Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.     

Impact of Assimilating Radiances with the WRFDA ETKF/3DVAR Hybrid System on Prediction of Two Typhoons in 2012

The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform ...     

“频率匹配法”在集合降水预报中的应用研究

基于“频率匹配法”的思路,采用两种方法进行了集合降水预报的订正研究,一种方法是利用集合成员降水频率订正简单集合平均平滑效应的“概率匹配平均”法,另一种方法是利用实况降水频率订正集合成员降水预报系统偏差的“预报偏差订正”法,通过个例和批量试验,结果表明:(1)概率匹配平均法可以矫正简单集合平均的平滑作用所造成的小量级降水分布范围增大而强降水被削弱的负作用,这种改进对强降水区更显著,并且集合系统离散度越大这种改进也越大;但该方法对预报区域内总降水量的预报没有改进作用,不能改善预报的系统性偏差.(2)虽然预报偏差订正法对降水落区预报的改进有限,但可以订正模式降水预报的系统性误差,改进雨量预报以及集合预报系统的离散度特征和概率预报技巧;直接对集合平均预报进行偏差订正的效果优于单个成员偏差订正后的简单算术平均.(3)在对每个集合成员的降水预报进行偏差订正后,概率匹配平均仍可改善其简单平均的效果,因此在实际业务中,应该综合采用上述两种方法,以获得在消除系统性偏差的同时各量级降水分布又合理的集合平均降水预报.     

MJO预报研究进展

热带大气季节内振荡 (Madden-Julian oscillation,MJO) 是次季节-季节时间尺度气候变率的支配模态。它不仅对低纬度地区天气气候产生重要影响,还能够通过经向传播和激发大气遥相关波列对中高纬度地区产生影响,是延伸期尺度最重要的可预报性来源。因此,MJO预报是次季节-季节气候预测中极为重要的部分,近年来受到国际学术界广泛关注。该文回顾了MJO预报发展历史,概述了当前国际上主要科研业务机构的MJO预报发展现状。目前基于统计方法和气候模式的MJO预报研究取得了较大进展,特别是多个耦合气候模式和一种基于时空投影方法的统计模型均能够显著提升MJO预报技巧 (有效预报可达20 d以上)。该文还介绍了中国气象局国家气候中心在MJO预报技术发展和业务系统研制方面的新进展,当前基于第2代大气环流模式的MJO业务预报填补了国内空白,技巧为16~17 d,而耦合气候模式试验的技巧已达到约20 d。总体来看,利用耦合模式预报MJO是未来发展的主要方向,其中,面向MJO的模式初始化和集合预报新方法研究将是关注重点。     

低温雨雪过程的粒子群-神经网络预报模型

利用逐日气温和降水量数据、NCEP/NCAR再分析资料以及预报场资料,通过分析提取我国南方区域持续性低温雨雪过程及其预报因子,使用粒子群-神经网络方法建立非线性的统计集合预报模型 (PSONN-EPM),对我国南方区域持续性低温雨雪过程进行预报试验。结果表明:以过程的冷湿程度及影响范围为标准,将低温雨雪过程分为一般过程和严重过程,并建立不同的预报模型效果较好。通过10 d独立样本预报试验看,基于粒子群-神经网络方法建立的集合预报模型比基于逐步回归方法建立的预报模型的预报平均相对误差小,对严重过程预报能力高于对一般过程预报,且这种非线性统计集合建模方法在建模过程中不需要调整神经网络参数,在实际预报业务中值得尝试。     

陕北能源化工基地采煤对地下水资源的影响及对策

陕北能源化工基地侏罗纪煤田是中国煤炭工业战略西移的首选基地,采煤引起的地下水资源及生态环境响应的研究是一个不可逾越的课题。从分析水文地质工程地质条件入手,将煤田区划分为7个水文地质结构类型和5个含水系统,按照排泄基准面进一步将含水系统划分若干地下水流子系统。在经验公式和数值法预测结果的基础上,综合考虑研究区实际,提出了本区能够保障生态、水资源和采煤安全的冒裂带高度为240 m,"三带"安全高度为300 m,并据此将煤采区划分为非导水区、无水导水区、贫水导水区和富水导水区。从水资源的角度提出了"保水采煤"、"煤水共采"和"含水层再造"的煤炭资源开发近期和中远期对策。     

WEP模型全局参数敏感性分析及其在汉江上游流域的应用

水文模型敏感性分析是用来确定控制模型效率关键参数非常有效的过滤工具之一,并可以帮助理解模型结构,乃至发现模型的结构缺陷,从而有可能改善模型结构.本文通过将全局敏感性分析方法的LH-OAT方法应用到WEP-L模型.并在汉江上游向家坪以上流域进行了成功应用.研究发现.通过LH-OAT方法分析,对于汉江上游向家坪以上流域,河道曼宁糙率和坡面曼宁糙率是影响NaSh效率系数最重要的参数.通过对这两个参数的手动调参,模拟的日Nash效率系数从0.35提高到0.75.     

Semisupervised heterogeneous ensemble for ship target discrimination in synthetic aperture radar images

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.     

大口黑鲈(Micropterus salmoides)致病性维氏气单胞菌的分离鉴定及其特性分析

大口黑鲈(Micropterus salmoides)是我国重要的淡水养殖经济鱼类品种,目前包括细菌性疾病在内的诸多因素影响了其养殖业的健康发展。维氏气单胞菌是引起淡水鱼败血症和溃疡综合征的重要病原菌。从患病大口黑鲈肝脏组织中分离到1株病原菌,通过形态学观察、生理生化特性分析、16S rRNA序列和特定毒力基因分析对其进行鉴定,并通过人工回归感染试验和药敏试验分析其致病性和耐药性。结果表明该菌为致病性维氏气单胞菌(Aeromonas veronii),含有aer、act、lip、fla、gcaT和OMPA I等毒力基因;药敏试验显示其对头孢曲松、氯霉素、氟苯尼考、强力霉素、四环素、多粘菌素、复方新诺明、左氟沙星等8种抗生素高度敏感,对新霉素、恩诺沙星、庆大霉素、链霉素、阿齐霉素、环丙沙星等6种抗生素中度敏感,对其他8种抗生素具有一定耐药性;人工回归感染实验表明该菌具有较高的致病性,对大口黑鲈的半致死浓度为1.4×10⁶ CFU/mL。维氏气单胞菌是水产养殖中的条件性致病菌,研究结果揭示了该菌株的部分生物学特性,有利于丰富维氏气单胞菌属的基础数据,同时也为鲈鱼养殖过程中的病害防控提供了一定的参考依据。     

基于深度学习的滑坡灾害易发性分析

滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。