This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for t...This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.展开更多
According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clu...According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clustering methods we select two more representative regional meteorological data. We establish three-layer model by transforming the interactive structure date into nested structure data. According the model theory we perform the corresponding model calculations, optimization and analysis, accordingly to interpret the level effects, and residual test. The results show that most of the difference in Monthly Rainfall was respectively explained by Variables (Meteorological factors, seasonal effects, geographic effects) in different levels.展开更多
[Objective] The research aimed to study a kind of precipitation predication model which was established by using the multi-level mode circulation output field. [Method] The downscaling prediction method which was run ...[Objective] The research aimed to study a kind of precipitation predication model which was established by using the multi-level mode circulation output field. [Method] The downscaling prediction method which was run in Anhui business was improved. The high related zone with the precipitation was found in the multi-level mode circulation field. Moreover, the optimal subset regression model was used to screen and assemble the forecast factors. The predication equation of monthly rainfall was formed. Finally, the actual and mode circulation fields during 2005-2009 were respectively set into the equation, and the prediction scores of two kinds of data schemes were contrasted. The monthly score was analyzed, and the feasibility of business operation was inspected. [Result] Compared with the traditional downscaling method, the data content of precipitation prediction model which was established by using the multi-level mode circulation output field was richer. Seen from the prediction effect, the average anomaly symbol consistence rate was 63%, and PS was 75 scores. It was not only higher than that of downscaling method of business operation, but also higher than the predicted score of business issue. In addition, the prediction effect of method on the typical flooding month was better. It showed that the method had the good prediction capability on the abnormal value. [Conclusion] The research provided the reference for enriching the downscaling technology scheme.展开更多
文摘This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.
文摘According to the hierarchical characteristics of monthly rainfall in different regions, the paper takes the geographical factors and seasonal factors into the hierarchical linear model as the level effect. Through clustering methods we select two more representative regional meteorological data. We establish three-layer model by transforming the interactive structure date into nested structure data. According the model theory we perform the corresponding model calculations, optimization and analysis, accordingly to interpret the level effects, and residual test. The results show that most of the difference in Monthly Rainfall was respectively explained by Variables (Meteorological factors, seasonal effects, geographic effects) in different levels.
基金Supported by Business Ability Construction Item of Anhui Meteorological Bureau(ybyb2010007)
文摘[Objective] The research aimed to study a kind of precipitation predication model which was established by using the multi-level mode circulation output field. [Method] The downscaling prediction method which was run in Anhui business was improved. The high related zone with the precipitation was found in the multi-level mode circulation field. Moreover, the optimal subset regression model was used to screen and assemble the forecast factors. The predication equation of monthly rainfall was formed. Finally, the actual and mode circulation fields during 2005-2009 were respectively set into the equation, and the prediction scores of two kinds of data schemes were contrasted. The monthly score was analyzed, and the feasibility of business operation was inspected. [Result] Compared with the traditional downscaling method, the data content of precipitation prediction model which was established by using the multi-level mode circulation output field was richer. Seen from the prediction effect, the average anomaly symbol consistence rate was 63%, and PS was 75 scores. It was not only higher than that of downscaling method of business operation, but also higher than the predicted score of business issue. In addition, the prediction effect of method on the typical flooding month was better. It showed that the method had the good prediction capability on the abnormal value. [Conclusion] The research provided the reference for enriching the downscaling technology scheme.
