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The global and multi-annual MUSICA IASI {H2O, δD} pair dataset
Título : The global and multi-annual MUSICA IASI {H2O, δD} pair dataset
Autor : Diekmann, ChristopherSchneider, MatthiasErtl, BenjaminHase, FrankGarcía Rodríguez, Omaira ElenaKhosrawi, FarahnazSepúlveda Hernández, EliezerKnippertz, PeterBraesicke, Peter
Palabras clave : Radiance measurements; Cycle of Atmospheric water; Isotopologues; Dataset; IASI
Fecha de publicación : 2021
Editor: Copernicus Publications
Citación : Earth System Science Data. 2021, 13(1), p. 5273–5292
Versión del editor: https://doi.org/10.5194/essd-13-5273-2021
Resumen : 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.
Patrocinador: 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).
URI : http://hdl.handle.net/20.500.11765/13439
ISSN : 1866-3508
1866-3516
Colecciones: Artículos científicos 2019-2022


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