A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d'Urville, Adélie Land, East Antarctica

• Published in: Earth System Science Data
• Main Authors: Wiener, Valentin, Roussel, Marie-Laure, Genthon, Christophe, Vignon, Étienne, Grazioli, Jacopo, Berne, Alexis
• Format: Text
• Language: English
• Published: 2024
• Subjects: Antarc*; Antarctica; East Antarctica
• Online Access: https://doi.org/10.5194/essd-16-821-2024; https://essd.copernicus.org/articles/16/821/2024/

Studying precipitation falling over Antarctica is crucial as snowfall represents the main water input term for the polar cap. However, precipitation observations still remain scarce – and, more particularly, in the atmospheric column – due to numerous experimental issues related to the white continent. This paper aims at helping to close this observation gap by presenting 7 years of Micro Rain Radar (Metek MRR-2) data at the Dumont d'Urville station in coastal Adélie Land, East Antarctica. Statistics are calculated on three radar variables (equivalent reflectivity, mean Doppler velocity and signal-to-noise ratio (SNR)) to outline the main characteristics of the radar dataset. Seasonal and interannual variabilities are also investigated, but no significant temporal trends are detected, except for the seasonal mean Doppler velocity, which is higher in summer and lower in winter. We then use the snowfall rate ( S ) data from a collocated snow gauge to estimate the MRR precipitation profile from the radar equivalent reflectivity ( Z e ) through a locally derived Z e – S relation. We find the relation Z e =43.3 S 0.88 . The processing method used to obtain this relation, data quality and uncertainty considerations are discussed in the paper. In order to give an example of application of the dataset, a brief statistical comparison of the MRR precipitation rate along the vertical with model data from the ERA5 reanalysis and the LMDZ climate model is performed, which notably shows that models underestimate heavy precipitation events. All datasets are available on the PANGAEA database with the associated DOI: https://doi.org/10.1594/PANGAEA.962727 ( Wiener et al. , 2023 ) .