Multi-Level Particle System Modeling Algorithm with WRF

Abstract

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.