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  • 1
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    American Meteorological Society
    In:  Journal of Climate, 30 (22). pp. 8913-8927.
    Publication Date: 2019-02-01
    Description: The regional climate model COSMOin Climate Limited-AreaMode (COSMO-CLM or CCLM) is used with a high resolution of 15km for the entire Arctic for all winters 2002/03–2014/15. The simulations show a high spatial and temporal variability of the recent 2-m air temperature increase in the Arctic. The maximum warming occurs north of Novaya Zemlya in the Kara Sea and Barents Sea between March 2003 and 2012 and is responsible for up to a 208C increase. Land-based observations confirm the increase but do not cover the maximum regions that are located over the ocean and sea ice.Also, the 30-km version of theArctic SystemReanalysis (ASR) is used to verify the CCLM for the overlapping time period 2002/03–2011/12. The differences between CCLM and ASR 2-m air temperatures vary slightly within 18C for the ocean and sea ice area. Thus,ASR captures the extreme warming as well. The monthly 2-m air temperatures of observations and ERA-Interim data show a large variability for the winters 1979–2016. Nevertheless, the air temperature rise since the beginning of the twenty-first century is up to 8 times higher than in the decades before. The sea ice decrease is identified as the likely reason for the warming. The vertical temperature profiles show that the warming has a maximum near the surface, but a 0.58Cyr21 increase is found up to 2 km. CCLM, ASR, and also the coarser resolved ERA-Interim data show that February and March are the months with the highest 2-m air temperature increases, averaged over the ocean and sea ice area north of 708N; for CCLM the warming amounts to an average of almost 58C for 2002/03–2011/12.
    Type: Article , PeerReviewed
    Format: text
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  • 2
    Publication Date: 2019-12-04
    Description: This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [6], averaging time [8 sec], chosen SNR threshold [-20 dB].
    Type: Dataset
    Format: text/tab-separated-values, 21889 data points
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  • 3
    Publication Date: 2019-12-04
    Description: This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [6], averaging time [8 sec], chosen SNR threshold [-20 dB].
    Type: Dataset
    Format: text/tab-separated-values, 17117 data points
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  • 4
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    PANGAEA
    In:  Supplement to: Zentek, Rolf; Kohnemann, Svenja H E; Heinemann, Günther (2018): Analysis of the performance of a ship-borne scanning wind lidar in the Arctic and Antarctic. Atmospheric Measurement Techniques, 11(10), 5781-5795, https://doi.org/10.5194/amt-11-5781-2018
    Publication Date: 2019-12-04
    Description: This data set includes vertical profiles of wind speed (FF), wind direction (DD), fit deviation (FD) and the backscatter confident (BB) measured by a ship born wind lidar. The definition of the fit deviation, the main processing of the lidar data and an evaluation of the measurements is described in Zentek et al. (2018; doi:10.5194/amt-11-5781-2018 ). For this data set winds were computed every 50 m up to 1000 m and the data is averaged over time. The averaging time is one hour (+-30min around each full hour) and missing values are removed. A weighted arithmetic mean was used for the u- and v-component as well as for the fit deviation with the weights "1/fit deviation". The backscatter coefficient was averaged without weights. As backscatter was always measured, hours were included even if no wind could be computed due to atmospheric conditions but hours with no reliable data were excluded (e.g. the lidar was turned off; the ship was rocking to hard; etc.). Further detailed information for this measurement campaign: number of rays per VAD [8], averaging time [1.5 sec], chosen SNR threshold [-17 dB].
    Type: Dataset
    Format: text/tab-separated-values, 16536 data points
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  • 5
    Publication Date: 2018-10-19
    Description: In the present study a non-motion-stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December 2015–January 2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement set-ups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings generally shows small root mean square deviation (bias) for wind speed of around 1ms−1 (0.1ms−1) and for wind direction of around 10∘ (1∘). The post-processing of the non-motion-stabilized data shows a comparably high quality to studies with motion-stabilized systems. Two case studies show that a flexible change in SNR threshold can be beneficial for special situations. Further the studies reveal that short-lived low-level jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion-stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.
    Print ISSN: 1867-1381
    Electronic ISSN: 1867-8548
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
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  • 6
    Publication Date: 2018-05-25
    Description: Profiles of wind speed and direction at high spatial and temporal resolution are fundamental meteorological quantities for studies of the atmospheric boundary layer. Ship-based Doppler lidar measurements can contribute to fill the data gap over oceans particularly in polar regions. In the present study a non-motion stabilized scanning Doppler lidar was operated on board of RV Polarstern in the Arctic (June 2014) and Antarctic (December–January 2015/2016). This is the first time that such a system measured on an icebreaker in the Antarctic. A method for a motion correction of the data in the post-processing is presented. The wind calculation is based on vertical azimuth display (VAD) scans with eight directions that pass a quality control. Additionally a method for an empirical signal-to-noise ratio (SNR) threshold is presented, which can be calculated for individual measurement setups. Lidar wind profiles are compared to total of about 120 radiosonde profiles and also to wind measurements of the ship. The performance of the lidar measurements in comparison with radio soundings shows generally small RMSD (bias) for wind speed of around 1ms−1 (0.1ms−1) and for wind direction of around 12° (6°). The postprocessing of the non-motion stabilized data shows a comparable good quality as studies with motion stabilized systems. Two case studies show that a flexible change of SNR can be beneficial for special situations. Further the studies reveal that short-lived Low-Level Jets in the atmospheric boundary layer can be captured by lidar measurements with a high temporal resolution in contrast to routine radio soundings. The present study shows that a non-motion stabilized Doppler lidar can be operated successfully on an icebreaker. It presents a processing chain including quality control tests and error quantification, which is useful for further measurement campaigns.
    Electronic ISSN: 1867-8610
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
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  • 7
    Publication Date: 2017-08-31
    Print ISSN: 0894-8755
    Electronic ISSN: 1520-0442
    Topics: Geography , Geosciences , Physics
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