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Griesche, Hannes; Seifert, Patric; Engelmann, Ronny; Radenz, Martin; Bühl, Johannes (2020): Cloudnet target categorization during PS106 [dataset]. Leibniz-Institut für Troposphärenforschung e.V., Leipzig, PANGAEA, https://doi.org/10.1594/PANGAEA.919344

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Abstract:
The dataset contains daily nc-files of the Cloudnet target categorization during Polarstern cruise PS106.
The data is retrieved using the instrument synergystic approach Cloudnet (Illingworth, 2007 doi:10.1175/BAMS-88-6-883 ).
This dataset is an aggregation of data from cloud radar, lidar, a numerical forecast model and optionally a rain gauge and microwave radiometer. It is intended to facilitate the application of synergistic cloud-retrieval algorithms by performing a number of the preprocessing tasks that are common to these algorithms. Each of the observational datasets has been interpolated onto the same grid, although the model data are provided on a reduced height grid. Radar reflectivity has been corrected for attenuation, where possible, and two additional fields have been added: \"category_bits\" contains a categorization of the targets in each pixel and \"quality_bits\" indicates the quality of the data at each pixel. Finally, estimates of the random and systematic errors in reflectivity factor and attenuated backscatter are provided.
Related to:
Griesche, Hannes; Seifert, Patric; Ansmann, Albert; Baars, Holger; Barrientos Velasco, Carola; Bühl, Johannes; Engelmann, Ronny; Radenz, Martin; Zhenping, Yin (in review): Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106. Atmospheric Measurement Techniques Discussions, https://doi.org/10.5194/amt-2019-434
Further details:
Illingworth, Anthony J; Hogan, Robin J; O'Connor, E J; Bouniol, D; Brooks, M E; Delanoé, J; Donovan, D P; Eastment, J D; Gaussiat, N; Goddard, J W F; Haeffelin, Martial; Klein Baltink, H; Krasnov, O A; Pelon, J; Piriou, J-M; Protat, Alain; Russchenberg, H W J; Seifert, A; Tompkins, A M; van Zadelhoff, G-J; Vinit, F; Willén, U; Wilson, D R; Wrench, C L (2007): Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations. Bulletin of the American Meteorological Society, 88(6), 883-898, https://doi.org/10.1175/BAMS-88-6-883
Project(s):
Funding:
German Research Foundation (DFG), grant/award no. 268020496: TRR 172: ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms
Coverage:
Median Latitude: 73.557710 * Median Longitude: 13.412315 * South-bound Latitude: 57.059650 * West-bound Longitude: 5.402260 * North-bound Latitude: 79.842110 * East-bound Longitude: 27.113210
Date/Time Start: 2017-05-24T00:00:00 * Date/Time End: 2017-07-18T00:00:00
Minimum Elevation: -512.0 m * Maximum Elevation: -46.6 m
Event(s):
PS106_0_Underway-11 * Latitude Start: 57.059650 * Longitude Start: 5.402260 * Latitude End: 79.842110 * Longitude End: 8.560310 * Date/Time Start: 2017-05-24T00:00:00 * Date/Time End: 2017-06-22T00:00:00 * Elevation Start: -46.6 m * Elevation End: -512.0 m * Campaign: PS106/1 (ARK-XXXI/1.1,PASCAL) * Basis: Polarstern * Method/Device: Remote sensing (Light detection and ranging, LiDAR) (LiDAR)
PS106_0_Underway-17 * Latitude Start: 77.971190 * Longitude Start: 12.573480 * Latitude End: 79.357890 * Longitude End: 27.113210 * Date/Time Start: 2017-06-23T16:15:00 * Date/Time End: 2017-07-15T20:00:00 * Elevation Start: -161.0 m * Elevation End: -286.0 m * Campaign: PS106/1 (ARK-XXXI/1.1,PASCAL) * Basis: Polarstern * Method/Device: Remote sensing (Light detection and ranging, LiDAR) (LiDAR)
Comment:
This is an updated version of this data set: https://doi.pangaea.de/10.1594/PANGAEA.899897
Parameter(s):
#NameShort NameUnitPrincipal InvestigatorMethod/DeviceComment
1Event labelEventGriesche, Hannes
2DATE/TIMEDate/TimeGriesche, HannesGeocode
3File nameFile nameGriesche, Hannes
4File formatFile formatGriesche, Hannes
5File sizeFile sizekByteGriesche, Hannes
6Uniform resource locator/link to fileURL fileGriesche, Hannes
Size:
220 data points

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