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ISPRS Int. J. Geo-Inf. 2017, 6(3), 77; doi:10.3390/ijgi6030077

A Spatial Lattice Model Applied for Meteorological Visualization and Analysis

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3,4,5,6,* , 1,2
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1
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
School of Geography and Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
4
State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
6
State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Milan Konecny and Wolfgang Kainz
Received: 10 October 2016 / Revised: 2 March 2017 / Accepted: 6 March 2017 / Published: 9 March 2017
View Full-Text   |   Download PDF [9417 KB, uploaded 9 March 2017]   |  

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. View Full-Text
Keywords: meteorological space; spatial lattice model; meteorological analysis meteorological space; spatial lattice model; meteorological analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, M.; Chen, M.; Wang, X.; Min, J.; Liu, A. A Spatial Lattice Model Applied for Meteorological Visualization and Analysis. ISPRS Int. J. Geo-Inf. 2017, 6, 77.

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