A Spatial Lattice Model Applied for Meteorological Visualization and Analysis

Abstract

Abstract:Meteorological information has obvious spatial-temporal characteristics. Although it is meaningful to employ a geographic information system (GIS) to visualize and analyze the meteorological information for better identification and forecasting of meteorological weather so as to reduce the meteorological disaster loss, modeling meteorological information based on a GIS is still difficult because meteorological elements generally have no stable shape or clear boundary. To date, there are still few GIS models that can satisfy the requirements of both meteorological visualization and analysis. In this article, a spatial lattice model based on sampling particles is proposed to support both the representation and analysis of meteorological information. In this model, a spatial sampling particle is regarded as the basic element that contains the meteorological information, and the location where the particle is placed with the time mark. The location information is generally represented using a point. As these points can be extended to a surface in two dimensions and a voxel in three dimensions, if these surfaces and voxels can occupy a certain space, then this space can be represented using these spatial sampling particles with their point locations and meteorological information. In this case, the full meteorological space can then be represented by arranging numerous particles with their point locations in a certain structure and resolution, i.e., the spatial lattice model, and extended at a higher resolution when necessary. For practical use, the meteorological space is logically classified into three types of spaces, namely the projection surface space, curved surface space, and stereoscopic space, and application-oriented spatial lattice models with different organization forms of spatial sampling particles are designed to support the representation, inquiry, and analysis of meteorological information within the three types of surfaces. Cases studies are conducted by (1) performing a visualization of radar data that is used to describe the reflectivity factor of a raindrop and the pressure field information acquired from the National Centers for Environmental Prediction (NCEP), and (2) taking cutting analysis as another example where advanced meteorological analysis is performed. The results show that the proposed spatial lattice model can contribute to the feasible and effective analysis of meteorological information.

Lightning Strike Location Identification Based on 3D Weather Radar Data

“…As highly reliable data in the field of meteorological detection, radar data have been widely considered by meteorologists, and many explorations and practices have been carried out. Lu et al (2017) presented a spatial lattice model based on sampling particles that was proposed to support both the representation and analysis of meteorological information. A 3D modeling strategy was used for weather radar data analysis (Lu et al, 2018).…”

Web‐based real‐time visualization of large‐scale weather radar data using 3D tiles

“…The WRCD enables the analysis of large amounts of meteorological data and brings challenges in many aspects, such as its complex data structure. Thanks to the rapid development of web‐based GIS, it is now feasible to perform 3D visualization of large‐scale meteorological data in a real‐time manner (Abimael, Scheer, & Sato, 2012; Lu, Chen, Wang, Min, & Liu, 2017). In order to make full use of the 3D information of the WRCD, this article aims to propose a new framework to perform real‐time 3D visualization of the WRCD taking advantage of 3D tiles and WebGIS.…”

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

“…An appropriate geovisualization method can provide prompt insight and understanding to support real-world knowledge construction and decision-making [ 1 ]. Recently, as a tool, a process and a mode of thought, geovisualization has been widely used in environmental monitoring [ 2 ], spatial decision-making [ 3 ], urban mobility [ 4 ], meteorology [ 5 ], and archaeology [ 6 ], among other fields. However, traditional geovisualization methods suffer from several drawbacks when facing various increasingly challenging representation needs.…”