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The PAZ Polarimetric Radio Occultation Research Dataset for Scientific Applications.

Authors :
Padullés, Ramon
Cardellach, Estel
Paz, Antía
Oliveras, Santi
Hunt, Douglas C.
Sokolovskiy, Sergey
Weiss, Jan P.
Wang, Kuo-Nung
Turk, F. Joe
Ao, Chi O.
Juárez, Manuel de la Torre
Source :
Earth System Science Data Discussions; 6/4/2024, p1-28, 28p
Publication Year :
2024

Abstract

Polarimetric Radio Occultations (PRO) represent an augmentation of the standard Radio Occultation (RO) technique that provides precipitation and clouds vertical information along with the standard thermodynamic products. A combined dataset that contains both the PRO observable and the RO standard retrievals, the resPrf , has been developed with the aim to foster the use of these unique observations and to fully exploit the scientific implication of having information about vertical cloud structures with intrinsically collocated thermodynamic state of the atmosphere. This manuscript describes such dataset and provides detailed information on the processing of the observations. The procedure followed at UCAR to combine both H and V observations to generate the equivalent profiles as in standard RO missions is described in detail, and the obtained refractivity is shown to be of equivalent quality as that from TerraSAR-X. The steps of the processing of the PRO observations are detailed, derived products such as the top-of-the-signal are described, and validation is provided. Furthermore, the dataset contains the simulated ray-trajectories for the PRO observation, and co-located information with global satellite-based precipitation products, such as merged rain rate retrievals or passive microwave observations. These co-locations are used for further validation of the PRO observations and they are also provided within the resPrf profiles for additional use. It is also shown how accounting for external co-located information can improve significantly the effective PRO horizontal resolution, tackling one of the challenges of the technique. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
RAINFALL
INFORMATION processing

Details

Language :
English
ISSN :
18663591
Database :
Complementary Index
Journal :
Earth System Science Data Discussions
Publication Type :
Academic Journal
Accession number :
177659709
Full Text :
https://doi.org/10.5194/essd-2024-150