地球同步卫星向日葵8号水汽频道推算高层大气运动向量
Upper-Tropospheric Atmospheric Motion Vectors Derived from Geostationary Satellite Himawari-8 Water Vapor Observations
DOI: 10.12677/AG.2017.72015, PDF, HTML, XML,  被引量 下载: 1,602  浏览: 4,248 
作者: 周鉴本：“中央气象局”四组，台湾 台北
关键词: 水汽频道；大气运动向量；Water Vapor Channel； Atmospheric Motion Vector
摘要: 本文使用日本地球同步卫星向日葵8号上所载7.0微米水汽频道推导大气运动向量，实验的结果显示：以连续两张间隔10分钟的水汽频道观测影像所推导的大气运动向量，与探空风场数据比对可以发现，当推导过程中设定比较严格的质量检定情形下，卫星推导的大气运动向量与探空风场差值的大小，与区域模式6小时预报风场与探空风场差值十分接近，这显示目前以向日葵8号卫星水汽频道所推导的大气运动向量，具有不错的准确性，有机会于未来将此风场数据放进数值预报数据同化系统中。
Abstract: In this article the water vapor channel at 7.0 micron on geostationary satellite Himawari-8 has been used to derive atmospheric motion vectors. The atmospheric motion vectors were created by two consecutive water vapor channel images with time interval 10 minutes. In order to estimate the errors of atmospheric motion vectors the atmospheric motion vectors were compare to sounding observation. It can be found that the errors of atmospheric motion vectors are close to the errors of 6 hours numerical weather forecast wind field where a strict quality check was adopted in deriving atmospheric motion vector procedure. These results reveal that the atmospheric motion vectors were accurate and have the potential to be used in data assimilation system in the future.
文章引用：周鉴本. 地球同步卫星向日葵8号水汽频道推算高层大气运动向量[J]. 地球科学前沿, 2017, 7(2): 142-150. https://doi.org/10.12677/AG.2017.72015

参考文献

[1] Fujita, T. (1968) Present Status of Cloud Velocity Computations from ATS-1 and ATS-3. Space Research, 9, 557-570.
[2] 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, 118-132.
https://doi.org/10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2
[3] Nieman, S.J., Menzel, W.P., Hayden, C.M., Gray, D., Wanzong, S.T., Velden, C.S. and Daniels, J. (1997) Fully Automated Cloud-Drift Winds in NEDIS Operations. Bulletin of the AMS, 78, 1121-1133.
[4] Walker, J.R., MacKenzie, W.M., Mecikalski, J.R. and Jewett, C.P. (2012) An Enhanced Geostationary Satellite-Based Convective Initaation Algorithm for 0-2 Nowcasting with Object Tracking. Journal of Applied Meteorology and Climatology, 51, 1933-1949.
https://doi.org/10.1175/JAMC-D-11-0246.1
[5] Bedka, K.M. and Mecikalski, J.R. (2005) Application of Satellite-Derived Atmospheric Motion Vectors for Estimating Mesoscale Flow. Journal of Applied Meteorology, 44, 1761-1772.
https://doi.org/10.1175/JAM2264.1
[6] Bormann, N. and Thepaut, J.-N. (2004) Impact of MODIS Polar Wind in ECMWF’s 4DVAR Data Assimilation System. Monthly Weather Review, 132, 929-940.
https://doi.org/10.1175/1520-0493(2004)132<0929:IOMPWI>2.0.CO;2
[7] Wu, T.-C., Liu, H., Majumdar, S.J., Velden, C.S. and Anderson, J.L. (2014) Influence of Assimilating Satellite-Derived Atmospheric Motion Vector Observations on Numerical Analyses and Forecast of Tropical Cyclone Track and Intensity. Monthly Weather Review, 142, 49-71.
https://doi.org/10.1175/MWR-D-13-00023.1
[8] Wu, Q., Wang, H.-Q., Lin, Y.-J., Zhuang, Y.-Z. and Zhang, Y. (2016) Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polyanmial Expansion. Journal of Atmospheric and Oceanic Technology, 33, 1727-1747.
[9] Menzel, W.L.S. and Stewart, T.R. (1992) Improved Cloud Motion Wind Vector and Altitude Assignment Using VAS. Journal of Applied Meteorology, 31, 270-384.
[10] Szejwach, G. (1982) Determination of Semi-Transparent Cirrus Cloud Temperature from Infrared Radiances: Application to Meteosat. Journal of Applied Meteorology and Climatology, 48, 450-463.
https://doi.org/10.1175/1520-0450(1982)021<0384:dostcc>2.0.co;2
[11] Jung, J., Le Marshall, J., Daniels, J. and Riishojgaard, L.P. (2010) Investigating Height Assignment Type Error in the NCEP Global Forecasting System. Proceedings of 10th International Wind Workshop, Tokyo, 22-26 February 2010, Session 3, Paper 4.
[12] Velden, C.S., Hayden, C.M., Nieman, S.J., Mieman, W.P., Menzel, W.P., Wanzong, S. and Goerss, J.S. (1997) Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations. Bulletin of the American Meteorological Society, 78, 173-195.
https://doi.org/10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2
[13] Bresky, W.C., Daniels, J.M., Bailey, A.A. and Wanzong, S.T. (2012) New Method toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors. Journal of Applied Meteorology and Climatology, 51, 2137-2151.
https://doi.org/10.1175/JAMC-D-11-0234.1
[14] Salonen, K., Cotton, J., Bormann, N. and Forsythe, M. (2015) Characterizing AMV Height-Assignment Error by Comparing Best-Fit Pressure Statistic from the Met Office and ECMWF Data Assimilation Systems. Journal of Applied Meteorology and Climatology, 54, 225-242.
https://doi.org/10.1175/JAMC-D-14-0025.1