营口地区一次暴雨过程中的预报失误分析

[目的]分析营口地区一次暴雨过程中预报失误的原因。[方法]利用自动站降水资料和MICAPS资料,对2010年7月19~22日营口地区出现的暴雨天气过程进行了分析;并利用雷达、云图资料和数值预报产品,分析了7月21日的预报失误原因。[结果]造成7月21日暴雨预报失误的主要原因是：①此次降水过程持续时间长,降水不连续且分布不均,21日的强降水与前2个时段的强降水不同,是局部短时强降水,此类降水预报难度大;②数值预报不稳定,误差大;③20日夜间已经出现强降水,与21日傍晚出现的强降水间隔时间不长,容易使预报员疏忽,并且在此期间雷达和云图的暂时静寂也形成了一定的干扰。对于暴雨预报的着眼点应该立足于数值预报的形势预报,而不应将注意力放在要素预报上;应坚持以形势分析判断为主,要素判断为辅的原则;对于强降水的预报,数值预报往往误差较大,不能盲信,预报员应根据经验对其进行正确的订正预报。[结论]该研究为暴雨的预报分析提供参考依据。     

基于贝叶斯概率的印度洋大眼金枪鱼渔场预报

本文采用贝叶斯概率为模型基础框架,利用来自印度洋金枪鱼管理委员会(IOTC)的大眼金枪鱼延绳钓历史渔获统计数据和美国国家海洋大气管理局(NOAA)的海温最优插值再分析数据,进行适用于印度洋金枪鱼延绳钓渔场的模型参数估算与预报模型构建。模型回报精度验证结果表明,印度洋大眼金枪鱼延绳钓渔场综合预报的准确率达到了65.96%。模型预报结果用概率百分比来表示,符合渔业资源分布的客观特点。利用中分辨率成像光谱仪MODIS提供的SST产品进行业务化运行的渔场预报,利用模型结果每周生成印度洋大眼金枪鱼延绳钓渔场概率预报图,用不同大小的圆形来表示渔场概率的高低,可以为印度洋区域的远洋渔业生产提供信息支持。     

宁夏红寺堡区水资源承载能力分析与研究

分析了宁夏红寺堡区当地水资源、扬黄水资源、可利用水资源的状况和用水现状。结合红寺堡区发展规划,在大力推广节水灌溉技术应用、种植结构调整、节水工程改造、节水灌溉制度应用的前提下,挖掘灌区用水潜力;设定区域用水水平,预测区域在2015年工业、农业、人畜、新增项目用水需求,分析区域水资源可利用量与用水需求的关系,评价区域水资源承载能力,为区域发展决策者提供参考。     

松原市粮食产量预报方法初探

通过对松原市粮食产量资料分析得出,玉米和水稻分别占总粮食产量的74.13%和12.43%,其他作物占产量的13.44%。采用等权重不同间距滑动平均得到的气象产量序列,间接反映出了因气象条件优劣造成的丰歉年景。由18个因子筛选组建的玉米产量预报方程验证结果,平均绝对误差仅0.08,平均相对误差〈1%。另外,单独组建的其他粮产预报方程,复相关系数均通过0.001信度水准检验。     

金坛地区白背飞虱的测报与防治

总结了1995年以来金坛地区白背飞虱的测报与发生情况,对田间虫卵量高的年份作了重点分析,并对白背飞虱的测报与防治药种提出了合理的建议。     

A predictive model for early-warning of Septoria leaf blotch on winter wheat

Disease–weather relationships influencing Septoria leaf blotch (SLB) preceding growth stage (GS) 31 were identified using data from 12 sites in the UK covering 8 years. Based on these relationships, an early-warning predictive model for SLB on winter wheat was formulated to predict the occurrence of a damaging epidemic (defined as disease severity of 5% or > 5% on the top three leaf layers). The final model was based on accumulated rain > 3 mm in the 80-day period preceding GS 31 (roughly from early-February to the end of April) and accumulated minimum temperature with a 0°C base in the 50-day period starting from 120 days preceding GS 31 (approximately January and February). The model was validated on an independent data set on which the prediction accuracy was influenced by cultivar resistance. Over all observations, the model had a true positive proportion of 0.61, a true negative proportion of 0.73, a sensitivity of 0.83, and a specificity of 0.18. True negative proportion increased to 0.85 for resistant cultivars and decreased to 0.50 for susceptible cultivars. Potential fungicide savings are most likely to be made with resistant cultivars, but such benefits would need to be identified with an in-depth evaluation.     

贵德县梨树始花期与气象因子的相关分析及预报模型

为准确预报贵德地区梨树的始花期提供方法支持。笔者利用2007—2014 年青海省贵德县气象局观测的梨树始花期资料和地面气象观测资料,对影响梨树始花期的气象因子进行分析。结果表明：影响梨树始花期的主要气象因子是气温稳定通过5.0℃到3 月底的积温值。利用逐步回归分析方法建立了基于主要气象因子的梨树始花期预报模型,用所建立的预测模型对2007—2014 年梨花始花期进行回测,准确率较高。对2015 年梨树始花期日期进行预测,预测值与实际观测值之间相差1 天,预报值基本吻合。所建立的预测模型能准确预测贵德县梨树始花期的预报。     

监测采样间隔对作物模型性能的影响研究

为了获得不同监测采样间隔对作物生长模型的影响规律,避免现有研究和应用中存在的主观性或随意性,以番茄叶片蒸腾速率和CO2交换率为对象,采用3 种采样间隔数据及2 种常用方法进行建模、预测和比较研究。研究结果显示,在15、30、60 min的监测采样间隔下,番茄叶片蒸腾速率模型和CO2交换率模型的预测误差是不同的,GA-BP神经网络模型的预测能力普遍优于纯二次回归模型,但两者的结论是一致的,即对蒸腾速率和CO2交换率而言,最合适的监测采样间隔分别为30 min和15 min。本研究结果为监测采样间隔的设定、试验数据的选取和建模方法的运用提供依据,对作物模型研究和应用具有重要意义。     

中长期径流预报模型优选研究

【目的】对中长期径流预报模型进行比较和优选,为中长期径流预报模型的应用及提高预报精度提供参考。【方法】以黄河流域黑石关、龙门、民和水文站年径流为研究对象,利用灰色预测进行趋势分析、逐步回归进行周期分析,两者耦合建立灰色-逐步回归周期模型;利用逐步回归分析确定均生函数的周期性基函数,耦合建立均生函数-逐步回归模型;利用灰色-逐步回归中的趋势项和周期项作为预报因子,建立投影寻踪回归模型;利用前4年的实测径流数据预报当年径流,建立BP神经网络模型,并通过信息熵原理进行站点模型的综合评价和优选。【结果】建模期除黑石关水文站BP神经网络模型外,各站点拟合预报的平均相对误差均小于11.0%,合格率均大于90%。验证期除民和水文站灰色-逐步回归周期模型外,各站点平均相对误差均小于20%,合格率等于或大于80%,满足精度要求。【结论】均生函数-逐步回归径流预报模型可作为黑石关水文站的优选模型,BP神经网络径流预报模型可作为龙门和民和水文站的优选模型。     

Application of modified Deep Belief Network in Forecasting Cotton Diseases and Insect Pests

[Objective] The occurrence and development of cotton diseases and insect pests are mainly related to environmental information. Because this environmental information is various, complex and unstable, the study on the prediction methods of cotton diseases and insect pests is a certain challenge. This study aims to establish a forecasting model for the timely and accurate prediction of cotton diseases and insect pests. [Method] A forecasting model of cotton diseases and insect pests is proposed based on environmental information and a modified Deep Belief Network (DBN) that is constructed by a three-layer restricted Boltzmann machine (RBM) and a supervised back-propagation (BP) network. In the method, the RBM is used to transform the original environmental information vectors into a new feature space related to the diseases and pests; the BP network is trained to classify and forecast the features generated by the last RBM layer and two rules of dynamic learning and comparison and dispersion are adopted to accelerate the training process of RBM. The proposed model was validated on a dataset of cotton bollworm, aphids, spider, cotton Verticillium wilt, and Fusarium wilt in a recent six-year period. [Result] Compared with the traditional prediction models of cotton diseases and insect pests, the proposed model can deeply explore the extensive correlation between the occurrence of cotton diseases and pests and environmental information. The results show that the proposed model has a higher accuracy compared with the classical predictive models, and the average forecasting accuracy is above 83%. [Conclusion] The proposed method is an effective crop disease and pest forecasting method that can provide a technical support for preventing cotton disease and insect pests.