• Abidi, M. A., and Gonzalez R. C. , 1988: Cloud motion measurement from radar image sequences. Digital Image Processing and Visual Communications Technologies in Meteorology, P. Janota, Ed., International Society for Optical Engineering (SPIE Proceedings, Vol. 0846), 54, doi:10.1117/12.942644.

  • Adelson, E. H., Anderson C. H. , Bergen J. R. , Burt P. J. , and Ogden J. M. , 1984: Pyramid methods in image processing. RCA Eng., 29, 3341.

    • Search Google Scholar
    • Export Citation
  • Arking, A., Lo R. C. , and Rosenfeld A. , 1978: A Fourier approach to cloud motion estimation. J. Appl. Meteor., 17, 735744, doi:10.1175/1520-0450(1978)017<0735:AFATCM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Baker, S., Scharstein D. , Lewis J. P. , Roth S. , Black M. J. , and Szeliski R. , 2011: A database and evaluation methodology for optical flow. Int. J. Comput. Vision, 92, 131, doi:10.1007/s11263-010-0390-2.

    • Search Google Scholar
    • Export Citation
  • Barron, J. L., Fleet D. J. , and Beauchemin S. S. , 1994: Performance of optical flow techniques. Int. J. Comput. Vision, 12, 4377, doi:10.1007/BF01420984.

    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., and Mecikalski J. R. , 2005: Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. J. Appl. Meteor., 44, 17611772, doi:10.1175/JAM2264.1.

    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., Velden C. S. , Petersen R. A. , Feltz W. F. , and Mecikalski J. R. , 2009: Comparisons of satellite-derived atmospheric motion vectors, rawinsondes, and NOAA wind profiler observations. J. Appl. Meteor. Climatol., 48, 15421561, doi:10.1175/2009JAMC1867.1.

    • Search Google Scholar
    • Export Citation
  • Borde, R., and Dubuisson P. , 2010: Sensitivity of atmospheric motion vectors height assignment methods to semitransparent cloud properties using simulated Meteosat-8 radiances. J. Appl. Meteor. Climatol., 49, 12051218, doi:10.1175/2010JAMC2352.1.

    • Search Google Scholar
    • Export Citation
  • Bormann, N., and Thépaut J.-N. , 2004: Impact of MODIS polar winds in ECMWF’s 4DVAR data assimilation system. Mon. Wea. Rev., 132, 929940, doi:10.1175/1520-0493(2004)132<0929:IOMPWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bresky, W., and Daniels J. , 2006: The feasibility of an optical flow algorithm for estimating atmospheric motion. Proc. Eighth Int. Winds Workshop, Beijing, China, EUMETSAT, P47_S4_04. [Available online at http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CONF_P47_S4_04_DANIELS_V&RevisionSelectionMethod=LatestReleased&Rendition=Web].

  • Büche, G., Karbstein H. , Kummer A. , and Fischer H. , 2006: Water vapor structure displacements from cloud-free Meteosat scenes and their interpretation for the wind field. J. Appl. Meteor. Climatol., 45, 556575, doi:10.1175/JAM2343.1.

    • Search Google Scholar
    • Export Citation
  • Campbell, G., and Holmlund K. , 2000: Geometric cloud heights from Meteosat and AVHRR. Fifth International Winds Workshop, Lorne, Australia, 28 February–3 March 2000: Proceedings, K. Holmlund, Ed., EUMETSAT, EUM P 28, 109–115. [Available online at http://cimss.ssec.wisc.edu/iwwg/iww5/S2-7_Campbell-Geometric.pdf.]

  • Chow, C. W., Belongie S. , and Kleissl J. , 2015: Cloud motion and stability estimation for intrahour solar forecasting. Sol. Energy, 115, 645655, doi:10.1016/j.solener.2015.03.030.

    • Search Google Scholar
    • Export Citation
  • Dew, G., and Holmulund K. , 2000: Investigations of cross-correlation and Euclidean distance target matching techniques in MPEF environment. Fifth International Winds Workshop, Lorne, Australia, 28 February–3 March 2000: Proceedings, K. Holmlund, Ed., EUMETSAT, EUM P 28, 235–243. [Available online at http://cimss.ssec.wisc.edu/iwwg/iww5/S5-3_Dew-Investigations.pdf.]

  • Dixon, M., and Wiener G. , 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Endlich, R. M., and Wolf D. E. , 1981: Automatic cloud tracking applied to GOES and METEOSAT observations. J. Appl. Meteor., 20, 309319, doi:10.1175/1520-0450(1981)020<0309:ACTATG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • 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. J. Appl. Meteor., 10, 105117, doi:10.1175/1520-0450(1971)010<0105:UOAPRT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Farnebäck, G., 2001: Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. Proceedings: Eighth IEEE International Conference on Computer Vision, Vol. 1, IEEE, 171–177, doi:10.1109/ICCV.2001.937514.

  • Farnebäck, G., 2002: Polynomial expansion for orientation and motion estimation. Ph.D. thesis, Dept. of Electrical Engineering, Computer Vision, Linköping University, 182 pp.

  • Farnebäck, G., 2003: Two-frame motion estimation based on polynomial expansion. Image Analysis: 13th Scandinavian Conference, J. Bigun and T. Gustavsson, Eds., Lecture Notes in Computer Science, Vol. 2749, Springer-Verlag, 363–370.

  • Holmlund, K., 1998: The utilization of statistical properties of satellite-derived atmospheric motion vectors to derive quality indicators. Wea. Forecasting, 13, 10931105, doi:10.1175/1520-0434(1998)013<1093:TUOSPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Horn, B. K. P., and Schunck B. G. , 1981: Determining optical flow. Artif. Intell., 17, 185203, doi:10.1016/0004-3702(81)90024-2.

  • Inoue, T., 1987a: A cloud type classification with NOAA 7 split-window measurements. J. Geophys. Res., 92, 39914000, doi:10.1029/JD092iD04p03991.

    • Search Google Scholar
    • Export Citation
  • Inoue, T., 1987b: An instantaneous delineation of convective rainfall areas using split window data of NOAA-7 AVHRR. J. Meteor. Soc. Japan, 65, 469481.

    • Search Google Scholar
    • Export Citation
  • Johnson, J. T., MacKeen P. L. , Witt A. , Mitchell E. D. W. , Stumpf G. J. , Eilts M. D. , and Thomas K. W. , 1998: The Storm Cell Identification and Tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263276, doi:10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kahn, B. H., and Coauthors, 2008: Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount. Atmos. Chem. Phys., 8, 12311248, doi:10.5194/acp-8-1231-2008.

    • Search Google Scholar
    • Export Citation
  • Kaur, I., Deb S. K. , Kishtawal C. M. , Pal P. K. , and Kumar R. , 2015: Atmospheric motion vector retrieval using improved tracer selection algorithm. Theor. Appl. Climatol., 119, 299312, doi:10.1007/s00704-014-1115-1.

    • Search Google Scholar
    • Export Citation
  • Laurent, H., 1993: Wind extraction from Meteosat water vapor channel image data. J. Appl. Meteor., 32, 11241133, doi:10.1175/1520-0450(1993)032<1124:WEFMWV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lazzara, M. A., Dworak R. , Santek D. A. , Hoover B. T. , Velden C. S. , and Key J. R. , 2014: High-latitude atmospheric motion vectors from composite satellite data. J. Appl. Meteor. Climatol., 53, 534547, doi:10.1175/JAMC-D-13-0160.1.

    • Search Google Scholar
    • Export Citation
  • 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. J. Appl. Meteor., 10, 118132, doi:10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Le Marshall, J., Pescod N. , Seaman B. , Mills G. , and Stewart P. , 1994: An operational system for generating cloud drift winds in the Australian region and their impact on numerical weather prediction. Wea. Forecasting, 9, 361370, doi:10.1175/1520-0434(1994)009<0361:AOSFGC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Lucas, B. D., and Kanade T. , 1981: An iterative image registration technique with an application to stereo vision. Proceedings of the Seventh International Joint Conference on Artificial Intelligence, A. Drinan, Ed., Vol. 2, Morgan Kaufmann Publishers, Inc., 674–679.

  • Menzel, W. P., 2001: Cloud tracking with satellite imagery: From the pioneering work of Ted Fujita to the present. Bull. Amer. Meteor. Soc., 82, 3347, doi:10.1175/1520-0477(2001)082<0033:CTWSIF>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Menzel, W. P., Smith W. L. , and Stewart T. R. , 1983: Improved cloud motion wind vector and altitude assignment using VAS. J. Appl. Meteor. Climatol., 22, 377384, doi:10.1175/1520-0450(1983)022<0377:ICMWVA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Nieman, S. J., Schmetz J. , and Menzel W. P. , 1993: A comparison of several techniques to assign heights to cloud tracers. J. Appl. Meteor., 32, 15591568, doi:10.1175/1520-0450(1993)032<1559:ACOSTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • 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 NESDIS operations. Bull. Amer. Meteor. Soc., 78, 11211133, doi:10.1175/1520-0477(1997)078<1121:FACDWI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rao, P. A., Velden C. S. , and Braun S. A. , 2002: The vertical error characteristics of GOES-derived winds: Description and experiments with numerical weather prediction. J. Appl. Meteor., 41, 253271, doi:10.1175/1520-0450(2002)041<0253:TVECOG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ricchiazzi, P., Yang S. , Gautier C. , and Sowle D. , 1998: SBDART: A research and teaching software tool for plane-parallel radiative transfer in the Earth’s atmosphere. Bull. Amer. Meteor. Soc., 79, 21012114, doi:10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Robbins, A. R., 1962: Long lines on the spheroid. Emp. Surv. Rev., 16, 301309, doi:10.1179/sre.1962.16.125.301.

  • Santek, D., García-Pereda J. , Velden C. , Genkova I. , Stettner D. , Wanzong S. , Nebuda S. , and Mindock M. , 2014: A new atmospheric motion vector intercomparison study. Proc. 12th Int. Winds Workshop, Copenhagen, Denmark, EUMETSAT, P.63, P61_S3_01. [Available online at http://www.eumetsat.int/website/wcm/idc/idcplg?IdcService=GET_FILE&dDocName=PDF_CONF_P61_S3_01_GARCAPER_V&RevisionSelectionMethod=LatestReleased&Rendition=Web.]

  • Sassen, K., Wang Z. , and Liu D. , 2008: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. J. Geophys. Res., 113, D00A12, doi:10.1029/2008JD009972.

    • Search Google Scholar
    • Export Citation
  • Schmetz, J., Holmlund K. , Hoffman J. , Strauss B. , Mason B. , Gaertner V. , Koch A. , and van de Berg L. , 1993: Operational cloud motion winds from Meteosat infrared images. J. Appl. Meteor., 32, 12061225, doi:10.1175/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Smith, E., and Phillips D. , 1972: Automated cloud tracking using precisely aligned digital ATS pictures. IEEE Trans. Comput., C-21, 715729, doi:10.1109/T-C.1972.223574.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 17711790, doi:10.1175/BAMS-83-12-1771.

    • Search Google Scholar
    • Export Citation
  • Szejwach, G., 1982: Determination of semi-transparent cirrus cloud temperature from infrared radiances: Application to Meteosat. J. Appl. Meteor., 21, 384393, doi:10.1175/1520-0450(1982)021<0384:DOSTCC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vega-Riveros, J. F., and Jabbour K. , 1989: Review of motion analysis techniques. IEE Proc., 136, 397404, doi:10.1049/ip-i-2.1989.0060.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., Hayden C. M. , Nieman S. J. , Menzel W. P. , Wanzong S. , and Goerss J. S. , 1997: Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc., 78, 173195, doi:10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., Olander T. L. , and Wanzong S. , 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description and case analysis. Mon. Wea. Rev., 126, 12021218, doi:10.1175/1520-0493(1998)126<1202:TIOMGW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205223, doi:10.1175/BAMS-86-2-205.

    • Search Google Scholar
    • Export Citation
  • Walker, J. R., MacKenzie W. M. Jr., Mecikalski J. R. , and Jewett C. P. , 2012: An enhanced geostationary satellite-based convective initiation algorithm for 0–2-h nowcasting with object tracking. J. Appl. Meteor. Climatol., 51, 19311949, doi:10.1175/JAMC-D-11-0246.1.

    • Search Google Scholar
    • Export Citation
  • Winker, D. M., Pelon J. , and McCormick M. P. , 2003: The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds. Lidar Remote Sensing for Industry and Environment Monitoring III, U. N. Singh, T. Itabe, and Z. Liu, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 4893), doi:10.1117/12.466539.

  • WMO, 1957: Meteorology—A three-dimensional science: Second session of the commission for aerology. WMO Bull., 6, 134138.

  • Wolf, D. E., Hall D. J. , and Endlich R. M. , 1977: Experiments in automatic cloud tracking using SMS-GOES data. J. Appl. Meteor., 16, 12191230, doi:10.1175/1520-0450(1977)016<1219:EIACTU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wu, Q., Wang H. Q. , Zhuang Y. Z. , Lin Y. J. , Zhang Y. , and Ding S. S. , 2016: Correlations of multispectral infrared indicators and applications in the analysis of developing convective clouds. J. Appl. Meteor. Climatol., 55, 945960, doi:10.1175/JAMC-D-15-0081.1.

    • Search Google Scholar
    • Export Citation
  • 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 forecasts of tropical cyclone track and intensity. Mon. Wea. Rev., 142, 4971, doi:10.1175/MWR-D-13-00023.1.

    • Search Google Scholar
    • Export Citation
  • Xu, J., Holmlund K. , Zhang Q. , and Schmetz J. , 2002: Comparison of two schemes for derivation of atmospheric motion vectors. J. Geophys. Res., 107, 4196, doi:10.1029/2001JD000744.

    • Search Google Scholar
    • Export Citation
  • Zhang, X., Xu J. , and Zhang Q. , 2014: Status of operational AMVs from FY-2 satellites since the 11th wind workshop. Proc. 12th Int. Winds Workshop, Copenhagen, Denmark, EUMETSAT, P.63, P61_S1_04. [Available online at http://www.eumetsat.int/website/home/News/ConferencesandEvents/PreviousEvents/DAT_2441511.html.]

  • Zinner, T., Mannstein H. , and Tafferner A. , 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteor. Atmos. Phys., 101, 191210, doi:10.1007/s00703-008-0290-y.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 384 211 0
PDF Downloads 315 148 0

Deriving AMVs from Geostationary Satellite Images Using Optical Flow Algorithm Based on Polynomial Expansion

View More View Less
  • 1 Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
  • | 2 National Meteorological Center, Chinese Meteorological Administration, Beijing, China
  • | 3 Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
Restricted access

Abstract

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

Corresponding author address: Hong-Qing Wang, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 5 Yiheyuan Road, Beijing 100871, China. E-mail: hqwang@pku.edu.cn

Abstract

An optical flow algorithm based on polynomial expansion (OFAPE) was used to derive atmospheric motion vectors (AMVs) from geostationary satellite images. In OFAPE, there are two parameters that can affect the AMV results: the sizes of the expansion window and optimization window. They should be determined according to the temporal interval and spatial resolution of satellite images. A helpful experiment was conducted for selecting those sizes. The limitations of window sizes can cause loss of strong wind speed, and an image-pyramid scheme was used to overcome this problem. Determining the heights of AMVs for semitransparent cloud pixels (STCPs) is challenging work in AMV derivation. In this study, two-dimensional histograms (H2Ds) between infrared brightness temperatures (6.7- and 10.8-μm channels) formed from a long time series of cloud images were used to identify the STCPs and to estimate their actual temperatures/heights. The results obtained from H2Ds were contrasted with CloudSat radar reflectivity and CALIPSO cloud-feature mask data. Finally, in order to verify the algorithm adaptability, three-month AMVs (JJA 2013) were calculated and compared with the wind fields of ERA data and the NOAA/ESRL radiosonde observations in three aspects: speed, direction, and vector difference.

Corresponding author address: Hong-Qing Wang, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 5 Yiheyuan Road, Beijing 100871, China. E-mail: hqwang@pku.edu.cn
Save