Review status: this preprint is currently under review for the journal ESSD.
The MUSICA IASI {H2O, δD} pair product
Christopher J. Diekmann1,Matthias Schneider1,Benjamin Ertl1,2,Frank Hase1,Omaira García3,Farahnaz Khosrawi1,Eliezer Sepúlveda3,Peter Knippertz1,and Peter Braesicke1Christopher J. Diekmann et al.Christopher J. Diekmann1,Matthias Schneider1,Benjamin Ertl1,2,Frank Hase1,Omaira García3,Farahnaz Khosrawi1,Eliezer Sepúlveda3,Peter Knippertz1,and Peter Braesicke1
Received: 12 Mar 2021 – Accepted for review: 22 Mar 2021 – Discussion started: 30 Mar 2021
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) onboard 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. By applying this improved water isotopologue post-processing for all cloud-free MUSICA IASI retrievals, this yields a {H2O, δD} pair dataset for the whole period from October 2014 to June 2019 with a global coverage twice per day (local morning and evening overpass times). In total, the dataset covers more than 1200 million individually processed observations. The retrievals are most sensitivity to variations of {H2O, δD} pairs within the free troposphere, with up to 30 % of all retrievals containing vertical profile information in the {H2O, δD} pair product. After applying appropriate quality filters, the largest number of reliable pair data arises for tropical and subtropical summer regions, but also for higher latitudes there is a considerable amount of reliable data. Exemplary time-series over the Tropical Atlantic and West Africa are chosen to illustrates the potential of the MUSICA IASI {H2O, δD} pair data for atmospheric moisture pathway studiess. Finally, the dataset is referenced with the DOI 10.35097/415 (Diekmann et al., 2021).
This manuscript presents water vapor and its isotope (delta D) from the IASI satellite. The manuscript is well written, has a significant contribution to the community, and is worthy of prompt publication. I recommend it be accepted for publication, with a few minor revisions.
One of the main suggestions is whether provide a fully gridded product in different resolutions. For example, 0.25 degrees with 12-hour intervals or monthly. It would be great for users (like me). Currently, it is not friendly at all.
Second, can the authors compare IASI products with other satellite products (such as TES or AIRS, if there is an overlap)? I think it is easy for us to understand the difference among the datasets (products).
Minor comments:
L43: ? --> citation
L44: ? --> wrong citation
L393: 1X1 degree?
Table 1: is it possible to add the information about flag values in the table? e.g., what is the meaning of the value of 1?
We would like to thank the Editor and the two referees for coordinating the review process and providing the reviews. Our point-by-point replies to the individual comments of both referees are given in the attached pdf file.
MUSICA IASI water isotopologue pair product (a posteriori processing version 2)Diekmann, C. J., Schneider, M., and Ertl, B. https://doi.org/10.35097/415
Christopher J. Diekmann et al.
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The joint analysis of different stable water isotopes in water vapour is a powerful tool for investigating atmospheric moisture pathways. This paper presents a novel global and multi-annual dataset of H2O and HDO in mid-tropospheric water vapour by using data from the satellite sensor Metop/IASI. Due to its unique combination of coverage and resolution in space and time, this dataset is highly promising for studying the hydrological cycle and its representation in weather and climate models.
The joint analysis of different stable water isotopes in water vapour is a powerful tool for...
