-based observations of COCCON, TROPOMI and IASITu, Qiansi; Hase, Frank; Schneider, Matthias, ...
essd-2021-247.pdf.jpg2021An eleven year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithmTaylor, Thomas E.; O'Dell, Christopher; Crisp, David, ...
amt-7-3071-2014.pdf.jpg2014Quality assessment of ozone total column amounts as monitored by ground-based solar absorption spectrometry in the near infrared (> 3000 cm−1)García Rodríguez, Omaira Elena; Schneider, Matthias; Hase, F., ...
AMT_Garcia_2022.pdf.jpg2022Impact of instrumental line shape characterization on ozone monitoring by FTIR spectrometryGarcía Rodríguez, Omaira Elena; Sanromá, Esther; Hase, Frank, ...
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, ...
Risi_2012-JGRSE(2).pdf.jpg2012Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopic observations: 2. Using isotopic diagnostics to understand the mid and upper tropospheric moist bias in the tropics and subtropicsRisi, Camille; Noone, David; Worden, John, ...
amt-7-2719-2014.pdf.jpg2014The MUSICA MetOp/IASI H2O and δD products: characterisation and long-term comparison to NDACC/FTIR dataWiegele, Andreas; Schneider, Matthias; Hase, F., ...
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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