最优气候均态法在台风长期预报中的应用

最优气侯均态法是制作气温年度预报的一种行之有效的方法。本文将它推广于台风的长期预报,用Ｋ年平均值作９０年代台风的年和月的生成数和登陆数预报,发现该方法简便、实用,其准确率达到和超过了长期天气预报的要求。     

烟威渔场盐度预报及分析

本文以我国北黄海的烟威渔场为研究对象,采用优选因子场预报模式,对盐度的时空变化进行预报试验。文中对所采用的统计预报方法,作了简要介绍。对影响本海区盐度变化的显著因子及预报结果作了初步分析。预报试验表明:预报的总体平均绝对误差为0.27‰,预报相对误差在18％左右,预报误差小于0.5‰的站数占总站数的85％,预报趋势与实测资料基本一致。     

一种分析台风路径预报误差的新方法

台风路径预报误差应包括距离误差和方向误差两个方面,而在以往的业务应用和科学研究中偏向于只考虑对应时刻预报和观测位置点间的距离误差。本文对评估台风路径误差的方法进行了改进,在距离误差基础上建立了一种新的表征台风路径误差的方法——多因子误差法。本文以中国气象局整编的热带气旋最佳路径数据为标准,对2008和2009年中国气象局、美国台风联合预警中心和日本气象厅预报的西北太平洋热带气旋路径,采用新的表征热带气旋路径预报误差的多因子误差法进行分析,并与普遍采用的距离误差法进行对比,结果表明:多因子误差分析方法和距离误差分析方法存在比较明显的差异,多因子误差法优势比较明显。本文是对更科学的台风路径预报误差评定方法的有益探索,有一定的参考价值。     

隶属函数的方法进行等级预报及隶属函数的构造

模糊数学在气象、水文预报领域中已经得到广泛的应用。其中，用隶属函数的方法作预报对象的多等级长期预报，能取得较好的效果。本文简要介绍用隶属函数的方法进行长期多等级预报的基本思路和步骤，重点介绍在作上述预报过程中，构造隶属函数的一种方法。     

优选因子场预报方法的研究

现行的场预报方法,一般是根据预报量之间的统计关系,进行场分解,而后用经验正交函数建立预报方程,但却很少考虑到外界因子的作用。本文尝试把影响因于引入经验正交预报方法中。在这种优选因子场预报方法中,作者不仅考虑预报量之间内部的相互关系,而且也考虑了外部因子的影响,借助于从综合因子中优选因子,可以建立对于预报数量和等级都适用的最优预报方程。提出了权重相关系数的概念。与其他相关系数之间的明显差别是,它具有场的特征。本文还介绍了水湿的盐度预报的一些实例。     

应用模式输出统计作海雾出现判别预报

应用模式输出统计的逐步判别方法，对山东南部沿海４～７月海雾出现作２４ｈ的判别预报。候选预报因子包括：日本数值预报传真图、近岸台站的观测、经验设计因子及其一些组合因子。对１９９３年４～７月独立资料，进行试报检验，结果表明，预报准确率达７７％     

试用隶属度与贴近度作海雾长期预报

青岛近海海雾多发生在4-7月份，是沿海灾害性天气之一。它对渔业生产、航海运输和国防建设影响很大。因此作好4-7月份海雾的长期预报有十分重要的意义。由于海雾的多寡和前期因子之间是一种模糊关系，     

一种基于深度学习的热带气旋路径集成预报方法

本文提出了一种基于深度学习的热带气旋(tropical cyclone,TC)路径集成预报方法.该方法以长短期记忆深度网络为模型构架,利用前4个时刻(24 h,间隔6 h)及当前时刻的TC路径记录,以及由不同环境因素所计算的方向预报因子作为输入,分别直接预报和间接(通过预报移动速度)预报路径,集成两者预报结果实现时效为...     

烟威渔场盐度预报及分析

本文以我国北黄海的烟威渔场为研究对象,采用优选因子场预报模式,对盐度的时空变化进行预报试验。文中对所采用的统计预报方法,作了简要介绍。对影响本海区盐度变化的显著因子及预报结果作了初步分析。预报试验表明:预报的总体平均绝对误差为0.27‰,预报相对误差在18％左右,预报误差小于0.5‰的站数占总站数的85％,预报趋势与实测资料基本一致。     

统计预测中虚假因子的识别理论及其在预测实践当中的应用(一)——偶然性单相关的识别与过滤

在统计预测中,如何选取物理意义明确、预报准确率又高的预报因子,几乎是统计预测成功与否的关键。在实际预测当中,常会碰到这样的情况:某些因子与预报量的单相关率甚高,但用它们构成预报方程并做预报时,却常常导致失败。事后分析发现,这些因子并非由于物理上的原因,而仅仅由于在有限样本的情况下（在实际预报中样本客量常常很小）,预报因子序列偶然地与预报量序列具有很高的相关性。本文把这样的因子称为虚假因子,并把这种相关称为偶然性单相关。 为了研究的方便,作者将因子和预报量分别化为（0,1）序列,并提出了一种最佳（0,1）化算法。在因子X和预报量Y分为（0,1）两级的情况下,假定它们相互独立,于是有 P_i（x,y）=P_i（x）P_i（y）即:X和Y在i级的正相关率等于各自出现在i级的概率之积。引进统计量 式中υ_i是（X,Y）实际出现在i级内的频数,n为子样总数。用该统计量对预报因子进行显著性检验,就能有效地识别和过滤假因子。 本文最后将理论应于台风的登陆,转向预报。显著水平为0.001时,有8个预报因子通过检验。从中选取η值较大的5个,用简单的编码相关法构成预测模型,其预报准确率为94.1％。     

灰色拓扑预测模型在初台预测中的应用

本文应用灰色系统理论，建立了南海初台的拓扑预测模型，并利用这些模型对南海初台未来十年出现日期作了预测分析，为南海台风的长期预测提供信息。     

Data-based yearly forecasting of beach volumes along the Dutch North Sea coast

Beach and nearshore levels have been measured yearly along the entire Dutch North Sea coast since the mid 1960s (the ‘Jarkus’ data set). This data set has been processed to create separate time series of beach volumes at longshore intervals of about 250 m, giving over 2000 time series in total. These time series typically show a high annual variability with weak long-term trends. The present Dutch national coastal management strategy involves making year-ahead forecasts of beach volumes by extrapolating a linear least squares trend through the previous ten years' data separately for each longshore location. In this paper, these forecasts are shown to be worse than the trivial forecast in which the most recently measured beach volume persists unchanged into the future, with a mean square error (MSE) about 13.5% worse (equivalent to a root mean square error (RMSE) 6.5% worse). Improvements to these forecasts are sought by testing six different univariate forecasting methods. The two best methods improve on the persistence of the most recently measured beach volume by about 15% MSE (8% RMSE), and on the presently used linear least squares trend method by about 25% MSE (13.5% RMSE). Further comparisons are made between the forecasting methods to investigate several factors. These include varying the amount of fitting data for the forecasting methods, smoothing of the fitting data, different methods for interpolating gaps in the data, the longshore aggregation of data, making forecasts for coastal profiles with and without nourishments, and making forecasts up to five years ahead. These forecasting methods are designed as a coastal management tool to provide yearly forecasts quickly and routinely for the whole Dutch North Sea coast.     

渤海冬季气温预报方法研究及应用

本文利用地磁指数的长序列资料，提出了一个段因子的普查方法，建立了地磁指数与渤海冬季气温的预报关系。多年来对渤海冬季气温长期预报的应用结果表明，效果良好。     

三种基于均生函数的统计模型在厄尔尼诺预测中的应用

为了探究厄尔尼诺的预测方法,提高其预报准确度与预报时效,本文基于均生函数的基本原理,在普通均生函数模型中分别加入气候序列的本征模函数和预报因子变量,构建了基于EEMD(Ensemble EmpiricalModeDecomposition)的均生函数模型和多变量均生函数模型;并应用三种方案对NINO3区海温指数进行了预报试验。结果表明,两种改进模型对厄尔尼诺的预报效果好于普通均生函数模型,是提高预报准确度的有效手段;同时,利用统计的预报模型,可以在一定程度上有效延长厄尔尼诺的预报时效,具有一定实践意义。     

西北太平洋热带气旋中期路径的统计释用综合预报模式

提出一个热带气旋中期路径的统计释用综合预报模式。该模式应用ＴＣ参数，初妈时刻及未来时刻的环境场参数和导出因子，构造了多种中期天气时空惊讶的预报因子；应用数值天气预报产品的统计释用技术和多种分类预报模式的综合集成，构造了ＴＣ中期路径的客观定量预报模式。     

浙北地区夏季异常高温的一种长期预报方法

本文提出了预报浙北地区夏季出现异常高温的一种长期预报方法,采用逻辑结构形式方法来描述预报对象和预报因子之间的关系,并用数理逻辑中的命题公式来描述本文的预报问题,进行逻辑推理预报。从历史资料的回报结果和1990年的预报结果来看,效果是令人满意的。最后,对提出的方法进行了若干讨论。     

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves, a forecasting model for wave heights at three stations in the Bohai Sea has been developed. This model is based on long short-term memory(LSTM) neural network with sea surface wind and wave heights as training samples. The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input, the prediction error produced by the proposed LSTM model at Sta. N01 is 20%, 18% and 23% lower than the conventional numerical wave models in terms of the total root mean square error(RMSE), scatter index(SI) and mean absolute error(MAE), respectively. Particularly, for significant wave height in the range of 3–5 m, the prediction accuracy of the LSTM model is improved the most remarkably, with RMSE, SI and MAE all decreasing by 24%. It is also evident that the numbers of hidden neurons, the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy. However, the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used. The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training. Overall, long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.     

Local model approximation in the real time wave forecasting

The notion of data assimilation is common in most wave predictions. This typically means nudging of wave observations into numerical predictions so as to drive the predictions towards the observations. In this approach, the predicted wave climate is corrected at each time of the observation. However, the corrections would diminish soon in the absence of future observations. To drive the model state predictions towards real time climatology, the updating has to be carried out in the forecasting horizon too. This could be achieved if the wave forecasting at the observational network is made available. The present study addresses a wave forecasting technique for a discrete observation station using local models. Embedding theorem based on the time-lagged embedded vector is the basis for the local model. It is a powerful tool for time series forecasting. The efficiency of the forecasting model as an error correction tool (by combining the model predictions with the measurements) has been brought up in a forecasting horizon from few hours to 24 h. The parameters driving the local model are optimised using evolutionary algorithms.     

Research on the fishing season forecasting with the freedom adjustment of double correlation coefficient

昒sually, the stepwise regression method is used to select the forecasting factors in the most forecasting problems and the double correlation coefficient is used as a standard for the judge ment of the forecasting function. In fact, it cannot be decided whether the forecasting function is to be adopted or not only according to whether the double correlation coefficient is large or small. Here, we used the freedom adjusting method of the double correlation coefficient to study the season forecasting and used the method to select the factors for the fishing season forecasting function of the Spanish Mackerel (Scomberomorus) at the estuary of the Changjiang River. The test result of reforecasting is satisfactory and the greatest error is only two days as compared with the real fishing season. The forecasting result of the fishing season is also satisfactory.     

WRF、EC和T639模式在福建沿海冬半年大风预报中的检验与应用

基于福建省冬半年沿海和港湾岛屿自动站的逐时极大风观测资料和WRF(Weather Research and Forecast)、EC(European Centre for Medium-Range Weather Forecasts)细网格以及T639(TL639L60)三种模式预报的10 m 风场资料, 将模式预报的风向风速与观测资料进行对比检验, 结果表明: 福建省沿海冬半年大风的盛行风向以东北风为主, 大风的时空分布极为不均, 沿海风力的脉动性、跳跃性、局地性突出。从三种模式对风速风向的模拟效果来看, WRF 和EC 细网格的预报效果较好, 有可参考性, T639可参考性不高。对于风速, 模式预报结果相比实况极大风速偏小, 港湾岛屿代表站风速的平均绝对误差均小于沿海代表站, 预报平均误差由沿海向内陆逐渐减小, 由中部向南北逐渐减小。风向相比风速的预报效果要差, WRF 和EC 细网格的风向预报误差在45°～50°, 有一定的参考意义; 港湾岛屿代表站风向的平均绝对误差大于沿海代表站, 以浮标站的误差最大。当观测风速出现7 级及以上风速时, 若对大风进行分级检验, 则较低风速的预报平均绝对误差小于较高风速; 风向预报的平均绝对误差也大大降低, 且误差都在45°以内, 具有良好的参考性。