Multi-Level Particle System Modeling Algorithm with WRF
In the fields of meteorological simulation and computer graphics, precise simulation of clouds has been a recent research hotspot. The existing cloud modeling methods often ignore the differentiated characteristics of cloud layers at different heights, and suffer from high computational costs under...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/5/571 |
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| _version_ | 1849710956629721088 |
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| author | Julong Chen Bin Wang Rundong Gan Xuepeng Mou Shiping Yang Ling Tan |
| author_facet | Julong Chen Bin Wang Rundong Gan Xuepeng Mou Shiping Yang Ling Tan |
| author_sort | Julong Chen |
| collection | DOAJ |
| description | In the fields of meteorological simulation and computer graphics, precise simulation of clouds has been a recent research hotspot. The existing cloud modeling methods often ignore the differentiated characteristics of cloud layers at different heights, and suffer from high computational costs under long-range conditions, making them unsuitable for large-scale scenes. Therefore, we propose a multi-level particle system 3D cloud modeling algorithm based on the Weather Research and Forecasting Model (WRF), which combines particle weight adjustment with a Proportional Integral Derivative (PID) feedback mechanism to represent cloud features of different heights and types. Based on the multi-scale mean-shift clustering algorithm, Adaptive Kernel Density Estimation (AKDE) is introduced to map density to bandwidth, achieving adaptive adjustment of clustering bandwidth while reducing computational resources and improving cloud hierarchy. Meanwhile, selecting the optimal control points based on the correlation between particle density in the edge region and cloud contour can ensure the integrity of the internal structure of the cloud and the clarity of the external contour. To improve modeling efficiency, cascade Bezier curves are designed at different line-of-sights (LoSs), utilizing the weight information of boundary particles to optimize cloud contours. Experimental results show that, compared with similar algorithms, our algorithm reduces the average running time by 37.5%, indicating enhanced computational efficiency and real-time capability, and the average number of required particles by 30.1%, reducing the cost of long-range computing. Our algorithm can fully demonstrate cloud characteristics and interlayer differences, significantly improve modeling efficiency, and can be used for accurate modeling of large-scale cloud scenes, providing strong support for meteorological and climate prediction. |
| format | Article |
| id | doaj-art-2b1974e6e3164736a2efebcb1deeff0b |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-2b1974e6e3164736a2efebcb1deeff0b2025-08-20T03:14:45ZengMDPI AGAtmosphere2073-44332025-05-0116557110.3390/atmos16050571Multi-Level Particle System Modeling Algorithm with WRFJulong Chen0Bin Wang1Rundong Gan2Xuepeng Mou3Shiping Yang4Ling Tan5Power Grid Planning & Research Center Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning & Research Center Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning & Research Center Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning & Research Center Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaPower Grid Planning & Research Center Guizhou Power Grid Co., Ltd., Guiyang 550003, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaIn the fields of meteorological simulation and computer graphics, precise simulation of clouds has been a recent research hotspot. The existing cloud modeling methods often ignore the differentiated characteristics of cloud layers at different heights, and suffer from high computational costs under long-range conditions, making them unsuitable for large-scale scenes. Therefore, we propose a multi-level particle system 3D cloud modeling algorithm based on the Weather Research and Forecasting Model (WRF), which combines particle weight adjustment with a Proportional Integral Derivative (PID) feedback mechanism to represent cloud features of different heights and types. Based on the multi-scale mean-shift clustering algorithm, Adaptive Kernel Density Estimation (AKDE) is introduced to map density to bandwidth, achieving adaptive adjustment of clustering bandwidth while reducing computational resources and improving cloud hierarchy. Meanwhile, selecting the optimal control points based on the correlation between particle density in the edge region and cloud contour can ensure the integrity of the internal structure of the cloud and the clarity of the external contour. To improve modeling efficiency, cascade Bezier curves are designed at different line-of-sights (LoSs), utilizing the weight information of boundary particles to optimize cloud contours. Experimental results show that, compared with similar algorithms, our algorithm reduces the average running time by 37.5%, indicating enhanced computational efficiency and real-time capability, and the average number of required particles by 30.1%, reducing the cost of long-range computing. Our algorithm can fully demonstrate cloud characteristics and interlayer differences, significantly improve modeling efficiency, and can be used for accurate modeling of large-scale cloud scenes, providing strong support for meteorological and climate prediction.https://www.mdpi.com/2073-4433/16/5/5713D cloud modelingmulti-level particle systemWRFweighted clusteringcascaded Bezier curve |
| spellingShingle | Julong Chen Bin Wang Rundong Gan Xuepeng Mou Shiping Yang Ling Tan Multi-Level Particle System Modeling Algorithm with WRF Atmosphere 3D cloud modeling multi-level particle system WRF weighted clustering cascaded Bezier curve |
| title | Multi-Level Particle System Modeling Algorithm with WRF |
| title_full | Multi-Level Particle System Modeling Algorithm with WRF |
| title_fullStr | Multi-Level Particle System Modeling Algorithm with WRF |
| title_full_unstemmed | Multi-Level Particle System Modeling Algorithm with WRF |
| title_short | Multi-Level Particle System Modeling Algorithm with WRF |
| title_sort | multi level particle system modeling algorithm with wrf |
| topic | 3D cloud modeling multi-level particle system WRF weighted clustering cascaded Bezier curve |
| url | https://www.mdpi.com/2073-4433/16/5/571 |
| work_keys_str_mv | AT julongchen multilevelparticlesystemmodelingalgorithmwithwrf AT binwang multilevelparticlesystemmodelingalgorithmwithwrf AT rundonggan multilevelparticlesystemmodelingalgorithmwithwrf AT xuepengmou multilevelparticlesystemmodelingalgorithmwithwrf AT shipingyang multilevelparticlesystemmodelingalgorithmwithwrf AT lingtan multilevelparticlesystemmodelingalgorithmwithwrf |