Advertisement

Environmental Earth Sciences

, 77:804 | Cite as

3D modelling strategy for weather radar data analysis

  • Mingyue Lu
  • Min Chen
  • Xinhao Wang
  • Manzhu Yu
  • Yongyao Jiang
  • Chaowei Yang
Original Article
  • 112 Downloads

Abstract

Weather radar data, which have obvious spatial characteristics, represent an important and essential data source for weather identification and prediction, and the multi-dimensional visualization and analysis of such data in a three-dimensional (3D) environment are important strategies for meteorological assessments of potentially disastrous weather. The previous studies have generally used regular 3D raster grids as a basic structure to represent radar data and reconstruct convective clouds. However, conducting weather radar data analyses based on regular 3D raster grids is time-consuming and inefficient, because such analyses involve considerable amounts of tedious data interpolation, and they cannot be used to address real-time situations or provide rapid-response solutions. Therefore, a new 3D modelling strategy that can be used to efficiently represent and analyse radar data is proposed in this article. According to the mode by which the radar data are obtained, the proposed 3D modelling strategy organizes the radar data using logical objects entitled radar-point, radar-line, radar-sector, and radar-cluster objects. In these logical objects, the radar point is the basic object that carries the real radar data unit detected from the radar scan, and the radar-line, radar-sector, and radar-cluster objects organize the radar-point collection in different spatial levels that are consistent with the intrinsic spatial structure of the radar scan. Radar points can be regarded as spatial points, and their spatial structure can support logical objects; thus, the radar points can be flexibly connected to construct continuous surface data with quads and volume data with hexahedron cells without additional tedious data interpolation. This model can be used to conduct corresponding operations, such as extracting an isosurface with the marching cube method and a radar profile with a designed sectioning algorithm to represent the outer and inner structure of a convective cloud. Finally, a case study is provided to verify that the proposed 3D modelling strategy has a better performance in radar data analysis and can intuitively and effectively represent the 3D structure of convective clouds.

Keywords

Weather radar data 3D modelling strategy Convective cloud Analysis 

Notes

Acknowledgements

We appreciate the detailed suggestions and comments from the secretariat and the anonymous reviewers. This work was supported by the NSF of China under Grant number 41871285, 41622108, and Priority Academic Program Development of Jiangsu Higher Education Institutions under Grant number 164320H116.

References

  1. Anagnostou EN, Krajewski WF, Smith J (1999) Un-certainty quantification of mean-areal radar-rainfall estimates. J Atmos Ocean Technol 16(2):206–215CrossRefGoogle Scholar
  2. Büyükbas E (2005) Training course on weather radar systems. Module A: introduction to radar. WMO, BalıkesirGoogle Scholar
  3. Chen M, Lin H, Kolditz O, Chen C (2015) Developing dynamic virtual geographic environments (VGEs) for geographic research. Environ Earth Sci 74(10):6975–6980CrossRefGoogle Scholar
  4. Ciach GJ, Krajewski WF (1999) Radar-rain gauge comparisons under observational uncertainties. J Appl Meteorol 38(38):1519–1525CrossRefGoogle Scholar
  5. Craglia M, Goodchild MF, Annoni A, Camara G, Gould M, Kuhn W, Mark D, Masser I et al (2008) Next-generation digital earth—a position paper from the vespucci initiative for the advancement of geographic information science. Int J Spat Data Infrastruct Res 3:146–167Google Scholar
  6. Crum TD, Alberty RL (2010) The WSR-88D and the WSR-88D operational support facility. Bull Am Meteorol Soc 74(9):1669–1687CrossRefGoogle Scholar
  7. Dobesch H, Dumolard P, Dyras I (2013) Spatial interpolation for climate data-the use of GIS in climatology and meteorology. ISTE Ltd, LondonGoogle Scholar
  8. Engwer C, Nüßing A (2017) Geometric reconstruction of implicitly defined surfaces and domains with topological guarantees. ACM Trans Math Softw.  https://doi.org/10.1145/3104989 CrossRefGoogle Scholar
  9. Ernvik A (2002) 3D visualization of weather radar data. Master’s thesis, Linköping UniversityGoogle Scholar
  10. Fulton RA, Breidenbach JP, Seo DJ, Miller DA, O’Bannon T (1997) The WSR-88D rainfall algorithm. Weather Forecast 13(2):377–395CrossRefGoogle Scholar
  11. Geçer C (2005) Training course on weather radar systems. Module D: radar products and operational applications. WMO, BalıkesirGoogle Scholar
  12. Goodchild MF (2012) The future of digital earth. Ann GIS 18(2):93–98CrossRefGoogle Scholar
  13. Guan L, Wei M, Wei K, Wang J (2015) 3D visualization of doppler radar data based on MATLAB platform. J Henan Norm Univ 43:43–48Google Scholar
  14. Han Y, Liu X, Lu X, Li H (2016) The 3D modeling and radar simulation of low-altitude wind shear via computational fluid dynamics method. In: 2016 Integrated Communications Navigation and Surveillance (ICNS), April. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7486349. Accessed 10 Dec 2018
  15. Liu ZN, Shi ZZ, Jiang MR, Zhang J, Chen LQ, Zhang T, Liu GQ (2017) Using MC algorithm to implement 3D Image reconstruction for Yunnan weather radar data. J Comput Commun 5(5):50–61CrossRefGoogle Scholar
  16. Lu ZY, Jin X, Han CY (2013) 3D reconstruction of strong convective cell based on doppler radar base data. In: International conference on machine learning and cybernetics IEEE, vol 2, Tianjin, China, pp 845–849Google Scholar
  17. Lu MY, Chen M, Wang X, Min JZ, Liu AL (2017) A spatial lattice model applied for meteorological visualization and analysis. ISPRS Int J GeoInf 6(3):77–90CrossRefGoogle Scholar
  18. Lü GN, Chen M, Yuan LW, Zhou LC, Wen YN, Wu MG, Hu B, Yu ZY et al (2018) Geographic scenario: a possible foundation for further development of virtual geographic environments. Int J Digit Earth 11(4):356–368CrossRefGoogle Scholar
  19. Newman TS, Yi H (2006) A survey of the marching cubes algorithm. Comput Graph 30(5):854–879CrossRefGoogle Scholar
  20. Newman TS, Byrd JB, Emani P, Narayanan A, Dastmalchi A (2004) High performance SIMD marching cubes isosurface extraction on commodity computers. Comput Graph 28(2):213–233CrossRefGoogle Scholar
  21. Oliveira Jr, Abimael A, Sérgio S, Fábio S (2014) 3D visualization tool for meteorological radar data using WebGL. In: 10th world congress on computational mechanics, May, vol 1, no 1. http://www.proceedings.blucher.com.br/evento/10wccm. Accessed 10 Dec 2018
  22. Rajon DA, Bolch WE (2003) Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. Comput Med Imaging Graph 27(5):411–435CrossRefGoogle Scholar
  23. Rinehart R (2004) Radar for meteorologists. Rinehart Publications, New YorkGoogle Scholar
  24. Wang P, Xu KJ, Zhang Y, Jia HZ (2012) Doppler weather radar clutter suppression based on texture feature. In: Proceedings of the 2012 international conference on machine learning and cybernetics, Xian, 15–17 July 2012, pp 1339–1344Google Scholar
  25. Xie HJ, Zhou XB, Vivoni ER, Hendrickx JMH, Small EE (2005) GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization. Comput Geosci 31(1):65–76CrossRefGoogle Scholar
  26. Xu WX, Li GD, Liao FJ (2010) Forecast hail by analysis radar image. In: Proceedings of the ninth international conference on machine learning and cybernetics, Qingdao, 11–14 July 2010, pp 730–734Google Scholar
  27. Yu YJ, Li Y (2016) Method for detecting and simulating 3D turbulence field of airborne weather radar. Syst Eng Electron 38:293–297Google Scholar
  28. Yu XD, Yao XP, Xiong YN (2006) Doppler weather radar principles and business applications. Meteorological Press, BeijingGoogle Scholar
  29. Zhang J, Howard K, Gourley JJ (2005) Constructing three-dimensional multiple-radar reflectivity mosaics: examples of convective storms and stratiform rain echoes. J Atmos Ocean Technol 22(1):30–42CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjingChina
  2. 2.Key Laboratory of Virtual Geographic Environment, Ministry of EducationNanjing Normal UniversityNanjingChina
  3. 3.Geography and GeoInformation ScienceGeorge Mason UniversityFairfaxUSA
  4. 4.School of Geography ScienceNanjing University of Information Science and TechnologyNanjingChina
  5. 5.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

Personalised recommendations