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Li X.,Guilin Meteorological Bureau | Yao L.,Guilin Meteorological Bureau | Lin Z.,Guangxi Meteorological Observatory
Applied Mechanics and Materials | Year: 2012

From 31 May to 1 June 2010, serious super high precipitation multi-cells caused convectional rainstorm in central region of Guangxi. The heaviest hour rainfall was 92mm, the heaviest rainfall for 48 hours was 503.9mm. The storms developed in the southwest warm moist wind and moved rightly. The radar reflectivity displayed several bow echoes moving from west to east along the 850hPa shear line. A line echo wave pattern developed. At 2.4° elevation angle, the maximum radar reflectivity of supercell reached 69dBz. The cross section of reflectivity showed that the 40dBz reflectivity core reached 9km in height, indicating a deep updraft. The radial velocity imagery showed with the mesocyclone features such as inverse wind area, the positive and negative velocity pairs. The VAD profile showed the deep warm-wet layer before the storm occurred. The 12m/s southwest wind reached 8km in depth. About 2 hours before the rainstorm, the VAD profile showed double dry region. When the double dry region appeared half hour later, the first rainstorm occurred. The numerical simulation analysis results indicated that the high precipitation multi-cell storms was produced and moved along the vortex path. Source

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. Source

Liang W.,Guangxi Meteorological Observatory | Jiang R.,Menshan County Weather Station
Hydraulic Engineering III - Proceedings of the 3rd Technical Conference on Hydraulic Engineering, CHE 2014 | Year: 2015

By using conventional data and numerical models, radar, satellite data, the continuous heavy rain were analyzed in Guilin which occurred during 8–10 June 2013. The results showed that: a significant upper rise force of the trough was the cause of heavy rain. The low-level cyclonic curvature was the maintaining mechanism of the continuous rainstorm. Radar wind profilers showed that: the enhancement of the southwest low level jet formed the bow echo along the valley between Hunan to Guangxi, and the corresponding convergence line led to the convective precipitation. Convection over the Bay of Bengal developed 3 days ahead of the convection in Guangxi, indicated that the wave motion broadcast eastward from the Bay of Bengal to Guangxi and produced the heavy rainstorm. © 2015 Taylor & Francis Group, London, UK. Source

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. Source

Gao A.,Guangxi Meteorological Observatory | Zhang R.,Guangxi Weather Modification Office | Chen J.,Guangxi Meteorological Observatory
Hydraulic Engineering II - Proceedings of the 2nd SREE Conference on Hydraulic Engineering, CHE 2013 | Year: 2014

The 0809 Severe Tropical Storm (STS) KAMMURI and the 0907 Tropical Storm (TS) GONI, moved suddenly southwestward and reinforced, into the Beibu Gulf, via Leizhou Peninsula, instead of moving northwestward as predicted into the southeast part of Guangxi. These phenomena resulted in clear deviation in forecast and passive situation in forecast service. By using the data of ECMWF, MICAPS and satellite images, the causes for the left-deflection tracks and booming of KAMMURI and GONI after landfall in coastal area of west Guangdong were analyzed, which showed that: the cause that made KAMMURI and GONI left deflection lied in: 1) Fujiwhara effect; 2) Asymmetrical structure; 3) The convective clouds developed over the Beibu Gulf and the pressure dropped rapidly, resulting in attraction; 4) The northeast component was bigger than the southwest component in the wind circumfluence. The cause that made KAMMURI and GONI boomed over the Beibu Gulf was primarily due to the behavior of ITCZ in the coast of the southern China, and the active period of the southwest monsoon. © 2014 Taylor & Francis Group, London. Source

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