Please use this identifier to cite or link to this item:
The global and multi-annual MUSICA IASI {H2O, δD} pair dataset
Title: The global and multi-annual MUSICA IASI {H2O, δD} pair dataset
Authors: Diekmann, ChristopherSchneider, Matthias RESEARCHERID Ertl, BenjaminHase, FrankGarcía Rodríguez, Omaira Elena ORCID RESEARCHERID Autor AEMETKhosrawi, FarahnazSepúlveda Hernández, EliezerAutor AEMETKnippertz, PeterBraesicke, Peter
Keywords: Radiance measurements; Cycle of Atmospheric water; Isotopologues; Dataset; IASI
Issue Date: 2021
Publisher: Copernicus Publications
Citation: Earth System Science Data. 2021, 13(1), p. 5273–5292
Publisher version:
Abstract: We present a global and multi-annual space-borne dataset of tropospheric {H2O, δD} pairs that is based on radiance measurements from the nadir thermal infrared sensor IASI (Infrared Atmospheric Sounding Interferometer) on board the Metop satellites of EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites). This dataset is an a posteriori processed extension of the MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) IASI full product dataset as presented in Schneider et al. (2021b). From the independently retrieved H2O and δD proxy states, their a priori settings and constraints, and their error covariances provided by the IASI full product dataset, we generate an optimal estimation product for pairs of H2O and δD. Here, this standard MUSICA method for deriving {H2O, δD} pairs is extended using an a posteriori reduction of the constraints for improving the retrieval sensitivity at dry conditions.
Sponsorship : This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. 290612604, project MOTIV and grant no. 416767181, project TEDDY), the European Research Council, FP7 Ideas: European Research Council (MUSICA, grant no. 256961), the Bundesministerium für Bildung und Forschung (ForHLR supercomputer), the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (ForHLR supercomputer), and the Ministerio de Economía y Competitividad (grant no. CGL2016-80688-P, project INMENSE).
ISSN: 1866-3508
Appears in Collections:Artículos científicos 2019-2022

Files in This Item:
  File Description SizeFormat 
947,78 kBAdobe PDFThumbnail
Show full item record

Items in Arcimis are protected by Creative Commons License, unless otherwise indicated.

Arcimis Repository
Nota Legal Contacto y sugerencias