Articles | Volume 10, issue 2
https://doi.org/10.5194/amt-10-507-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-507-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Feb 2017
Research article |
| 13 Feb 2017
MUSICA MetOp/IASI {H2O,δD} pair retrieval simulations for validating tropospheric moisture pathways in atmospheric models
Matthias Schneider
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
Christian Borger
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
Andreas Wiegele
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
Frank Hase
Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe Institute of Technology, Karlsruhe, Germany
Omaira E. García
Izaña Atmospheric Research Center, Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain
Eliezer Sepúlveda
Izaña Atmospheric Research Center, Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, Spain
Atmospheric Optics Group (GOA), University of Valladolid, Valladolid, Spain
Martin Werner
Alfred Wegener Institute (AWI), Bremerhaven, Germany
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Cited
13 citations as recorded by crossref.
- Potential of Mid‐tropospheric Water Vapor Isotopes to Improve Large‐Scale Circulation and Weather Predictability K. Toride et al. 10.1029/2020GL091698
- Disentangling different moisture transport pathways over the eastern subtropical North Atlantic using multi-platform isotope observations and high-resolution numerical modelling F. Dahinden et al. 10.5194/acp-21-16319-2021
- Assessing the potential of free-tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events M. Schneider et al. 10.5194/amt-17-5243-2024
- An unmanned aerial vehicle sampling platform for atmospheric water vapor isotopes in polar environments K. Rozmiarek et al. 10.5194/amt-14-7045-2021
- From climatological to small-scale applications: simulating water isotopologues with ICON-ART-Iso (version 2.3) J. Eckstein et al. 10.5194/gmd-11-5113-2018
- Improving weather forecasting by assimilation of water vapor isotopes M. Tada et al. 10.1038/s41598-021-97476-0
- Evaluation of MUSICA IASI tropospheric water vapour profiles using theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements C. Borger et al. 10.5194/amt-11-4981-2018
- Recent progress on the sources of continental precipitation as revealed by moisture transport analysis L. Gimeno et al. 10.1016/j.earscirev.2019.103070
- Isotopic Composition of Tropospheric Water Vapor in the Vicinity of St. Petersburg Y. Virolainen & K. Shpak 10.1134/S1024856023060234
- The Use of Water Vapor Isotopes to Determine Evapotranspiration Source Contributions in the Natural Environment T. Bernier 10.3390/w12113203
- Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature T. von Clarmann et al. 10.5194/amt-13-4393-2020
- The Climatological Impacts of Continental Surface Evaporation, Rainout, and Subcloud Processes on δD of Water Vapor and Precipitation in Europe E. Christner et al. 10.1002/2017JD027260
- The global and multi-annual MUSICA IASI {H2O, δD} pair dataset C. Diekmann et al. 10.5194/essd-13-5273-2021
13 citations as recorded by crossref.
- Potential of Mid‐tropospheric Water Vapor Isotopes to Improve Large‐Scale Circulation and Weather Predictability K. Toride et al. 10.1029/2020GL091698
- Disentangling different moisture transport pathways over the eastern subtropical North Atlantic using multi-platform isotope observations and high-resolution numerical modelling F. Dahinden et al. 10.5194/acp-21-16319-2021
- Assessing the potential of free-tropospheric water vapour isotopologue satellite observations for improving the analyses of convective events M. Schneider et al. 10.5194/amt-17-5243-2024
- An unmanned aerial vehicle sampling platform for atmospheric water vapor isotopes in polar environments K. Rozmiarek et al. 10.5194/amt-14-7045-2021
- From climatological to small-scale applications: simulating water isotopologues with ICON-ART-Iso (version 2.3) J. Eckstein et al. 10.5194/gmd-11-5113-2018
- Improving weather forecasting by assimilation of water vapor isotopes M. Tada et al. 10.1038/s41598-021-97476-0
- Evaluation of MUSICA IASI tropospheric water vapour profiles using theoretical error assessments and comparisons to GRUAN Vaisala RS92 measurements C. Borger et al. 10.5194/amt-11-4981-2018
- Recent progress on the sources of continental precipitation as revealed by moisture transport analysis L. Gimeno et al. 10.1016/j.earscirev.2019.103070
- Isotopic Composition of Tropospheric Water Vapor in the Vicinity of St. Petersburg Y. Virolainen & K. Shpak 10.1134/S1024856023060234
- The Use of Water Vapor Isotopes to Determine Evapotranspiration Source Contributions in the Natural Environment T. Bernier 10.3390/w12113203
- Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature T. von Clarmann et al. 10.5194/amt-13-4393-2020
- The Climatological Impacts of Continental Surface Evaporation, Rainout, and Subcloud Processes on δD of Water Vapor and Precipitation in Europe E. Christner et al. 10.1002/2017JD027260
- The global and multi-annual MUSICA IASI {H2O, δD} pair dataset C. Diekmann et al. 10.5194/essd-13-5273-2021
Latest update: 31 May 2025
Short summary
The characteristics of {H2O,δD} pair space-based remote sensing data depend on the atmospheric and surface conditions, which compromises their usage for model evaluation studies. This paper shows how the problem can be overcome by simulating MUSICA MetOp/IASI {H2O,δD} remote sensing products for any given model atmosphere. The remote sensing retrieval simulator is freely provided as a MATLAB and Python routine.
The characteristics of {H2O,δD} pair space-based remote sensing data depend on the atmospheric...
