Guangxi Climate Center

Nanning, China

Guangxi Climate Center

Nanning, China
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Xiao Z.,CAS Institute of Atmospheric Physics | Zhou X.,Guangxi Climate Center | Yang P.,China Meteorological Administration Training Center | Liu H.,China Meteorological Administration Training Center
Frontiers of Earth Science | Year: 2015

This study analyzed the changes in precipitation over summer and autumn across the Yunnan region of China, and undertook a composite analysis of the atmospheric circulations in the troposphere, which included an analysis of the interannual and interdecadal variations. This paper examines in detail the circulation backgrounds of the wet and dry periods in summer and autumn and their correlations with the sea surface temperature. The results indicated that the summer and autumn precipitation across Yunnan has significantly decreased over the past 50 years. Furthermore, since the beginning of the century, the summer and autumn precipitation cycle has been in a low precipitation phase. The overlap of two extremely low rain phases has caused frequent droughts in the region. In addition, the atmospheric circulation fields during these wet and dry periods are very different. These are mainly shown as a meridional wind anomaly in eastern China in the low atmosphere, as a cross-equatorial airflow anomaly, a tropical zonal wind anomaly over the Indian Ocean, and as a related South Asia High and Western Pacific Subtropical High. Further analysis suggested that the SST over the Indian Ocean and the Pacific warm pool critically affect the anomalous summer and autumn precipitation over Yunnan by impacting the monsoon circulations. Future projections for greenhouse gas warming suggest a potential anomalous circulation background between 2010 and 2020 which may result in less precipitation during the wet season or even drought events across the Yunnan region. © 2015 Higher Education Press and Springer-Verlag Berlin Heidelberg

Huang X.-Y.,Guangxi Meteorological Observatory | Jin L.,Guangxi Climate Center | Shi X.-M.,Guangxi Research Institute of Meteorological Disasters Mitigation
Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 | Year: 2011

Using the capability of extraction the main signal feature from meteorological fields with random noise, and eliminate random disturbance by principal component analysis with conducted on empirical orthogonal functions(EOF), and following the thinking clue of the ensemble prediction in numerical weather prediction (NWP), a novel nonlinear artificial intelligence ensemble prediction (NAIEP) model has been developed based on the multiple neural networks with identical expected output created by using the genetic algorithm (GA) of evolutionary computation. Basing on the sample of typhoon in July from 1980 to 2009 for 30 years in the South China Sea, setting up the genetic neural network (GNN) ensemble prediction (GNNEP) model which selecting the predictors by the method of Stepwise regression and EOF both in the predictors of climatology persistance and Numerical forecasting(NWP) products to predict the typhoon track. The mean error for 24 hours of this new model is 125.7km, and the results of prediction experiments showed that the NAIEP model is obviously more skillful than the climatology and persistence (CLIPER) model with the circumstance of identical predictors and sample cases. © 2011 IEEE.

Zhao H.-S.,Guangxi Climate Center | Jin L.,Guangxi Climate Center | Huang X.-Y.,Guangxi Meteorological Observatory
3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice | Year: 2010

A nonlinear prediction model has been presented of PSO-ANN of monthly precipitation in rain season. It differs from traditional prediction modeling in the following aspects: (1) input factors of the PSO-ANN model of monthly precipitation were selected from a large quantity of preceding period high correlation factors, and they were also highly information-condensed by using the empirical orthogonal function (EOF) method; which effectively condensed the useful information of predictors. (2) Different from the traditional neural network modeling, the PSO-ANN modeling is able to objectively determine the network structure of the PSO-ANN model, and the model constructed has a better generalization capability. The model changes the prediction of climate field to that of the principal component of that field. According to the approximate invariability of eigenvectors of climate field, the prediction of climate field is obtained by return computation, together with the principal component. A test example is predicting the flood period rainfall for the 37 basic stations in Guangxi. The prediction of field for June to September in 2009 is made and comparisons with the field of observations. The results show that the predictive efficacy is remarkable. © 2010 IEEE.

Wu J.,Liuzhou Teacher College | Liu M.,Massey University | Jin L.,Guangxi Climate Center
International Journal of Computational Intelligence and Applications | Year: 2010

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application. © 2010 Imperial College Press.

Jin J.,East China Normal University | Jin L.,Guangxi Climate Center
Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 | Year: 2011

A mixture of experts with a two-stage adaptive FCMef (2-AFCMef) gate is presented to overcome problems of equal-sized divide and conquer and its efficiency. In 2- AFCMef algorithm, a descending ordered series of indexes of effectiveness factor is applied in turn for trying, so that the maximal index can be achieved while any cluster does not disappear. The result of traditional fuzzy c-means clustering algorithm, instead of blindly and randomly searching for many times, is pre-treated as the initial condition of FCMef algorithm. The new method is applied to the field typhoon tracks prediction in South China Sea. Results show great improvement by more than 10% compared to that of the traditional method under the same conditions. © 2011 IEEE.

Wu J.,Liuzhou Teachers College | Liu M.,Massey University | Jin L.,Guangxi Climate Center
Lecture Notes in Electrical Engineering | Year: 2010

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. In this paper, a novel nonlinear regression ensemble model is proposed for rainfall forecasting. The model employs Least Square Support Vector Machine (LS-SVM) based on linear regression and nonlinear regression. Firstly, Projection Pursuit (PP) technology and Particle Swarm Optimization (PSO) algorithm are used to obtain the main factors of the rainfall, which optimize projection index from high dimensionality to a lower dimensional subspace. Secondly, using different linear regressions extract linear characteristics of the rainfall system, and using different Neural Network (NN) algorithms and different network architectures extract nonlinear characteristics of the rainfall system. Finally, LS-SVM regression is used for nonlinear ensemble model. This technique is implemented to forecast daily rainfall in Guangxi, China. Empirical results show that the prediction by using the LS-SVM ensemble model is generally better than those obtained using other models presented in this study in terms of the same evaluation measurements. The results suggest that our nonlinear ensemble model can be extended to meteorological applications in achieving greater forecasting accuracy and improving prediction quality. © 2010 Springer-Verlag Berlin Heidelberg.

Jin J.,East China Normal University | Li M.,East China Normal University | Jin L.,Guangxi Climate Center
Mathematical Problems in Engineering | Year: 2015

When pure linear neural network (PLNN) is used to predict tropical cyclone tracks (TCTs) in South China Sea, whether the data is normalized or not greatly affects the training process. In this paper, min.-max. method and normal distribution method, instead of standard normal distribution, are applied to TCT data before modeling. We propose the experimental schemes in which, with min.-max. method, the min.-max. value pair of each variable is mapped to (-1, 1) and (0, 1); with normal distribution method, each variable's mean and standard deviation pair is set to (0, 1) and (100, 1). We present the following results: (1) data scaled to the similar intervals have similar effects, no matter the use of min.-max. or normal distribution method; (2) mapping data to around 0 gains much faster training speed than mapping them to the intervals far away from 0 or using unnormalized raw data, although all of them can approach the same lower level after certain steps from their training error curves. This could be useful to decide data normalization method when PLNN is used individually. © 2015 Jian Jin et al.

Zhao H.-S.,Guangxi Climate Center | Jin L.,Guangxi Climate Center | Huang Y.,Guangxi Climate Center | Jin J.,East China Normal University
Natural Hazards | Year: 2014

A nonlinear ensemble prediction model for typhoon rainstorm has been developed based on particle swarm optimization-neural network (PSO-NN). In this model, PSO algorithm is employed for optimizing the network structure and initial weight of the NN with creating multiple ensemble members. The model input of the ensemble member is the high correlated grid point factors selected from the rainfall forecast field of Japan Meteorological Agency numerical prediction products using the stepwise regression method, and the model output is the future 24 h rainfall forecast of the 89 stations. Results show that the objective prediction model is more accurate than the numerical prediction model which is directly interpolated into the stations, so it can better been implemented for the interpretation and application of numerical prediction products, indicating a potentially better operational weather prediction. © 2014 Springer Science+Business Media Dordrecht.

Huang Y.,Guangxi Research Institute of Meteorological Disasters Mitigation | Jin L.,Guangxi Climate Center
Meteorology and Atmospheric Physics | Year: 2013

A western North Pacific tropical cyclone (TC) intensity prediction scheme has been developed based on climatology and persistence (CLIPER) factors as potential predictors and using genetic neural network (GNN) model. TC samples during June-October spanning 2001-2010 are used for model development. The GNN model input is constructed from potential predictors by employing both a stepwise regression method and an Isometric Mapping (Isomap) algorithm. The Isomap algorithm is capable of finding meaningful low-dimensional architectures hidden in their nonlinear high-dimensional data space and separating the underlying factors. In this scheme, the new developed model, which is termed the GNN-Isomap model, is used for monthly TC intensity prediction at 24- and 48-h lead times. Using identical modeling samples and independent samples, predictions of the GNN-Isomap model are compared with the widely used CLIPER method. By adopting different numbers of nearest neighbors, results of sensitivity experiments show that the mean absolute prediction errors of the independent samples using GNN-Isomap model at 24- and 48-h forecasts are smaller than those using CLIPER method. Positive skills are obtained as compared to the CLIPER method with being above 12 % at 24 h and above 14 % at 48 h. Analyses of the new scheme suggest that the useful linear and nonlinear prediction information of the full pool of potential predictors is excavated in terms of the stepwise regression method and the Isomap algorithm. Moreover, the GNN is built by integrating multiple individual neural networks with the same expected output and network architecture is optimized by an evolutionary genetic algorithm, so the generalization capacity of the GNN-Isomap model is significantly enhanced, indicating a potentially better operational weather prediction. © 2013 Springer-Verlag Wien.

Jin L.,Guangxi Climate Center | Huang Y.,Guangxi Climate Center | Zhao H.-S.,Guangxi Climate Center
Proceedings of the 2012 IEEE 13th International Conference on Information Reuse and Integration, IRI 2012 | Year: 2012

A nonlinear statistical ensemble prediction modeling method has been developed for predicting monthly mean rainfall using Particle Swarm Optimization (PSO) algorithm and neural network (NN) technique. Comparison results of prediction experiments show that the PSO-NN ensemble prediction (PNNEP) model is superior to the traditional linear statistical forecast method in prediction capability. Computation and analysis of the PNNEP also demonstrate that the prediction of the ensemble model integrates predictions of dozens of ensemble members and the network structure of each member is objectively determined by means of PSO algorithm, so the generalization capacity of the ensemble prediction model is also enhanced, suggesting that the PNNEP model opens up a vast range of possibilities for operational weather prediction. © 2012 IEEE.

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