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A review of the technical system of spaceborne Doppler wind lidar and its assessment method

  • role： First author第一作者
  • Affiliation：

    College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

  • Email：xuejin.sun@outlook.com
  • Introduction：孙学金，研究方向为大气辐射与大气遥感。E-mail： xuejin.sun@outlook.com
SUN Xuejin1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

    Military unit 61741 of Chinese People's Liberation Army, Beijing 100081, China

  • Email：zhangcl2015@126.com
  • Introduction：张传亮，研究方向为激光雷达测风技究。E-mail： zhangcl2015@126.com
ZHANG Chuanliang12*,  
  • Affiliation：

    Military unit 61741 of Chinese People's Liberation Army, Beijing 100081, China

FANG Le2,  
  • Affiliation：

    College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

LU Wen1,  
  • Affiliation：

    College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

ZHAO Shijun1,  
  • Affiliation：

    College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China

YE Song1

résumé

Spaceborne Doppler Wind Lidars (DWL) are powerful tools in global wind observations. The first spaceborne Doppler wind lidar designed by European Space Agency (ESA) was launched successfully in August 2018. Meanwhile, the US and Japan provide huge resources in the demonstration and development of new technical systems of spaceborne DWLs, which are Hybrid DWL (HDWL) and Coherent DWL (CDWL), respectively. The technical systems of Aeolus, HDWL, and CDWL were assessed from three aspects, including the data acquisition rate or measurement number, the accuracy of wind observations, and the role played in improving the Numerical Weather Prediction (NWP) results to provide reference for our country to develop our own spaceborne DWL.We introduced the three technical systems briefly because the three technical systems of spaceborne DWLs are relatively different. These technical systems were assessed from the three aspects using previous research results.The three technical systems, which consist of Aeolus, HDWL, and CDWL, were assessed through the data acquisition rate or measurement number. Previous studies illustrated that the profiles of measurements obtained by HDWL is twice as much that of CDWL, and four times as much that of Aeolus. The data acquisition rate of CDWL is low due to its coherent-detection technology.The three technical systems are also assessed through the accuracy of wind observations. The main factors, which affect the accuracy of wind observations, are Poisson noise and atmospheric heterogeneity. Previous studies demonstrated that wind observations obtained by coherent-detection technology or the Mie channel of Aeolus has high accuracy (about 0—2 m/s) and traced by aerosol or cloud particles. However, its observations only cover about 30% of the total observations. The accuracy of wind observations obtained by direct detection is relatively low (about 1—3 m/s) and traced by molecules. Its observations can cover about 70% of the total observations. Generally, the global wind distributions can be well detected by combining coherent and direct detection.Observing System Simulation Experiments (OSSEs) provide a quantitative evaluation of new observing systems for the improvement of NWP. ESA, the US, and Japan verified the positive impact of Aeolus, HDWL, and CDWL on NWP results through OSSEs. Studies also indicate that uniform spaceborne DWL profile coverage is more important than the observations of horizontal vector wind using joint observations with two Aeolus-type spaceborne DWLs. Meanwhile, the observations of horizontal vector wind perform better in the improvement of the forecast results close to the satellite tracks than the observations of line-of-sight wind observations.HDWL is expected to achieve more favorable improvement of NWP forecast due to its larger data coverage and ability to observe the horizontal vector wind. The conclusions are drawn based on previous studies. Furthermore, HDWL and CDWL are still on the demonstration phase. Their parameters may be justified in the future, affecting the accuracy of wind observations. Future research on the comparison of the technical systems of spaceborne DWLs should be developed.

mots-clés

remote sensing;spaceborne Doppler wind lidar;Aeolus;HDWL;CDWL;data acquisition rate;accuracy of wind observations;NWP

References

  1. 1.
    Abreu V J. 1979. Wind measurements from an orbital platform using a lidar system with incoherent detection: an analysis. Applied Optics, 18(17): 2992-2997
  2. 2.
    Atlas R, Kalnay E and Halem M. 1985. Impact of satellite temperature sounding and wind data on numerical weather prediction. Optical Engineering, 24(2): 242341
  3. 3.
    Baars H, Geiß A, Wandinger U, Herzog A, Engelmann R, Bühl J, Radenz M, Seifert P, Ansmann A, Martin A, Leinweber R, Lehmann V, Weissmann M, Cress A, Filioglou M, Komppula M and Reitebuch O. 2019. First results from the German CAL/VAL activities for Aeolus//The 29th International Laser Radar Conference. Hefei, China: EDP Sciences: 01008 [DOI: 10.1051/epjconf/202023701008]
  4. 4.
    Baker W. 2008. Doppler wind lidar: current activities and future plans//Winter T-PARC Workshop. [s.l.]: NOAA/NASA/DoD Joint Center for Satellite Data Assimilation
  5. 5.
    Baker W E, Emmitt G D, Robertson F, Atlas R M, Molinari J E, Bowdle D A, Paegle J, Hardesty R M, Menzies R T, Krishnamurti T N, Brown R A, Post M J, Anderson J R, Lorenc A C and Mcelroy J. 1995. Lidar-measured winds from space: a key component for weather and climate prediction. Bulletin of the American Meteorological Society, 76(6): 869-888
  6. 6.
    Baron P, Ishii S, Okamoto K, Gamo K, Mizutani K, Takahashi C, Itabe T, Iwasaki T, Kubota T, Maki T, Oki R, Ochiai S, Sakaizawa D, Satoh M, Satoh Y, Tanaka T Y and Yasui M. 2017. Feasibility study for future spaceborne coherent Doppler wind lidar, part 2: measurement simulation algorithms and retrieval error characterization. Journal of the Meteorological Society of Japan, 95(5): 319-342
  7. 7.
    Barre H M J P, Duesmann B and Kerr Y H. 2008. SMOS: the mission and the system. IEEE Transactions on Geoscience and Remote Sensing, 46(3): 587-593
  8. 8.
    Beranek R G, Bilbro J W, Fitzjarrald D E, Jones W D, Keller V W and Perrine B S. 1989. Laser atmospheric wind sounder (LAWS)//Proceedings of SPIE 1062, Laser Applications in Meteorology and Earth and Atmospheric Remote Sensing. Los Angeles: SPIE: 234-248
  9. 9.
    Eastman R and Warren S G. 2014. Diurnal cycles of cumulus, cumulonimbus, stratus, stratocumulus, and fog from surface observations over land and ocean. Journal of Climate, 27(6): 2386-2404
  10. 10.
    Emmitt G D. 2004. Combining direct and coherent detection for Doppler wind lidar//Proceedings of SPIE 5575, Laser Radar Techniques for Atmospheric Sensing. Maspalomas: SPIE: 31-37
  11. 11.
    Endlich R M, Wolf D E, Hall D J and Brain A E. 1971. Use of a pattern recognition technique for determining cloud motions from sequences of satellite photographs. Journal of Applied Meteorology, 10(1): 105-117
  12. 12.
    Gaiser P W, St Germain K M, Twarog E M, Poe G A, Purdy W, Richardson D, Grossman W, Jones W L, Spencer D, Golba G, Cleveland J, Choy L, Bevilacqua R M and Chang P S. 2004. The WindSat spaceborne polarimetric microwave radiometer: sensor description and early orbit performance. IEEE Transactions on Geoscience and Remote Sensing, 42(11): 2347-2361
  13. 13.
    Hasinoff S W, Durand F and Freeman W T. 2010. Noise-optimal capture for high dynamic range photography//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE: 553-560
  14. 14.
    Ishii S, Baron P, Aoki M, Mizutani K, Yasui M, Ochiai S, Sato A, Satoh Y, Kubota T, Sakaizawa D, Oki R, Okamoto K, Ishibashi T, Tanaka T Y, Sekiyama T T, Maki T, Yamashita K, Nishizawa T, Satoh M and Iwasaki T. 2017. Feasibility study for future space-borne coherent Doppler wind lidar, part 1: instrumental overview for global wind profile observation. Journal of the Meteorological Society of Japan, 95(5): 301-317
  15. 15.
    Ishii S, Iwasaki T, Sato M, Oki R, Okamoto K, Ishibashi T, Baron P and Nishizawa T. 2012. Future Doppler lidar wind measurement from space in Japan//Proceedings of SPIE 8529, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions IV. Kyoto, Japan: SPIE: 85290A
  16. 16.
    Itabe T, Mizutani K, Ishizu M and Asai K. 2001. ISS/JEM-borne coherent Doppler lidar program to Measure the Wind from space//Proceedings of SPIE 4153, Lidar Remote Sensing for Industry and Environment Monitoring. Sendai, Japan: SPIE: 412-419
  17. 17.
    Kanitz T, Ciapponi A, Mondello A, D’Ottavi A, Mateo A B, Straume A G, Voland C, Bon D, Checa E, Alvarez E, Bellucci I, Do Carmo J P, Brewster J, Marshall J, Schillinger M, Hannington M, Rennie M, Reitebuch O, Lecrenier O, Bravetti P, Sacchieri V, De Sanctis V, Lefebvre A, Parrinello T and Wernham D. 2019. ESA's lidar missions Aeolus and EarthCare//The 29th International Laser Radar Conference. Hefei, China: EDP Sciences: 01006 [DOI: 10.1051/epjconf/202023701006]
  18. 18.
    Lahoz W A, Brugge R, Jackson D R, Migliorini S, Swinbank R, Lary D and Lee A. 2005. An observing system simulation experiment to evaluate the scientific merit of wind and ozone measurements from the future SWIFT instrument. Quarterly Journal of the Royal Meteorological Society, 131(606): 503-523
  19. 19.
    Leese J A, Novak C S and Clark B B. 1971. An automated technique for obtaining cloud motion from geosynchronous satellite data using cross correlation. Journal of Applied Meteorology, 10(1): 118-132
  20. 20.
    Liu Z Y, Hunt W, Vaughan M, Hostetler C, McGill M, Powell K, Winker D and Hu Y X. 2006. Estimating random errors due to shot noise in backscatter lidar observations. Applied Optics, 45(18): 4437-4447
  21. 21.
    Long Z W, Shi H Q and Huang S X. 2010. A new idea of cloud motion wind derived from satellite images. Acta Physica Sinica, 60(5): 059202
  22. 22.
    Lu W, Yan W, Shi J K and Ren J Q. 2010. A geolocation algorithm for WindSat. Remote Sensing Technology and Application, 25(1): 126-131
  23. 23.
    Ma X L, Luo P, Chen Z B and Cai M. 2014. A method for cloud motion wind vector prediction based on scale-invariant feature transform. Meteorological Science and Technology, 42(3): 391-396
  24. 24.
    Marseille G J and Stoffelen A. 2003. Simulation of wind profiles from a space-borne Doppler wind lidar. Quarterly Journal of the Royal Meteorological Society, 129(594): 3079-3098
  25. 25.
    Marseille G J, Stoffelen A and Barkmeijer J. 2008. Impact assessment of prospective spaceborne Doppler wind lidar observation scenarios. Tellus A: Dynamic Meteorology and Oceanography, 60(2): 234-248
  26. 26.
    Marx C T, Gentry B, Jordan P, Dogoda P, Faust E and Kavaya M. 2013. Lab demonstration of the hybrid Doppler wind lidar (HDWL) transceiver//Proceedings OF SPIE 8872, Lidar Remote Sensing for Environmental Monitoring XIV. San Diego: SPIE: 887207 [DOI: 10.1117/12.2029649]
  27. 27.
    Masutani M, Woollen J S, Lord S J, Emmitt G D, Kleespies T J, Wood S A, Greco S, Sun H B, Terry J, Kapoor V, Treadon R and Campana K A. 2010. Observing system simulation experiments at the National Centers for Environmental Prediction. Journal of Geophysical Research: Atmospheres, 115(D7): D07101
  28. 28.
    Min M and Zhang Z B. 2014. On the influence of cloud fraction diurnal cycle and sub-grid cloud optical thickness variability on all-sky direct aerosol radiative forcing. Journal of Quantitative Spectroscopy and Radiative Transfer, 142: 25-36
  29. 29.
    Njoku E, Christensen E and Cofield R. 1980. The Seasat scanning multichannel microwave radiometer (SMMR): antenna pattern corrections-Development and implementation. IEEE Journal of Oceanic Engineering, 5(2): 125-137
  30. 30.
    Okamoto K, Ishibashi T, Ishii S, Baron P, Gamo K, Tanaka T Y, Yamashita K and Kubota T. 2018. feasibility study for future space-borne coherent Doppler wind lidar, part 3: impact assessment using sensitivity observing system simulation experiments. Journal of the Meteorological Society of Japan, 96(2): 179-199
  31. 31.
    Reitebuch O. Lemmerz C, Lux O, Marksteiner U, Rahm S, Weiler F, Witschas B, Meringer M, Schmidt K, Huber D, Nikolaus I, Geiss A, Vaughan M, Dabas A, Flament T, Stieglitz H, Isaksen L, Rennie M, de Kloe J, Marseille G J, Stoffelen A, Wernham D, Kanitz T, Straume A G, Fehr T, von Bismarck J, Floberghagen R and Parrinello T. 2019. Initial assessment of the performance of the first wind lidar in space on Aeolus//The 29th International Laser Radar Conference. Heifei, China: EDP Sciences: 01010 [DOI: 10.1051/epjconf/202023701010]
  32. 32.
    Reitebuch O, Marksteiner U, Rompel M, Meringer M, Schmidt K, Huber D, Nikolaus I, Dabas A, Marshall J, de Bruin F, Kanitz T and Straume A G. 2017. Aeolus end-to-end simulator and wind retrieval algorithms up to level 1B//The 28th International Laser Radar Conference (ILRC 28). Bucharest: EDP Sciences: 02010 [DOI: 10.1051/epjconf/201817602010]
  33. 33.
    Reitebuch O, Paffrath U and Leike I. 2006. ATBD: ADM-Aeolus Level 1B Product. European Space Research and Technology Centre. .
  34. 34.
    Rohaly G D and Krishnamurti T N. 1993. An observing system simulation experiment for the laser atmospheric wind sounder (LAWS). Journal of Applied Meteorology, 32(9): 1453-1471
  35. 35.
    Shi J K, Yan W, Han Y J and Chen L. 2009. Studies on faraday rotation correction for fully polarimetric microwave radiometer at 10.7 GHz. Journal of Microwaves, 25(6): 79-83, 96
  36. 36.
    Stoffelen A, Marseille G J, Bouttier F, Vasiljevic D, de Haan S and Cardinali C. 2006. ADM-Aeolus Doppler wind lidar observing system simulation experiment. Quarterly Journal of the Royal Meteorological Society, 132(619): 1927-1947
  37. 37.
    Stoffelen A, Pailleux J, Källén E, Vaughan J M, Isaksen L, Flamant P, Wergen W, Andersson E, Schyberg H, Culoma A, Meynart R, Endemann M and Ingmann P. 2005. The atmospheric dynamics mission for global wind field measurement. Bulletin of the American Meteorological Society, 86(1): 73-88
  38. 38.
    Straume A G, Rennie M, Isaksen L, de Kloe J, Marseille G J, Stoffelen A, Flament T, Stieglitz H, Dabas A, Huber D, Reitebuch O, Lemmerz C, Lux O, Marksteiner U, Weiler F, Witschas B, Meringer M, Schmidt K, Nikolaus I, Geiss A, Flamant P, Kanitz T, Wernham D, von Bismarck J, Bley S, Fehr T, Floberghagen R and Parinello T. 2019. ESA's space-based Doppler wind lidar mission Aeolus - First wind and aerosol product assessment results//The 29th International Laser Radar Conference. Hefei, China: EDP Sciences: 01007 [DOI: 10.1051/epjconf/202023701007]
  39. 39.
    Sun X J, Zhang R W, Marseille G J, Stoffelen A, Donovan D, Liu L and Zhao J. 2014. The performance of Aeolus in heterogeneous atmospheric conditions using high-resolution radiosonde data. Atmospheric Measurement Techniques, 7(8): 2695-2717
  40. 40.
    Tan D G H, Andersson E, Fisher M and Isaksen L. 2007. Observing-system impact assessment using a data assimilation ensemble technique: application to the ADM-Aeolus wind profiling mission. Quarterly Journal of the Royal Meteorological Society, 133(623): 381-390
  41. 41.
    Wang Z H and Zeng W L. 1996. A PC-based objective inferring system for cloud motion winds from geostationary satellite images. Journal of Nanjing Institute of Meteorology, 19(1): 69-75
  42. 42.
    Wernham D, Ciapponi A, Riede W, Allenspacher P, Era F, D'Ottavi A and Thibault D. 2016. Verification for robustness to laser-induced damage for the Aladin instrument on the ADM-Aeolus satellite//Proceedings of SPIE 10014, Laser-Induced Damage in Optical Materials 2016. Boulder: SPIE: 1001408 [DOI: 10.1117/12.2245545]
  43. 43.
    WMO. 2001. Statement of Guidance Regarding How Well Satellite and in Situ Sensor Capabilities Meet WMO User Requirements in Several Application Areas. WMO Sat-26, WMO/TD-1052. World Meteorological Organization
  44. 44.
    Wood R, Bretherton C S and Hartmann D L. 2002. Diurnal cycle of liquid water path over the subtropical and tropical oceans. Geophysical Research Letters, 29(23): 2092
  45. 45.
    Xu J M, Zhang Q S, Wang D C and Fan C Y. 1997. Two Geometrical problems in cloud motion wind algorithm. Quarterly Journal of Applied Meteorology, 8(1): 11-18
  46. 46.
    Yan W, Shi J K and Lu W. 2010. Improving the correction accuracy of faraday rotation by using TEC data released by IGS. Journal of Infrared and Millimeter Waves, 29(3): 225-229
  47. 47.
    Zahir M and Durand Y. 2011. Critical laser technology developments and ESA space qualification approach in support of ESA's earth observation missions//Proceedings of SPIE 8159, Lidar Remote Sensing for Environmental Monitoring XII. San Diego: SPIE: 815904 [DOI: 10.1117/12.893587]
  48. 48.
    Zhang C L, Sun X J, Zhang R W and Liu Y W. 2018. Simulation and assessment of solar background noise for spaceborne lidar. Applied Optics, 57(31): 9471-9479
  49. 49.
    Zhang C L, Sun X J, Zhang R W, Zhao S J, Lu W, Liu Y W and Fan Z Q. Impact of solar background radiation on the accuracy of wind observations of spaceborne Doppler wind lidars based on their orbits and optical parameters. 2019. Optics Express, 27(12): A936-A952
  50. 50.
    Zhang R W, Sun X J, Yan W, Liu L, Li Y, Zhao J, Yan W X and Li H R. 2014. Simulation of frequency discrimination for spaceborne Doppler wind lidar (I): study on the retrieval of atmospheric wind speed for Mie channel based on Fizeau interferometer. Acta Physica Sinica, 63(14): 136-146
  51. 51.
    Zhu P, Wang Z H and Xu J M. 2007. Introduction to TCFM technique for tracking cloud and a preliminary experiment. Journal of Remote Sensing, 11(4): 538-544
  52. 52.
    Wang X H and Li H. 2005. The application status and future development trend of foreign spaceborne microwave radiometers. Aerospace China, (4):16-20

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