多旋翼无人机搭载二维超声风传感器观测研究

利用多旋翼无人机搭载二维超声风传感器进行40 m低空悬停测风试验,试验结果表明无人机搭载二维超声风传感器测风与同高度梯度塔静态测风结果时序性趋势吻合,两者风速平均偏差0.46 m/s,风速均方根误差1.15 m/s,风向平均偏差4.21°,风向均方根误差16.68°风速出现系统性高估。无人机测风结果与静态测风结果差异与环境风速关系显著,而与环境风向观测不明显。同时随无人机倾角增大,两种方法绝对误差表现为先减小后增大。

Predicting climate anomalies:A real challenge

In recent decades,the damage and economic losses caused by climate change and extreme climate events have been increasing rapidly.Although scientists all over the world have made great efforts to understand and predict climatic variations,there are still several major problems for improving climate prediction.In 2020,the Center for Climate System Prediction Research(CCSP) was established with support from the National Natural Science Foundation of China.CCSP aims to tackle three scientific problems related to climate prediction—namely,El Ni?o-Southern Oscillation(ENSO) prediction,extended-range weather forecasting,and interannual-to-decadal climate prediction—and hence provide a solid scientific basis for more reliable climate predictions and disaster prevention.In this paper,the major objectives and scientific challenges of CCSP are reported,along with related achievements of its research groups in monsoon dynamics,land-atmosphere interaction and model development,ENSO variability,intraseasonal oscillation,and climate prediction.CCSP will endeavor to tackle key scientific problems in these areas.

Research Framework for Ecosystem Vulnerability:Measurement, Prediction, and Risk Assessment

The fragility of ecosystem health has become a key factor hindering the sustainable development of the ecological environment. Through a review of published research from domestic and foreign scholars, starting from the endogenous logic of studies in the field of ecosystem vulnerability(EV), this paper sorts out the literature on the aspects of measurement models, prediction methods and risk assessment, comprehensively defines the research category and scientific framework of EV, and analyzes the research ideas and development trends. We arrived at the following conclusions: 1) The connotation of ecosystem vulnerability not only embodies the change in the vulnerability of the natural environment, but it also reflects the irreversible damage to the ecosystem caused by excessive development and industrial production activities. 2) The setting of ecosystem vulnerability indices should aim to fully reflect the essential features of that vulnerability, which should include the index systems of natural, social, economic and other related factors. 3) There are many types of ecosystem vulnerability measurement methods, prediction models and risk evaluation models, which have different focuses and advantages. The most appropriate method should be adopted for conducting comprehensive and systematic evaluation, prediction and estimation according to the different representation and evolution mechanisms of the chosen research object and regional ecosystem vulnerability. 4) Based on the regional system characteristics, corresponding risk management measures should be proposed, and pertinent policy suggestions should be put forward to improve the ecological safety and sustainable development of an ecologically vulnerable area.

An ensemble-based likelihood ratio approach for family-based genomic risk prediction

Objective: As one of the most popular designs used in genetic research, family-based design has been well recognized for its advantages, such as robustness against population stratification and admixture. With vast amounts of genetic data collected from family-based studies, there is a great interest in studying the role of genetic markers from the aspect of risk prediction. This study aims to develop a new statistical approach for family-based risk prediction analysis with an improved prediction accuracy compared with existing methods based on family history. Methods: In this study, we propose an ensemble-based likelihood ratio(ELR) approach, Fam-ELR, for family-based genomic risk prediction. Fam-ELR incorporates a clustered receiver operating characteristic(ROC) curve method to consider correlations among family samples, and uses a computationally efficient tree-assembling procedure for variable selection and model building. Results: Through simulations, Fam-ELR shows its robustness in various underlying disease models and pedigree structures, and attains better performance than two existing family-based risk prediction methods. In a real-data application to a family-based genome-wide dataset of conduct disorder, Fam-ELR demonstrates its ability to integrate potential risk predictors and interactions into the model for improved accuracy, especially on a genome-wide level. Conclusions: By comparing existing approaches, such as genetic risk-score approach, Fam-ELR has the capacity of incorporating genetic variants with small or moderate marginal effects and their interactions into an improved risk prediction model. Therefore, it is a robust and useful approach for high-dimensional family-based risk prediction, especially on complex disease with unknown or less known disease etiology.