Theoretical and Applied Climatology

, Volume 119, Issue 1–2, pp 299–312 | Cite as

Atmospheric motion vector retrieval using improved tracer selection algorithm

  • Inderpreet Kaur
  • S. K. Deb
  • C. M. Kishtawal
  • P. K. Pal
  • Raj Kumar
Original Paper
First Online:
Received:
Accepted:

Abstract

Tracer selection is the fundamental step in the retrieval of atmospheric motion vectors (AMVs). In this study, a new technique for tracer selection based on extracting the corner points in an infrared (IR) image of a geostationary satellite for the retrieval of AMVs is developed. Corner points are frequently used in computer vision to identify the important features of an image. These points are usually characterized by high gradient values of the image intensity in all directions and lie at the junctions of different brightness regions in the image. Corner points find application in computer vision for motion tracking, stereo vision, mosaics, etc., but this is the first time that the information from corners is used for tracer selection in AMV retrieval. In the present study, a commonly used Harris corner (HC) detection algorithm is followed to extract corners from the image intensity of an IR image. The tracers selected using the HC method are then passed on to the other steps of the retrieval algorithm, viz., tracking, height assignment, and quality control procedures for the retrieval of AMVs. For the initial development of the HC, Meteosat-7 IR images are used to derive AMVs for July and December 2010. The AMVs retrieved using HC are validated against collocated radiosonde observations, and the results are compared with the local anomaly (LA) method as reference. LA is used for tracer selection in operational AMV retrieval algorithm from the Indian geostationary satellite Kalpana-1. AMVs retrieved using HC have shown considerable improvement in the AMV accuracy over the AMVs derived using LA.

Keywords

Corner Point Local Image Anomaly Harris Corner Height Assignment Tracer Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

The authors would like to thank the anonymous reviewer for carefully reviewing the manuscript and providing valuable comments which have helped in improving the manuscript considerably. The authors would also like to thank EUMETSAT (http://archive.eumetsat.int/; http://eportal.eumetsat.int) for providing the Meteosat-7 data. The authors are thankful to Sh. Nikunj Darji, Scientist, Space Applications Centre, for his useful insights into the initial development of the Harris corner technique. The authors are also thankful to the Director, Space Applications Centre, Indian Space Research Organisation, and the Deputy Director, EPSA, for their encouragement.

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Inderpreet Kaur
    • 1
  • S. K. Deb
    • 1
  • C. M. Kishtawal
    • 1
  • P. K. Pal
    • 1
  • Raj Kumar
    • 1
  1. 1.Atmospheric and Oceanic Sciences Group, Space Applications CentreIndian Space Research OrganizationAhmedabadIndia

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