DOI QR코드

DOI QR Code

Fast Coordinate Conversion Method for Real-time Weather Radar Data Processing

  • Jang, Bong-Joo (Korea Institute of Civil Engineering and Building Technology) ;
  • Lim, Sanghun (Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Won (Korea Institute of Civil Engineering and Building Technology)
  • Received : 2018.03.07
  • Accepted : 2018.03.11
  • Published : 2018.03.30

Abstract

The coordinate system conversion of weather radar data is a basic and important process because it can be a factor to measure the accuracy of radar precipitation rate by comparison with the ground rain gauge. We proposed a real-time coordinate system conversion method that combines the advantages of the interpolation masks of SPRINT and REORDER to use tables of predetermined radar samples for each interpolated object coordinate and also distance weights for each precomputed sample. Experimental results show that the proposed method improves the computation speed more than 20~30 times compared with the conventional method and shows that the deterioration of image quality is hardly ignored.

Keywords

E1MTCD_2018_v5n1_1_f0001.png 이미지

Fig. 1. Each interpolation mask used in the SPRINT andREORDER algorithms. (a) SPRINT algorithm [9], and (b)REORDER algorithm [10].

E1MTCD_2018_v5n1_1_f0002.png 이미지

Fig. 2. Example of interpolation masks application of SPRINTand REORDER algorithm in realistic coordinate system [9].

E1MTCD_2018_v5n1_1_f0003.png 이미지

Fig. 3. Example of applying SPRINT and REORDER from radardata [9].

E1MTCD_2018_v5n1_1_f0004.png 이미지

Fig. 4. Block diagram of the proposed method.

E1MTCD_2018_v5n1_1_f0005.png 이미지

Fig. 5. Examples of non-normalized radar data (raw data).

E1MTCD_2018_v5n1_1_f0006.png 이미지

Fig. 6. Ray normalization process and result example for primitive radar data, (a) the ray profile of the raw data ofFigure 5, (b) the result of the surplus ray filtering, and (c) the azimuthal rearrangement result.

E1MTCD_2018_v5n1_1_f0007.png 이미지

Fig. 7. (a) Normalized ray profile, (b) example of windowing forcoordinate system transformation to arbitrary positions in the realcoordinate system, and (c) radar image with coordinate systemconversion.

E1MTCD_2018_v5n1_1_f0008.png 이미지

Fig. 8. Example of the same lookup table application for radars with different observation radii.

E1MTCD_2018_v5n1_1_f0009.png 이미지

Fig. 9. Example of allowing only the calculation area for the watershed to be handled when given watershedinformation.

E1MTCD_2018_v5n1_1_f0010.png 이미지

Fig. 10. The result of the coordinate system transformation of theradar data by the proposed method is as follows: (a) As a result ofcalculating weights for all coordinate points, (b) as a result ofapplying the LUT mask, and (c) errors by the azimuthnormalization sampling process.

Table 1. Processing speed of the proposed method using look-up table mask and not.

E1MTCD_2018_v5n1_1_t0001.png 이미지

References

  1. V. N, Bringi and V. Chandrasekar, Polarimetric Doppler Weather Radar: Principles and Applications. New York, NY: Cambridge University Press, 2001.
  2. M. R. Duncan, A. Bellon, A. Kilambi, and G. L. Austin, "PPS and PPS Jr: A distribution network for weather radar products, severe weather warnings and rainfall forecasts," Eighth Int. Conf. on Interactive Information and Processing Systems for meteorology, oceanography and hydrology, Atlanta, GA, Amer. Meteor. Soc., pp. 67-74, 1992.
  3. V. Chandrasekar, et al., "Accomplishments, challenges and opportunities in developing network based radar systems for high-impact small-scale weather events," 2011 IEEE RadarCon (RADAR), Kansas City, MO, pp. 1056-1061, May 2011.
  4. J. C. Hinton, "GIS and remote sensing integration for environmental applications," International Journal of Geographical Information Systems, vol. 10, no. 7, pp. 877-890, 1996. https://doi.org/10.1080/02693799608902114
  5. X. Zhang, and R. Srinivasan, "GIS-based spatial precipitation estimation using next generation radar and raingauge data," Environmental Modelling & Software, vol. 25, no. 12, pp. 1781-1788, 2010. https://doi.org/10.1016/j.envsoft.2010.05.012
  6. B. J. Jang and S. Lim, "GIS Based Realistic Weather Radar Data Visualization Technique," Journal of Multimedia Information Systems, vol. 4, no. 1, pp. 1-8, 2017. https://doi.org/10.24167/sisforma.v4i1.1040
  7. R. Oye, and M. Case, REORDER- A program for gridding radar data: Installation and use manual for the UNIX version, NCAR, 44 p. 1992.
  8. L. J. Miller, and S. M. Fredrick, SPRINT- Sorted Position Radar INTerpolation, NCAR, 76 p. 1999.
  9. L. J. Miller, "Some Fundamentals of Doppler Radar Velocity Analysis - Data Preparation and Gridding for Wind Synthesis Using REORDER, SPRINT, and CEDRIC PROGRAMS," https://wiki.ucar.edu/download/attachments/41487211/Lecture2-9.ppt, accessed Mar. 2018.
  10. S. X. Zhang, CASA real-time multi-Doppler retrieval system. Colorado State University, 2011.