星载激光测风技术体制及其评估方法综述
A review of the technical system of spaceborne Doppler wind lidar and its assessment method
- 2022年26卷第6期 页码：1260-1273
DOI： 10.11834/jrs.20229067
引用
扫 描 看 全 文
摘要
星载激光测风雷达是探测全球风场的重要工具。目前由欧洲空间局研制的全球首颗星载激光测风卫星Aeolus已于2018年8月顺利升空部署，美国和日本也在积极论证和研制新的星载激光测风雷达技术体制，分别采用混合多普勒测风雷达HDWL（Hybrid Doppler Wind Lidar）和相干多普勒测风雷达CDWL（Coherent Doppler Wind Lidar）技术体制。本文简要介绍了Aeolus、HDWL和CDWL技术体制，并根据前人的研究成果，从数据获取率和获取量、风场探测精度以及对数值预报系统的改进作用对这3种技术体制进行了评估综述。研究结果表明以气溶胶和云滴粒子为示踪物进行的大气风场探测具有更高的探测精度，其测风精度约为0—2 m/s，其探测范围为边界层和对流层下层；以大气分子为示踪物的大气风场探测精度相对较低，其精度约为1—3 m/s，但其具有更大的探测范围。根据3种星载激光测风雷达技术体制，Aeolus和HDWL具备探测边界层和对流层的大气风场能力，CDWL只能获取边界层至对流层下层的风场数据，HDWL体制相比Aeolus和CDWL，能够获取更多的风场探测数据，且能够实现较高精度水平风场矢量探测数据。OSSE（Observing System Simulation Experiments）实验表明，将星载激光测风雷达风场探测资料同化到数值预报系统之后，预报结果得到明显的改善，在双星联合探测体制下，更大的风场探测范围相比径向风场的探测更有助于提升数值预报系统的精度，而径向风场的探测将更好地提升星下点的探测精度。HDWL体制相比Aeolus和CDWL，由于其探测范围更广，且可以实现径向风速的探测，故推测其对数值预报系统的精度的提升作用更明显。对这3种技术体制的分析评估可为发展中国的星载激光测风雷达技术体制提供参考。
Abstract
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.
关键词
遥感星载激光测风雷达AeolusHDWLCDWL数据获取率测风精度数值预报系统
Keywords
remote sensingspaceborne Doppler wind lidarAeolusHDWLCDWLdata acquisition rateaccuracy of wind observationsNWP
references
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 [DOI: 10.1364/AO.18.002992http://dx.doi.org/10.1364/AO.18.002992]
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 [DOI: 10.1117/12.7973481http://dx.doi.org/10.1117/12.7973481]
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]
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
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 [DOI: 10.1175/1520-0477(1995)076<0869:LMWFSA>2.0.CO;2http://dx.doi.org/10.1175/1520-0477(1995)076<0869:LMWFSA>2.0.CO;2]
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 [DOI: 10.2151/jmsj.2017-018http://dx.doi.org/10.2151/jmsj.2017-018]
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 [DOI: 10.1109/TGRS.2008.916264http://dx.doi.org/10.1109/TGRS.2008.916264]
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 [DOI: 10.1117/12.951882http://dx.doi.org/10.1117/12.951882]
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 [DOI: 10.1175/JCLI-D-13-00352.1http://dx.doi.org/10.1175/JCLI-D-13-00352.1]
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 [DOI: 10.1117/12.576539http://dx.doi.org/10.1117/12.576539]
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 [DOI: 10.1175/1520-0450(1971)010<0105:UOAPRT>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1971)010<0105:UOAPRT>2.0.CO;2]
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 [DOI: 10.1109/TGRS.2004.836867http://dx.doi.org/10.1109/TGRS.2004.836867]
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 [DOI: 10.1109/CVPR.2010.5540167http://dx.doi.org/10.1109/CVPR.2010.5540167]
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 [DOI: 10.2151/jmsj.2017-017http://dx.doi.org/10.2151/jmsj.2017-017]
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 [DOI: 10.1117/12.977283http://dx.doi.org/10.1117/12.977283]
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 [DOI: 10.1117/12.417026http://dx.doi.org/10.1117/12.417026]
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]
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 [DOI: 10.1256/qj.03.109http://dx.doi.org/10.1256/qj.03.109]
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 [DOI: 10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2]
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 [DOI: 10.1364/AO.45.004437http://dx.doi.org/10.1364/AO.45.004437]
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
龙智勇, 石汉青, 黄思训. 2011. 利用卫星云图反演云导风的新思路. 物理学报, 60(5): 059202 [DOI: 10.7498/aps.60.059202http://dx.doi.org/10.7498/aps.60.059202]
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
陆文, 严卫, 施健康, 任建奇. 2010. 一种适用于WindSat的地理定位方法. 遥感技术与应用, 25(1): 126-131 [DOI: 10.11873/j.issn.1004-0323.2010.1.126http://dx.doi.org/10.11873/j.issn.1004-0323.2010.1.126]
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
马侠霖, 罗鹏, 陈志斌, 蔡铭. 2014. 基于尺度不变特征变换的云导风计算方法. 气象科技, 42(3): 391-396 [DOI: 10.3969/j.issn.1671-6345.2014.03.006http://dx.doi.org/10.3969/j.issn.1671-6345.2014.03.006]
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 [DOI: 10.1256/qj.02.96http://dx.doi.org/10.1256/qj.02.96]
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 [DOI: 10.1111/j.1600-0870.2007.00289.xhttp://dx.doi.org/10.1111/j.1600-0870.2007.00289.x]
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]
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 [DOI: 10.1029/2009JD012528http://dx.doi.org/10.1029/2009JD012528]
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 [DOI: 10.1016/j.jqsrt.2014.03.014http://dx.doi.org/10.1016/j.jqsrt.2014.03.014]
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 [DOI: 10.1109/JOE.1980.1145460http://dx.doi.org/10.1109/JOE.1980.1145460]
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 [DOI: 10.2151/jmsj.2018-024http://dx.doi.org/10.2151/jmsj.2018-024]
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]
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]
Reitebuch O, Paffrath U and Leike I. 2006. ATBD: ADM-Aeolus Level 1B Product. European Space Research and Technology Centre. https://earth.esa.int/files/Aeolus_L1B_Algorithm_TBDhttps://earth.esa.int/files/Aeolus_L1B_Algorithm_TBD.
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 [DOI: 10.1175/1520-0450(1993)032<1453:AOSSEF>2.0.CO;2http://dx.doi.org/10.1175/1520-0450(1993)032<1453:AOSSEF>2.0.CO;2]
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
施健康, 严卫, 韩有君, 陈磊. 2009. 全极化微波辐射计10.7GHz极化通道法拉第旋转校正分析. 微波学报, 25(6): 79-83, 96
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 [DOI: 10.1256/qj.05.83http://dx.doi.org/10.1256/qj.05.83]
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 [DOI: 10.1175/BAMS-86-1-73http://dx.doi.org/10.1175/BAMS-86-1-73]
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]
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 [DOI: 10.5194/amt-7-2695-2014http://dx.doi.org/10.5194/amt-7-2695-2014]
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 [DOI: 10.1002/qj.43http://dx.doi.org/10.1002/qj.43]
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
王振会, 曾维麟. 1996. 卫星云迹风微机客观导出系统. 南京气象学院学报, 19(1):69-75 [DOI: 10.13878/j.cnki.dqkxxb.1996.01.010http://dx.doi.org/10.13878/j.cnki.dqkxxb.1996.01.010]
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]
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
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 [DOI: 10.1029/2002GL015371http://dx.doi.org/10.1029/2002GL015371]
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
许健民, 张其松, 王大昌, 樊昌尧. 1997. 云迹风计算中的两个几何问题. 应用气象学报, 8(1): 11-18
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
严卫, 施健康, 陆文. 2010. 用IGS发布的TEC数据提高法拉第旋转校正精度. 红外与毫米波学报, 29(3): 225-229
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]
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 [DOI: 10.1364/AO.57.009471http://dx.doi.org/10.1364/AO.57.009471]
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 [DOI: 10.1364/OE.27.00A936http://dx.doi.org/10.1364/OE.27.00A936]
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
张日伟, 孙学金, 严卫, 刘磊, 李岩, 赵剑, 颜万祥, 李浩然. 2014. 星载激光多普勒测风雷达鉴频系统仿真(I): 基于Fizeau干涉仪的Mie通道大气风速反演研究. 物理学报, 63(14): 136-146 [DOI: 10.7498/aps.63.140702http://dx.doi.org/10.7498/aps.63.140702]
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
朱平, 王振会, 许建明. 2007. TCFM导风技术介绍及其初步试验研究. 遥感学报, 11(4): 538-544
Wang X H and Li H. 2005. The application status and future development trend of foreign spaceborne microwave radiometers. Aerospace China, (4):16-20
王晓海, 李浩. 2005. 国外星载微波辐射计应用现状及未来发展趋势. 中国航天, (4): 16-20
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
国家卫星气象中心(国家空间天气监测预警中心) 中国气象局中国遥感卫星辐射测量和 定标重点开放实验室
许健民气象卫星创新中心
南京大学 国际地球系统科学研究所
河南理工大学 测绘与国土信息工程学院
中国科学院空天信息创新研究院
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部