A simulation method of three-dimensional cloud over WRF big data

Abstract

Nowadays, due to the expansion of people's living ranges and the impact of human life on the natural environment, climate changes fiercely than before. In order to observe the changing climate environment accurately, multi-modal sensors are used to collect the various data around us, and we could analyze and predict the weather based on these collected data. One of the applications is 3D visualization simulation, and the 3D visualization simulation of cloud data has always been the research hotspot in the field of computer graphics and meteorology. Currently, it is a key challenge to resolve the problems of 3D cloud simulation, such as reducing complexity of modeling and computation and improving the real-time performance. Technically, a method for data modeling and optimizing based on Weather Research and Forecasting (WRF) is proposed in this paper, aiming to solve the problems of the existing 3D cloud simulation and realize 3D virtual simulation of real-world cloud data. According to the characteristics (e.g., color, size, shape) of the cloud, the spherical particle system is designed to model, and the initial color, size, shape, and other attributes are given to these spherical particles to realize the modeling of WRF cloud data. From the perspective of new particles' generation, the level of detail (LOD) technique, based on the relationship between the quantity of new generated spherical particles and the distance of the viewpoint, is used to change the quantity of new particles generated in real time according to the distance of the simulated scene distance. Finally, illumination model is introduced to render and simulate the modeling particles. Experimental simulation results verify the effectiveness of this method in improving the modeling and rendering speed of cloud data as well as the fidelity of the 3D virtualization model.

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

“…A high performance query analytical framework that proposes grid transformation can provide a complex climate data observation and model simulation (L et al, 2017). For climate environmental analyses, a 3D visualization simulation of cloud data is gaining attention in the fields of computer graphics and meteorology (Xie Y. et al, 2019).…”

“…Cloud tracking (He et al, 2020) further helps to assess the evolution of air pollution, the reliability of which can be further enhanced with statistical downscaling solutions (Wang Q. et al, 2020). The time-series data (Joshi et al, 2019) extracted from satellite images support long-term forecasts, but the description of cloud motion (Xie Y. et al, 2019) can also be used to refine shorter-term analyzes. The use of satellite imagery as a data source in urban planning also helps identify climate-friendly solutions (Milojevic-Dupont et al, 2020).…”

“…Climate-friendly urban planning (Milojevic-Dupont et al, 2020), monitoring the energy demand of buildings (Gouveia and Palma, 2019), and defining disaster resilience (Sasaki et al, 2020) play an important role in achieving sustainable cities and communities (SDG11). The Climate Action goal (SDG13) tackles most data gaps, so research such as linking satellite images to Semlali and El Amrani (2021) with air quality, preprocessing them (Meraner et al, 2020;Qin and Chi, 2020;Semlali et al, 2020), the analysis of time series data (Ise et al, 2020) and its exploration (Joshi et al, 2019), downscaling (Wang Q. et al, 2020) techniques, enrichment of precipitation and temperature data (Jimenez et al, 2019), tracking the movement of clouds (Xie Y. et al, 2019), or just using IoT sensors (Mabrouki et al, 2021) are all key in creating a strategy to support the achievement of the climate goal. For the sustainability of life below water (SDG14), marine life prediction models (Coro et al, 2020) and human coastal activity (Kubo et al, 2020) can be integrated.…”

Atmospheric Cloud Representation Methods in Computer Graphics: A Review

“…Chen et al [81] proposed a sketch-based retrieval system from pre-generated cumulus cloud models for cumulus cloud scene construction by generating 3D cloud models from natural images and then constructing a 3D cloud model database. Xie et al [7] analyzed the weather forecast data and build a group of the spherical-shape representation that constructs the basic shape of clouds.…”

“…Many atmospheric cloud representation methods (e.g. [3]- [7]) have been proposed for the last forty years by the computer graphics community to solve different issues regarding the visualization of atmospheric clouds. However, lack of review papers could be referred on the cloud representation methods available in the area of computer graphics caused some difficulties for researchers especially novice and inexperienced researchers to understand and choose the appropriate solutions for their research work.…”