2026-06-02T05:29:59Zhttps://www.radar-service.eu/oai/OAIHandler10.35097/4922025-10-21T06:06:05ZIMKradar4kit
10.35097/492
Diekmann, Christopher J.
Christopher J.
Diekmann
0000-0002-8961-5241
Karlsruhe Institute of Technology (KIT)
Schneider, Matthias
Matthias
Schneider
0000-0001-8452-0035
Karlsruhe Institute of Technology (KIT)
Ertl, Benjamin
Benjamin
Ertl
0000-0003-1431-2243
Karlsruhe Institute of Technology (KIT)
Data for "Disentangling different moisture transport pathways over the eastern subtropical North Atlantic using multi-platform isotope observations and high-resolution numerical modelling"
Institute of Meteorology and Climate Research, Atmospheric Trace Gases and Remote Sensing (IMK-ASF), Karlsruhe Institute of Technology (KIT)
2019
2021
Geological Science
Atmosphere
Remote Sensing, Satellite
Metop, IASI, MUSICA
Atmospheric Composition, H2O, HDO, Water Vapour, Water Vapour Isotopologues
Open Access
Creative Commons Attribution 4.0 International
Christopher J. Diekmann
Matthias Schneider
Benjamin Ertl
Karlsruhe Institute of Technology (KIT)
Dahinden, Fabienne
ETH Zurich
This is the MUSICA IASI dataset of {H2O, dD} pairs used for the publication "Disentangling different moisture transport pathways over the eastern subtropical North Atlantic using multi-platform isotope observations and high-resolution numerical modelling" (Dahinden et al., 2021, https://doi.org/10.5194/acp-2021-269). It includes the MUSICA IASI {H2O, dD} pair results over Tenerife for the period July - August 2013. Information about the MUSICA IASI dataset can be found in Diekmann et al. (2021, https://doi.org/10.5194/essd-2021-87).
Metop IASI
en
Deutsche Forschungsgemeinschaft
501100001659
290612604
MOTIV
Deutsche Forschungsgemeinschaft
501100001659
416767181
TEDDY
European Research Council
501100000781
ERC Grant Agreement number 256961
MUSICA
Ministerio de Economía y Competitividad
501100003329
CGL2016-80688-P
INMENSE
Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg
501100003542
ForHLR supercomputer
Bundesministerium für Bildung und Forschung
501100002347
ForHLR supercomputer
1,1 GB
application/x-tar