一个ENSO多模式集合预报系统介绍

厄尔尼诺-南方涛动(El Ni?o and Southern Oscillation,简称ENSO)是短期气候预测的主要可预报性来源.为提高ENSO的预报水平,我们构建了一个包含5个动力耦合模式的多模式集合预报系统,其中各个模式具有不同的复杂程度、参数化方案、分辨率、初始化和集合扰动生成方案,以尽可能全面地考虑各种不确定性对预报结果的影响.回报试验结果表明:多模式集合的预报效果明显好于任何一个单一模式,其显著降低了均方根误差,并提高了异常相关性技巧.该多模式集合的预报效果可以比肩甚至优于同期北美多模式比较计划中的ENSO多模式集合预报技巧.此外,多模式集合预报能够削弱“春季预报障碍”的影响,提供更为可靠的概率预报.实时预报能够提前六个月准确地捕捉到近期连续两年的拉尼娜事件,并且该系统从2022年4月开始的预报也显示出连续三年拉尼娜事件的可能性.总体来说,这一多模式集合预报系统能够提供比任何模式成员更加准确的确定性预报和概率预报.这些改进主要得益于:一方面不同模式之间的优势互补增加了可预报信息,另一方面该系统集合成员规模较大有助于更为合理地刻画预报不确定性的分布.     

A pilot survey on antimicrobial activity and diversity of soil-derived actinobacteria from different depths in Gudzhirganskoe saline lake in Eastern Siberia

To explore pharmaceutical actinobacteria from salty environments in Siberia, 8 soil samples were collected from different depths of Gudzhirganskoe saline lake, the Republic of Buryatia, Russia.Totally, 635 actinobacterial strains affiliated with 21 genera in 12 families of 7 orders were obtained by culture-dependent approaches. The predominant genus was Streptomyces(74.5%), followed by Microbacterium(4.9%), Agromyces(3.5%), Nocardiopsis(3.0%), and Kitasatospora(2.5%). Seven strains showed relati...     

Application of soft computing techniques for shallow foundation reliability in geotechnical engineering

This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.     

New Cases of Superflares on Slowly Rotating Solar-type Stars and Large Amplitude Superflares in G-and M-type Main Sequence Stars

In our previous work, we searched for superflares on different types of stars while focusing on G-type dwarfs using entire Kepler data to study statistical properties of the occurrence rate of superflares. Using these new data, as a byproduct, we found 14 cases of superflare detection on 13 slowly rotating Sun-like stars with rotation periods of24.5–44 days. This result supports the earlier conclusion by others that the Sun may possibly undergo a surprise superflare. Moreover, we found 12 and se...     

The Variability and Radial Velocity of Planetary Nebula Central Stars

The extremely accurate estimates of stellar variability and radial velocity in the Gaia Data Release 3(Gaia DR3)have enabled us to examine the close binarity and radial velocity(RV) of central stars(CSs) of planetary nebulae(PNe). This study is twofold:(1) searching for new close binary CS candidates to better understand how binarity affects the formation and evolution of PNe; and(2) extending the sample size of known RVs of PNe in order to understand their kinematics and the dynamics of the Mil...     

加权整体最小二乘EIV模型与算法——与"加权整体最小二乘EIO模型与算法"一文的讨论

加权整体最小二乘方法是一种能同时顾及EIV（errors-in-variables）模型中系数矩阵和观测向量误差的参数估计方法。根据不同的应用场景,EIV模型则表现出不同的结构特征。"加权整体最小二乘EIO模型与算法"一文采用EIO模型处理EIV模型中的结构化问题^*。为了将其与现有方法进行对比,本文罗列出4种处理EIV模型结构特征的方法,并归纳了8种参数估计公式。同时从精度评定的角度讨论了整体最小二乘解的一阶及更高阶精度近似评定方法。需要强调的是,针对EIV模型及其参数估计理论可以从函数模型、随机模型和参数估计方法3个方面展开研究,但各方法殊途同归。     

中国作物物候对气候变化的响应与适应研究进展

以气候变暖为主要特征的气候变化对作物物候产生了重要的影响,通常气温升高会导致作物生长速度加快,生育期缩短,从而造成作物产量下降,不利于农业发展。同时,作物物候变化可以直接或间接反映气候变化情况,对于气候变化具有重要的指示意义。作物物候的研究对于农业气象灾害的预防、农业生产管理水平的进步以及农业产量提高都极为关键。随着全球地表气温的持续升高,作物物候相关研究也越来越引起科学家的关注。论文结合作物物候的主要研究方法,综述了中国近几十年来小麦、玉米、水稻以及棉花、大豆等主要农作物的生育期变化特征以及主要的驱动因子,得到以下主要结论：①在研究方法上,统计分析方法应用最为普遍,其他几种方法都需要与统计分析方法相结合使用。另外,作物机理模型模拟方法易于操作、可行性强,在物候研究中应用也比较多。遥感反演方法对作物生育期的特征规律要求较高,一般主要关注作物返青期。②整体上,小麦全生育期主要呈缩短趋势,而玉米和水稻全生育期以延长趋势为主。③作物物候变化的驱动因子主要是气候变化和农业管理措施改变,其中,气候变化是主导驱动因子,对作物物候变化起决定作用,而调整农业管理措施,在一定程度上抵消气候变化对作物生育期的不利影响。作物物候对气候变化的响应和适应研究可以为农业生产适应气候变化提供重要的理论依据和对策。     

广东省连平县西湖村金多金属矿床成矿地质特征及成因浅析

西湖村位于南岭成矿带南东缘,北东向河源深断裂带的南东段;北东向河源-邵武深断裂带、北西向连平-紫金深断裂带、东西向佛岗-丰良深断裂带交汇复合地带,区域构造活动、岩浆活动、成矿热液活动都较为活跃,具有有利的成矿地质背景。根据矿体的空间分布位置及矿石的矿物组合特征,认为本矿床类型为破碎带蚀变岩型金矿床。     

非等间距GM(1,1)模型的总体最小二乘算法及其病态问题

在非等间距GM(1,1)模型中,系数矩阵中有无误差的常数项和有误差的随机项,并且系数矩阵与观测向量误差同源,即系数矩阵与观测向量中有相同的元素存在,这些相同元素应该有相同的改正数,为此本文推导了一种适合非等间距GM(1,1)模型求解的总体最小二乘算法。同时,考虑到非等间距GM(1,1)模型中存在病态问题时影响总体最小二乘计算结果的稳定性,提出对系数矩阵常数列乘以某一常数的方法,以改善病态问题。