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3D modelling strategy for weather radar data analysis

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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.

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References

  • Anagnostou EN, Krajewski WF, Smith J (1999) Un-certainty quantification of mean-areal radar-rainfall estimates. J Atmos Ocean Technol 16(2):206–215

    Article  Google Scholar 

  • Büyükbas E (2005) Training course on weather radar systems. Module A: introduction to radar. WMO, Balıkesir

    Google Scholar 

  • Chen M, Lin H, Kolditz O, Chen C (2015) Developing dynamic virtual geographic environments (VGEs) for geographic research. Environ Earth Sci 74(10):6975–6980

    Article  Google Scholar 

  • Ciach GJ, Krajewski WF (1999) Radar-rain gauge comparisons under observational uncertainties. J Appl Meteorol 38(38):1519–1525

    Article  Google Scholar 

  • 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 3:146–167

    Google Scholar 

  • Crum TD, Alberty RL (2010) The WSR-88D and the WSR-88D operational support facility. Bull Am Meteorol Soc 74(9):1669–1687

    Article  Google Scholar 

  • Dobesch H, Dumolard P, Dyras I (2013) Spatial interpolation for climate data-the use of GIS in climatology and meteorology. ISTE Ltd, London

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Ernvik A (2002) 3D visualization of weather radar data. Master’s thesis, Linköping University

  • Fulton RA, Breidenbach JP, Seo DJ, Miller DA, O’Bannon T (1997) The WSR-88D rainfall algorithm. Weather Forecast 13(2):377–395

    Article  Google Scholar 

  • Geçer C (2005) Training course on weather radar systems. Module D: radar products and operational applications. WMO, Balıkesir

    Google Scholar 

  • Goodchild MF (2012) The future of digital earth. Ann GIS 18(2):93–98

    Article  Google Scholar 

  • Guan L, Wei M, Wei K, Wang J (2015) 3D visualization of doppler radar data based on MATLAB platform. J Henan Norm Univ 43:43–48

    Google Scholar 

  • 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

  • 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):50–61

    Article  Google Scholar 

  • 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 845–849

  • 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):77–90

    Article  Google Scholar 

  • 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):356–368

    Article  Google Scholar 

  • Newman TS, Yi H (2006) A survey of the marching cubes algorithm. Comput Graph 30(5):854–879

    Article  Google Scholar 

  • 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):213–233

    Article  Google Scholar 

  • 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

  • Rajon DA, Bolch WE (2003) Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. Comput Med Imaging Graph 27(5):411–435

    Article  Google Scholar 

  • Rinehart R (2004) Radar for meteorologists. Rinehart Publications, New York

    Google Scholar 

  • 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 1339–1344

  • 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):65–76

    Article  Google Scholar 

  • 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 730–734

  • Yu YJ, Li Y (2016) Method for detecting and simulating 3D turbulence field of airborne weather radar. Syst Eng Electron 38:293–297

    Google Scholar 

  • Yu XD, Yao XP, Xiong YN (2006) Doppler weather radar principles and business applications. Meteorological Press, Beijing

    Google Scholar 

  • 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):30–42

    Article  Google Scholar 

Download references

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.

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Authors and Affiliations

  1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Mingyue Lu & Xinhao Wang

  2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, 210023, China

    Min Chen

  3. Geography and GeoInformation Science, George Mason University, Fairfax, 22031, USA

    Manzhu Yu, Yongyao Jiang & Chaowei Yang

  4. School of Geography Science, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China

    Mingyue Lu & Xinhao Wang

  5. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, Jiangsu, China

    Min Chen

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  1. Mingyue Lu
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  2. Min Chen
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  3. Xinhao Wang
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  4. Manzhu Yu
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  5. Yongyao Jiang
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  6. Chaowei Yang
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Correspondence to Min Chen.

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Lu, M., Chen, M., Wang, X. et al. 3D modelling strategy for weather radar data analysis. Environ Earth Sci 77, 804 (2018). https://doi.org/10.1007/s12665-018-7985-2

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