3D modelling strategy for weather radar data analysis

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.

[1]  Witold F. Krajewski,et al.  Radar–Rain Gauge Comparisons under Observational Uncertainties , 1999 .

[2]  Aron Ernvik.  3D Visualization of Weather Radar Data , 2002 .

[3]  Hong Yi,et al.  A survey of the marching cubes algorithm , 2006, Comput. Graph..

[4]  Min Chen,et al.  A Spatial Lattice Model Applied for Meteorological Visualization and Analysis , 2017, ISPRS Int. J. Geo Inf..

[5]  Min Chen,et al.  Geographic scenario: a possible foundation for further development of virtual geographic environments , 2018, Int. J. Digit. Earth.

[6]  Emmanouil N. Anagnostou,et al.  Uncertainty Quantification of Mean-Areal Radar-Rainfall Estimates , 1999 .

[7]  Jan M. H. Hendrickx,et al.  GIS-based NEXRAD Stage III precipitation database: automated approaches for data processing and visualization , 2005, Comput. Geosci..

[8]  Jie Zhang,et al.  Using MC Algorithm to Implement 3D Image Reconstruction for Yunnan Weather Radar Data , 2017 .

[9]  Michael F. Goodchild.  The future of Digital Earth , 2012, Ann. GIS.

[10]  Timothy D. Crum,et al.  The WSR-88D and the WSR-88D Operational Support Facility , 1993 .

[11]  Renbiao Wu,et al.  The 3D modeling and radar simulation of low-altitude wind shear via computational fluid dynamics method , 2016, 2016 Integrated Communications Navigation and Surveillance (ICNS).

[12]  Hui Lin,et al.  Developing dynamic virtual geographic environments (VGEs) for geographic research , 2015, Environmental Earth Sciences.

[13]  W E Bolch,et al.  Marching cube algorithm: review and trilinear interpolation adaptation for image-based dosimetric models. , 2003, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[14]  Jay P. Breidenbach,et al.  The WSR-88 D Rainfall Algorithm , 1998 .

[15]  Wen-Xia Xu,et al.  Forecast hail by analysis radar image , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[16]  Jian Zhang,et al.  Constructing Three-Dimensional Multiple-Radar Reflectivity Mosaics: Examples of Convective Storms and Stratiform Rain Echoes , 2005 .

[17]  Timothy S. Newman,et al.  High performance SIMD marching cubes isosurface extraction on commodity computers , 2004, Comput. Graph..

[18]  David M. Mark,et al.  Next-Generation Digital Earth: A position paper from the Vespucci Initiative for the Advancement of Geographic Information Science , 2008, Int. J. Spatial Data Infrastructures Res..

[19]  Christian Engwer,et al.  Geometric Reconstruction of Implicitly Defined Surfaces and Domains with Topological Guarantees , 2017, ACM Trans. Math. Softw..

[20]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[21]  Yuan Zhang,et al.  Doppler weather radar clutter suppression based on texture feature , 2012, 2012 International Conference on Machine Learning and Cybernetics.

[22]  P. Dumolard,et al.  Spatial interpolation for climate data : the use of GIS in climatology and meterology , 2007 .