Evaluation of Cold Dew Wind Monitoring Precision for Late Rice Based on CLDAS Data in Guangxi

The low temperature process of cold dew wind( from September 19 to 27 in 2011) for late rice production was dynamically monitored by using CLDAS temperature,combined with the background information of rice cultivation from multi-source satellite database together with an reference to the monitoring indexes of cold dew wind disaster to verify the precision of CLDAS data,so as to provide a reference for monitoring chilling damage caused by cold dew wind in late rice production in Guangxi. The results showed that the cold wind dew caused heavy damage to an area of 3 159. 76 km^2,moderate damage to an area of 559. 77 km^2 and light damage to an area of 2 452. 14 km^2. The correlation coefficients between CLDAS inversion temperature and actual temperature of 12 verification meteorological stations were all larger than 0. 93,and the difference in daily average temperature was 0. 3 ℃. The time difference between maximum and minimum temperature provided by CLDAS and corresponding actual temperature from 12 meteorological stations was less than 1 h. The temperature data provided by CLDAS was in accordance with actual temperature data. With an advantage of rapidly,minutely and accurately monitoring the grade distribution of local cold dew wind disaster for late rice,CLDAS can be used in monitoring cold dew wind in late rice production in Guangxi.

STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency for the network to transform to an issue of local solution, a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP, that is, the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights, trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

DOWNSCALING FORECAST OF MONTHLY PRECIPITATION OVER GUANGXI BASED ON BP NEURAL NETWORK MODEL

Variables fields such as enstrophy, meridional-wind and zonal-wind variables are derived from monthly 500 hPa geopotential height anomalous fields. In this work, we select original predictors from monthly 500-hPa geopotential height anomalous fields and their variables in June of 1958 - 2001, and determine comprehensive predictors by conducting empirical orthogonal function (EOF) respectively with the original predictors. A downscaling forecast model based on the back propagation (BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitation in June over Guangxi with the monthly dynamic extended range forecast products. For comparison, we also build another BP neural network model with the same predictands by using the former comprehensive predictors selected from 500-hPa geopotential height anomalous fields in May to December of 1957 - 2000 and January to April of 1958 - 2001. The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors, but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended range forecast.

Study on the Overfitting of the Artificial Neural Network Forecasting Model

Because of overfitting and the improvement of generalization capability (GC)available in the construction of forecasting models using artificial neural network (ANN), a newmethod is proposed for model establishment by means of making a low-dimension ANN learning matrixthrough principal component analysis (PCA). The results show that the PC A is able to construct anANN model without the need of finding an optimal structure with the appropriate number ofhidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducingdimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwiseregression techniques for model establishment.