Applications of Bias-removed Ensemble Mean in the Gale Forecasts over the Yellow Sea and the Bohai Sea

Based on the daily sea surface wind field prediction data of Japan Meteorological Agency(JMA) forecast model,National Centers for Environmental Prediction(NCEP GFS) model and U.S.Navy Operational Global Atmospheric Prediction System(NOGAPS) model at 12:00 UTC from June 28 to August 10 in 2009,the bias-removed ensemble mean(BRE) was used to do the forecast test on the sea surface wind fields,and the root-mean-square error(RMSE) was used to test and evaluate the forecast results.The results showed that the BRE considerably reduced the RMSEs of 24 and 48 h sea surface wind field forecasts,and the forecast skill was superior to that of the single model forecast.The RMSE decreases in the south of central Bohai Sea and the middle of the Yellow Sea were the most obvious.In addition,the BRE forecast improved evidently the forecast skill of the gale process which occurred during July 13-14 and August 7 in 2009.The forecast accuracy of the wind speed and the gale location was also improved.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

The Relationship between Deterministic and Ensemble Mean Forecast Errors Revealed by Global and Local Attractor Radii

It has been demonstrated that ensemble mean forecasts, in the context of the sample mean, have higher forecasting skill than deterministic(or single) forecasts. However, few studies have focused on quantifying the relationship between their forecast errors, especially in individual prediction cases. Clarification of the characteristics of deterministic and ensemble mean forecasts from the perspective of attractors of dynamical systems has also rarely been involved. In this paper, two attractor statistics—namely, the global and local attractor radii(GAR and LAR, respectively)—are applied to reveal the relationship between deterministic and ensemble mean forecast errors. The practical forecast experiments are implemented in a perfect model scenario with the Lorenz96 model as the numerical results for verification. The sample mean errors of deterministic and ensemble mean forecasts can be expressed by GAR and LAR, respectively, and their ratio is found to approach2^(1/2) with lead time. Meanwhile, the LAR can provide the expected ratio of the ensemble mean and deterministic forecast errors in individual cases.

Improving Multimodel Weather Forecast of Monsoon Rain Over China Using FSU Superensemble

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are： ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.

An Effective Configuration of Ensemble Size and Horizontal Resolution for the NCEP GEFS

Two important questions are addressed in this paper using the Global Ensemble Forecast System （GEFS） from the National Centers for Environmental Prediction （NCEP）： （1） How many ensemble members are needed to better represent forecast uncertainties with limited computational resources？ （2） What is tile relative impact on forecast skill of increasing model resolution and ensemble size？ Two-month experiments at T126L28 resolution were used to test the impact of varying the ensemble size from 5 to 80 members at the 500- hPa geopotential height. Results indicate that increasing the ensemble size leads to significant improvements in the performance for all forecast ranges when measured by probabilistic metrics, but these improvements are not significant beyond 20 members for long forecast ranges when measured by deterministic metrics. An ensemble of 20 to 30 members is the most effective configuration of ensemble sizes by quantifying the tradeoff between ensemble performance and the cost of computational resources. Two representative configurations of the GEFS the T126L28 model with 70 members and the T190L28 model with 20 members, which have equivalent computing costs--were compared. Results confirm that, for the NCEP GEFS, increasing the model resolution is more （less） beneficial than increasing the ensemble size for a short （long） forecast range.

STUDY OF THE MODIFICATION OF MULTI-MODEL ENSEMBLE SCHEMES FOR TROPICAL CYCLONE FORECASTS

This study investigates multi-model ensemble forecasts of track and intensity of tropical cyclones over the western Pacific, based on forecast outputs from the China Meteorological Administration, European Centre for Medium-Range Weather Forecasts, Japan Meteorological Agency and National Centers for Environmental Prediction in the THORPEX Interactive Grand Global Ensemble(TIGGE) datasets. The multi-model ensemble schemes, namely the bias-removed ensemble mean(BREM) and superensemble(SUP), are compared with the ensemble mean(EMN) and single-model forecasts. Moreover, a new model bias estimation scheme is investigated and applied to the BREM and SUP schemes. The results showed that, compared with single-model forecasts and EMN, the multi-model ensembles of the BREM and SUP schemes can have smaller errors in most cases. However, there were also circumstances where BREM was less skillful than EMN, indicating that using a time-averaged error as model bias is not optimal. A new model bias estimation scheme of the biweight mean is introduced. Through minimizing the negative influence of singular errors, this scheme can obtain a more accurate model bias estimation and improve the BREM forecast skill. The application of the biweight mean in the bias calculation of SUP also resulted in improved skill. The results indicate that the modification of multi-model ensemble schemes through this bias estimation method is feasible.

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.

基于K-means聚类与集成学习算法的小流域山洪灾害易发性评估

为了更好地分析空间异质性对山洪灾害易发性评估的影响,建立了基于K-means聚类与集成学习算法的小流域山洪灾害易发性评估模型。首先,选取中国江西省12338个小流域为研究区,对各时段不同频率降雨量指标进行K-means聚类。其次,以误差平方和与平均轮廓系数为聚类效果评价指标,将小流域分为2个类内聚集、类外分散的子集。最后,针对不同子集,从几何特征、环境特征以及降水特征3个方面选取平均坡度、形心高程、形状系数、最长汇流路径比降、地形湿度指数、归一化植被指数、距离河流最近距离、降雨量、洪峰模数以及汇流时间10个山洪影响因素,应用自适应增强算法与极致梯度提升算法进行山洪灾害易发性评估。研究发现,降水是导致山洪灾害的重要因素,江西省高降水区域山洪灾害易发程度普遍高于低降水区,同时省内高风险区分布较为分散,主要分布在东北区域与西北边缘区域。对聚类后两类相似小流域分别进行山洪易发性评估,接受者操作特征曲线下面积值均在0.90以上,精度较聚类前有所提高。聚类策略作为易发性评估模型的前驱过程,可以有效解决小流域异质性问题。

Regionalization of River Basins Using Cluster Ensemble

In the wake of global water scarcity, forecasting of water quantity and quality, regionalization of river basins has attracted serious attention of the hydrology researchers. It has become an important area of research to enhance the quality of prediction of yield in river basins. In this paper, we analyzed the data of Godavari basin, and regionalize it using a cluster ensemble method. Cluster Ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous basins. The goal is to identify, analyse and describe hydrologically similar catchments using cluster analysis. Clustering has been done using RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. Clustering results of RCDA algorithm have been compared with Best of k-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the Godavari basin data.

Impacts of Stochastic Forcing on Ensemble Prediction Effect

Based on the dynamic framework of Lorenz 96 model,the ensemble prediction system(EPS)containing stochastic forcing has been developed.In this system,effects of stochastic forcing on the model climate state and ensemble mean prediction have been studied.The results show that the climate mean and standard deviation provided by a new computing paradigm by means of introduction of the proper stochastic forcing into numerical model integration process are closer to that of the true value than that made by the non-stochastic forcing.In other words,numerical model integration process with stochastic forcing has positive effect on the model climate state,and the effect is found to be positive mainly in the long lead time.Meanwhile,with respect to ensemble forecast effect yielded by white noise stochastic forcing,most results are better than those provided by no-stochastic forcing,and improvements pertaining to white noise stochastic forcing vary non-monotonically with the increase of the size of white noise.Moreover,the effects made by the identical white noise stochastic forcing also are different in various non-linear systems.With respect to EPS effect yielded by red noise stochastic forcing,most results are better than those provided by no-stochastic forcing,but only a part of ensemble forecast effect influenced by red noise is superior to that influenced by white noise.Finally,improvements pertaining to red noise stochastic forcing vary non-symmetrically and non-monotonically with the distribution of coefficientΦ.Besides,the selection of correlation coefficientΦis also dependent on non-linear models.

Research on Precipitation Prediction Model Based on Extreme Learning Machine Ensemble

Precipitation is a significant index to measure the degree of drought and flood in a region,which directly reflects the local natural changes and ecological environment.It is very important to grasp the change characteristics and law of precipitation accurately for effectively reducing disaster loss and maintaining the stable development of a social economy.In order to accurately predict precipitation,a new precipitation prediction model based on extreme learning machine ensemble(ELME)is proposed.The integrated model is based on the extreme learning machine(ELM)with different kernel functions and supporting parameters,and the submodel with the minimum root mean square error(RMSE)is found to fit the test data.Due to the complex mechanism and factors affecting precipitation change,the data have strong uncertainty and significant nonlinear variation characteristics.The mean generating function(MGF)is used to generate the continuation factor matrix,and the principal component analysis technique is employed to reduce the dimension of the continuation matrix,and the effective data features are extracted.Finally,the ELME prediction model is established by using the precipitation data of Liuzhou city from 1951 to 2021 in June,July and August,and a comparative experiment is carried out by using ELM,long-term and short-term memory neural network(LSTM)and back propagation neural network based on genetic algorithm(GA-BP).The experimental results show that the prediction accuracy of the proposed method is significantly higher than that of other models,and it has high stability and reliability,which provides a reliable method for precipitation prediction.

Regionalization of Rainfall Using RCDA Cluster Ensemble Algorithm in India

The magnitude and frequency of precipitation is of great significance in the field of hydrologic and hydraulic design and has wide applications in varied areas. However, the availability of precipitation data is limited to a few areas, where the rain gauges are successfully and efficiently installed. The magnitude and frequency of precipitation in ungauged sites can be assessed by grouping areas with similar characteristics. The procedure of grouping of areas having similar behaviour is termed as Regionalization. In this paper, RCDA cluster ensemble algorithm is employed to identify the homogeneous regions of rainfall in India. Cluster ensemble methods are commonly used to enhance the quality of clustering by combining multiple clustering schemes to produce a more robust scheme delivering similar homogeneous regions. The goal is to identify, analyse and describe hydrologically similar regions using RCDA cluster ensemble algorithm. RCDA cluster ensemble algorithm, which is based on discriminant analysis. The algorithm takes H base clustering schemes each with K clusters, obtained by any clustering method, as input and constructs discriminant function for each one of them. Subsequently, all the data tuples are predicted using H discriminant functions for cluster membership. Tuples with consistent predictions are assigned to the clusters, while tuples with inconsistent predictions are analyzed further and either assigned to clusters or declared as noise. RCDA algorithm has been compared with Best of K-means and Clue cluster ensemble of R software using traditional clustering quality measures. Further, domain knowledge based comparison has also been performed. All the results are encouraging and indicate better regionalization of the rainfall in different parts of India.

A Clustering Ensemble approach based on the similar ities in 2- mode social networ ks

For a particular clustering problems, selecting the best clustering method is a challenging problem.Research suggests that integrate the multiple clustering can improve the accuracy of clustering ensemble greatly. A new clustering ensemble approach based on the similarities in 2-mode networks is proposed in this paper. First of all, the data object and the initial clustering clusters transform into 2-mode networks, then using the similarities in 2-mode networks to calculate the similarity between different clusters iteratively to refine the adjacency matrix, K-means algorithm is finally used to get the final clustering, then obtain the final clustering results.The method effectively use the similarity between different clusters,example shows the feasibility of this method.

基于随机取样的选择性K-means聚类融合算法

由于缺少数据分布、参数和数据类别标记的先验信息,部分基聚类的正确性无法保证,进而影响聚类融合的性能;而且不同基聚类决策对于聚类融合的贡献程度不同,同等对待基聚类决策,将影响聚类融合结果的提升。为解决此问题,提出了基于随机取样的选择性K-means聚类融合算法(RS-KMCE)。该算法中的随机取样策略可以避免基聚类决策选取陷入局部极小,而且依据多样性和正确性定义的综合评价值,有利于算法快速收敛到较优的基聚类子集,提升融合性能。通过2个仿真数据库和4个UCI数据库的实验结果显示:RS-KMCE的聚类性能优于K-means算法、K-means融合算法(KMCE)以及基于Bagging的选择性K-means聚类融合(BA-KMCE)。

结合X-means聚类的自适应随机子空间组合分类算法

针对大规模数据的分类准确率低且效率下降的问题,提出一种结合X-means聚类的自适应随机子空间组合分类算法。首先使用X-means聚类方法,保持原有数据结构的同时,把复杂的数据空间自动分解为多个样本子空间进行分治学习;而自适应随机子空间组合分类器,提升了基分类器的差异性并自动确定基分类器数量,提升了组合分类器的鲁棒性及分类准确性。该算法在人工和UCI数据集上进行了测试,并与传统单分类和组合分类算法进行了比较。实验结果表明,对于大规模数据集,该方法具有更好的分类精度和健壮性,并提升了整体算法的效率。

基于k-means和邻域粗糙集的航空客户价值分类研究

对客户价值进行分类,识别重要价值客户,对航空公司获利至关重要。本文提出了基于k-means和邻域粗糙集的航空客户价值分类模型。首先,从客户的当前价值和潜在价值双视角出发,建立了航空客户综合价值评价指标体系;之后,采用基于Elbow的k-means方法对航空客户进行聚类,采用邻域粗糙集方法对决策系统进行指标约简,根据约简后的决策系统完成客户价值初筛。评估前先使用SMOTE方法消除数据的不平衡性,而后采用网格搜索组合分类器的方法对航空客户价值分类的效果进行评估和检验。最后,根据评估结果对航空客户价值细分。文末,对国内某航空公司的62988条真实客户记录进行了实证分析和验证,其中,潜在VIP客户群的分类准确率达到了92%,从而为航空客户价值分类提供了一种新思路。

基于MapReduce的K-means聚类集成

针对传统聚类算法难以高效进行海量数据聚类分析的问题,提出一种基于MapReduce框架的K-means聚类集成算法。利用K-means算法生成不同聚簇数目的基聚类结果,改进共协关系矩阵,依据数据点对出现次数进行集成,自动得出最终聚类结果。实验结果表明,该算法能够有效地改善聚类质量,具有良好的扩展性,适用于海量数据的聚类分析。

基于集成多示例学习的Mean Shift跟踪算法

为了实现长时间稳定的对特定目标的跟踪,结合匹配型跟踪方法和决策型跟踪方法的优势,同时利用集成学习的思想构建多个强分类器,提出一种基于集成多示例学习的mean shift跟踪算法.首先在上一帧中对示例进行随机采样,构建分类器的集体,通过集成学习合成最终的分类器以确定当前帧中目标的初始位置;然后对初始位置和上一帧目标最终位置的距离与设定的阈值进行判断,决定是否采用mean shift跟踪算法对初始位置进行修订,以确定目标的最终位置.实验结果表明,该算法不但可以应对目标的形变、旋转、遮挡以及光照变化等各种复杂的情况,而且可以做到长时间的跟踪,具有较强的鲁棒性.

基于Mean Decrease Impurity改进XGBoost算法

针对XGBoost算法在处理高维数据集分类准确率和效率下降的情况,文中提出一种基于mean decrease impurity算法改进的XGBoost算法;并设计频数算法解决mean decrease impurity算法对特征重要度排名的随机性。实验结果表明,本方法预测效率以及准确率要优于未改进XGBoost算法,同时mean decrease impurity算法也优于基于方差算法。因此,文中所提出的分类方法具有更高的准确率和效率。

基于K-Means聚类和集成学习的HTD仿真

为了实时检测信息系统中是否存在硬件木马,提出基于K-Means聚类和集成学习的硬件木马检测方法。采用基于信息熵改进的K-means动态聚类算法去除冗余数据,挖掘信息系统硬件运行的有效数据;在该数据中采用基于改进旋转森林的集成学习方法建立识别硬件木马的文本分类器,引入动态加权投票集成方法,检测出硬件木马。仿真结果显示,所提方法硬件木马数据检测率高达99%,误报率最大值仅为3%;可以实时检测出硬件木马,不存在时延。和同类检测方法相比,所提方法对硬件木马的检测精度、检测实时性存在优越性。