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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

邮发代号 80-963

2019 Impact Factor: 1.62

Frontiers of Earth Science  2022, Vol. 16 Issue (1): 75-89   https://doi.org/10.1007/s11707-020-0838-9
  本期目录
Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019)
Shengming TANG1,2, Yun GUO3, Xu WANG3(), Jie TANG1, Tiantian LI1, Bingke ZHAO1, Shuai ZHANG1, Yongping LI1
1. Shanghai Typhoon Institute of China Meteorological Administration, Shanghai 200030, China
2. Key Laboratory of Numerical Modeling for Tropical Cyclones, China Meteorological Administration, Shanghai 200030, China
3. State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China
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Abstract

This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of<100 m is unreliable owing to the drift effect. Optimal agreement (at heights of>100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19 m·s1). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI<12 mm·h1), the DWL data below 300 m were considered valid, whereas for PI classed as a severe rainstorm (PI>90 mm·h1), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s1), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.

Key wordsLidar    WindCube    GPS sonde    Super Typhoon Lekima    precipitation
收稿日期: 2020-06-13      出版日期: 2022-03-04
Corresponding Author(s): Xu WANG   
 引用本文:   
. [J]. Frontiers of Earth Science, 2022, 16(1): 75-89.
Shengming TANG, Yun GUO, Xu WANG, Jie TANG, Tiantian LI, Bingke ZHAO, Shuai ZHANG, Yongping LI. Validation of Doppler Wind Lidar during Super Typhoon Lekima (2019). Front. Earth Sci., 2022, 16(1): 75-89.
 链接本文:  
https://academic.hep.com.cn/fesci/CN/10.1007/s11707-020-0838-9
https://academic.hep.com.cn/fesci/CN/Y2022/V16/I1/75
1 G P Agrawal (2010). Fiber-Optic Communication Systems, 4th ed. New York: John Wiley & Sons, Inc
2 A A Alford, J A Zhang, M I Biggerstaff, P Dodge, F D Marks, D J Bodine (2020). Transition of the hurricane boundary layer during the landfall of Hurricane Irene (2011). J Atmos Sci: JAS-D-19-0290.1
https://doi.org/10.1175/JAS-D-19-0290.1
3 J Barat, C Cot (1995). Accuracy analysis of Rubsonde-GPS wind sounding system. J Appl Meteorol, 34(5): 1123–1132
https://doi.org/10.1175/1520-0450(1995)034<1123:AAORGW>2.0.CO;2
4 Z Bu, Y Zhang, S Chen, G Pan, L Lu, C He (2014). Noise modeling by the trend of each range gate for coherent Doppler LIDAR. Opt Eng, 53 (6): 063109.063101–063109.063106
5 L R Bucci, C O’Handley, G D Emmitt, J A Zhang, K Ryan, R Atlas (2018). Validation of an airborne doppler wind lidar in tropical cyclones. Sensors (Basel), 18(12): 4288
https://doi.org/10.3390/s18124288 pmid: 30563181
6 M Courtney, R Wagner, P Lindelöw (2008). Testing and comparison of lidars for profile and turbulence measurements in wind energy. In: IOP Conference Series Earth and Environmental Science, 1(1): 012021
7 J Davis, C Collier, F Davies, R Burton, G Pearson, P Di Girolamo (2013). Vertical velocity observed by Doppler lidar during cops–a case study with a convective rain event. Meteorol Z (Berl), 22(4): 463–470
https://doi.org/10.1127/0941-2948/2013/0411
8 P Devara, P Raj, G Pandithurai, K Dani, R Maheskumar (2003). Relationship between lidar-based observations of aerosol content and monsoon precipitation over a tropical station, Pune, India. Meteorol Appl, 10(3): 253–262
https://doi.org/10.1017/S1350482703003050
9 D R Drew, J F Barlow, S E Lane (2013). Observations of wind speed profiles over Greater London, UK, using a Doppler lidar. J Wind Eng Ind Aerodyn, 121(121): 98–105
https://doi.org/10.1016/j.jweia.2013.07.019
10 G D Emmitt (2010). Airborne Doppler wind lidar investigations of western pacific typhoon genesis and evolution. IEEE International Geoscience & Remote Sensing Symposium, 2010
11 R G Frehlich, M J Kavaya (1991). Coherent laser radar performance for general atmospheric refractive turbulence. Appl Opt, 30(36): 5325–5352
https://doi.org/10.1364/AO.30.005325 pmid: 20717362
12 P Gasch, A Wieser, J K Lundquist, N Kalthoff (2020). An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions. Atmos Meas Tech, 13(3): 1609–1631
https://doi.org/10.5194/amt-13-1609-2020
13 GB/T 19201–2006 (2006). Grade of Tropical Cyclones. General Administration of Quality Supervision Inspection and Quaratine of the People’s Republic China. Beijing: Standards Press of China
14 S M Hannon (2000). Autonomous infrared Doppler radar: airport surveillance applications. Phys Chem Earth Pt B, 25(10–12): 1005–1011
https://doi.org/10.1016/S1464-1909(00)00143-X
15 F Köpp, R L Schwiesow, C Werner (1984). Remote measurements of boundary-layer wind profiles using a CW Doppler Lidar. J Appl Meteorol, 23(1): 148–154
https://doi.org/10.1175/1520-0450(1984)023<0148:RMOBLW>2.0.CO;2
16 V Kumer, J Reuder, B R Furevik (2014). A comparison of LiDAR and radiosonde wind measurements. Energy Procedia, 53: 214–220
https://doi.org/10.1016/j.egypro.2014.07.230
17 W C Lambert, G E Taylor (1998). Data quality assessment methods for the eastern range 915 MHz wind profiler network. NASA Contractor Report NASA/CR-1998-207906
18 R Lhermitte, D Atlas (1961). Precipitaion motion by pulse Doppler radar. In: 9th Weather Radar Conference. Boston: American Meteological Society, 218–223
19 J L Li, X Yu (2017). LiDAR technology for wind energy potential assessment: demonstration and validation at a site around Lake Erie. Energy Convers Manage, 144: 252–261
https://doi.org/10.1016/j.enconman.2017.04.061
20 J L Li, X F Wang, X Yu (2018). Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment. Appl Energy, 213: 469–485
https://doi.org/10.1016/j.apenergy.2018.01.063
21 G Matvienko, A Grishin, A Zilberman (1995). Correlation lidar measurements of meteorological characteristics in conditions of atmospheric condensation. In: European Symposium on Optics for Environmental & Public Safety. International Society for Optics and Photonics
22 A Peña, C B Hasager, S E Gryning, M Courtney, I Antoniou, T Mikkelsen (2009). Offshore wind profiling using light detection and ranging measurements. Wind Energy (Chichester Engl), 12(2): 105–124
https://doi.org/10.1002/we.283
23 M D Powell, P J Vickery, T A Reinhold (2003). Reduced drag coefficient for high wind speeds in tropical cyclones. Nature, 422(6929): 279–283
https://doi.org/10.1038/nature01481 pmid: 12646913
24 Z X Pu, L Zhang, G D Emmitt (2010). Impact of airborne Doppler wind lidar profiles on numerical simulations of a tropical cyclone. Geophys Res Lett, 37(5): L05801
https://doi.org/10.1029/2009GL041765
25 G J Rabadan, N P Schmitt, T Pistner, W Rehm (2010). Airborne lidar for automatic feedforward control of turbulent in-flight phenomena. J Aircr, 47(2): 392–403
https://doi.org/10.2514/1.44950
26 F M Ralph, P J Neiman, D W van de Kamp, D C Law (1995). Using spectral moment data from NOAA’s 404-MHz radar wind profilers to observe precipitation. Bull Am Meteorol Soc, 76(10): 1717–1739
https://doi.org/10.1175/1520-0477(1995)076<1717:USMDFN>2.0.CO;2
27 J R Roadcap, P J Mcnicholl, E H T Jr, M H Laird (2001). Comparison of CO2 Doppler lidar and GPS rawinsonde wind velocity measurements. Proceedings of Spie the International Society for Optical Engineering, 4376: 141–152
28 D A Smith, M Harris, A S Coffey, T Mikkelsen, H E Jorgensen, J Mann, G Danielian (2006). Wind lidar evaluation at the Danish wind test site in Hovsore. Wind Energy (Chichester Engl), 9(1–2): 87–93
https://doi.org/10.1002/we.193
29 K Träumner, A Wieser, J Grenzhäuser, C Kottmeierr (2009). Advantages of a coordinated scanning Doppler lidar and cloud radar system for wind measurements. In: 4th symposium on lidar atmospheric applications, Arizona, USA
30 C Werner (1985). Fast sector scan and pattern recognition for a cw laser Doppler anemometer. Appl Opt, 24(21): 3557–3564
https://doi.org/10.1364/AO.24.003557 pmid: 18224087
31 D E Wolfe, C W Fairall, J M Intrieri, M Ratterree, S Tucker (2005). Shipboard multisensor wind profiles from NEAQS 2004: radar wind profiler, high resolution Doppler lidar, GPS rawinsonde. In: 13th Symposium on Meteorological Observations and Instrumentation, Joint Poster Session JP2.27
32 M Ying, W Zhang, H Yu, X Q Lu, J X Feng, Y X Fan, Y T Zhu, D Q Chen (2014). An overview of the China meteorological administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301
https://doi.org/10.1175/JTECH-D-12-00119.1
33 H Yu, L S Chen (2019). Impact assessment of landfalling tropical cyclones: introduction to the special issue. Front Earth Sci, 13(4): 669–671
https://doi.org/10.1007/s11707-019-0809-1
34 J A Zhang, R Atlas, G D Emmitt, L Bucci, K Ryan (2018). Airborne Doppler wind lidar observations of the tropical cyclone boundary layer. Remote Sens, 10(6): 825
https://doi.org/10.3390/rs10060825
35 J A Zhang, J P Dunion, D S Nolan (2020). In situ observations of the diurnal variation in the boundary layer of mature hurricanes. Geophysical Research Letters, 47 (3)
36 C Zheng, Z Gao, Z Rui, X Chen (2015). Application of T639 forecasting wind field around Taiwan Island. Journal of PLA University of Science & Technology, 16(1): 80–88
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