Dual-wavelength radar observations of precipitation at Ny-Ålesund (1 Sep. 2017 - 9 Oct. 2018, 14 June 2019 - 28 Feb. 2021)

DOI

This dataset contains time-height series of dual-wavelength radar reflectivity ratio (DWR) and mean Doppler velocity (MDV) from vertically-pointing observations of a 94-GHz cloud radar (RPG-FMCW-94-SP) and a 24-GHz Micro Rain Radar 2 (MRR-2), located at the AWIPEV observatory in Ny-Ålesund, Svalbard, Norway. This dataset is accompanying Chellini et al. (2022, doi:10.1029/2022JD036860), which contains detailed information on how this dataset was produced. DWR is dependent on the size of the ice particles in the radar volume (e.g., Hogan et al., 2000, doi:10.1175/1520-0426(2000)0172.0.CO;2), and combined with MDV it can provide information on the processes that lead to the formation of frozen precipitation (e.g., Dias Neto et al., 2019, doi:10.5194/essd-11-845-2019). Measurements are given for two time periods: from 1 September 2017 to 9 October. 2018, and from 14 June 2019 to 28 February 2021. Two different RPG-FMCW-94-SP cloud radar systems were used: MiRAC-A (Mech et al., 2019, doi:10.5194/amt-12-5019-2019) during the first period, and JOYRAD-94 (Küchler et al., 2017, doi:10.1175/JTECH-D-17-0019.1) during the second period. The same MRR-2 (https://metek.de/product/mrr-2/) instrument was used during both periods. Measurements from the 94-GHz cloud radars are averaged to the same time and range resolution as the MRR-2, and DWR values are obtained by dividing the MRR-2 radar reflectivity by the averaged 94-GHz cloud radar reflectivity, then converted to decibels. MDV reported is obtained from the 94-GHz cloud radars alone, and is also averaged to the MRR-2 resolution to enable a joint analysis with DWR. MRR-2 reflectivities were calibrated with a widely used disdrometer-based approach (e.g., Myagkov et al., 2020, doi:10.5194/amt-13-5799-2020), while the DWR values were calibrated using an approach similar to that by Dias Neto et al. (2019, doi:10.5194/essd-11-845-2019). Temperature time series from a co-located weather station (Vaisala WXT536) is also included. The data were collected in the framework of the Transregional Collaborative Research Center TR 172 “Arctic Amplification: Climate Relevant Atmospheric and Surface Processes and Feedback Mechanisms (AC)3” (https://www.ac3-tr.de/), funded by the German Research Fundation (Deutsche Forschungsgemeinschaft, DFG).

Identifier
DOI https://doi.org/10.1594/PANGAEA.943550
Related Identifier https://doi.org/10.1029/2022JD036860
Related Identifier https://doi.org/10.1175/JTECH-D-17-0019.1
Related Identifier https://doi.org/10.5194/amt-12-5019-2019
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.943550
Provenance
Creator Chellini, Giovanni ORCID logo; Gierens, Rosa ORCID logo; Kneifel, Stefan ORCID logo
Publisher PANGAEA
Publication Year 2022
Funding Reference German Research Foundation https://doi.org/10.13039/501100001659 Crossref Funder ID 268020496 https://gepris.dfg.de/gepris/projekt/268020496 TRR 172: ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Resource Type Dataset
Format text/tab-separated-values
Size 4 data points
Discipline Earth System Research
Spatial Coverage (11.930 LON, 78.920 LAT); Spitsbergen, Svalbard
Temporal Coverage Begin 2017-09-01T00:00:00Z
Temporal Coverage End 2021-02-28T00:00:00Z