基于投影体积与八叉树的三维网格模型体素化方法

使用八叉树结构生成三维多边形网格模型的体素表示,提出了基于投影体积判断体素是否位于模型内部、再利用6-邻接关系快速准确获得模型内部体素的方法,避免了Flooding方法在处理内部封闭空腔时产生的错误.实例测试表明,该文提出的体素化方法能正确处理内部含有空腔的模型,并且效率较高,在三维GIS、地学建模和CAD等体图形学相关的领域具有一定的应用价值.     

INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS

Some intelligent algorithms (IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane (ZC) model to solve conditional nonlinear optimal perturbation (CNOP) for studying El Ni?o – Southern Oscillation (ENSO) predictability. Compared to the adjoint-based method (the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns. Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization (PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy (CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model (MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models.     

国家自然科学基金大气科学学科二级申请代码下设研究方向与关键词解读:D0510大气数据与信息技术

2021年,国家自然科学基金委员会进行了大气科学学科资助布局改革,形成了分属于“分支学科”“支撑技术”和“发展领域”三大板块的共15个二级申请代码的全新资助体系。作为“支撑技术”板块中的重要成员,“D0510大气数据与信息技术”申请代码旨在鼓励先进技术与方法的创新以及大气学科基础理论与技术的交叉融合。本文从改革背景、逻辑框架、内涵构成等方面对新编“D0510大气数据与信息技术”申请代码的四大类研究方向及关键词进行了专门解读,阐明了D0510主要侧重于提高包容性与覆盖面的设计思路,强调了D0510对“卡脖子”关键技术和潜在“颠覆性”技术的引领作用。本文对往年D0510各方向关键词的基金申请和文献使用情况进行了统计分析,以帮助相关科研人员及时把握D0510申请代码的发展趋势,充分理解研究方向与关键词的内涵和逻辑关系,避免研究方向和关键词的选取过于集中或者与其它板块申请代码间产生混淆等问题,从而为更准确地选择相应的研究方向与关键词提供参考。     

Ensemble Forecast for Tropical Cyclone Based on CNOP-P Method: A Case Study of WRF Model and Two Typhoons

In this paper, we set out to study the ensemble forecast for tropical cyclones. The case study is based on the Conditional Nonlinear Optimal Perturbation related to Parameter (CNOP-P) method and the WRF model to improve the prediction accuracy for track and intensity, and two different typhoons are selected as cases for analysis. We first select perturbed parameters in the YSU and WSM6 schemes, and then solve CNOP-Ps with simulated annealing algorithm for single parameters as well as the combination of multiple parameters. Finally, perturbations are imposed on default parameter values to generate the ensemble members. The whole proposed procedures are referred to as the Perturbed Parameter Ensemble (PPE). We also conduct two experiments, which are control forecast and ensemble forecast, termed Ctrl and perturbed-physics ensemble (PPhyE) respectively, to demonstrate the performance for contrast. In the article, we compare the effects of three experiments on tropical cyclones in aspects of track and intensity, respectively. For track, the prediction errors of PPE are smaller. The ensemble mean of PPE filters the unpredictable situation and retains the reasonably predictable components of the ensemble members. As for intensity, ensemble mean values of the central minimum sea-level pressure and the central maximum wind speed are closer to CMA data during most of the simulation time. The predicted values of the PPE ensemble members included the intensity of CMA data when the typhoon made landfall. The PPE also shows uncertainty in the forecast. Moreover, we also analyze the track and intensity from physical variable fields of PPE. Experiment results show PPE outperforms the other two benchmarks in track and intensity prediction.