Machine Learning of Weather Forecasting Rules from Large Meteorological Data Bases

Discovery of useful forecasting rules from observational weather data is an outstanding interesting topic.The traditional methods of acquiring forecasting knowledge are manual analysis and investigation performed by human scientists.This paper presents the experimental results of an automatic machine learning system which derives forecasting rules from real observational data.We tested the system on the two large real data sets from the areas of centra! China and Victoria of Australia.The experimental results show that the forecasting rules discovered by the system are very competitive to human experts.The forecasting accuracy rates are 86.4% and 78% of the two data sets respectively

Machining Error Control by Integrating Multivariate Statistical Process Control and Stream of Variations Methodology

For aircraft manufacturing industries, the analyses and prediction of part machining error during machining process are very important to control and improve part machining quality. In order to effectively control machining error, the method of integrating multivariate statistical process control （MSPC） and stream of variations （SoV） is proposed. Firstly, machining error is modeled by multi-operation approaches for part machining process. SoV is adopted to establish the mathematic model of the relationship between the error of upstream operations and the error of downstream operations. Here error sources not only include the influence of upstream operations but also include many of other error sources. The standard model and the predicted model about SoV are built respectively by whether the operation is done or not to satisfy different requests during part machining process. Secondly, the method of one-step ahead forecast error （OSFE） is used to eliminate autocorrelativity of the sample data from the SoV model, and the T2 control chart in MSPC is built to realize machining error detection according to the data characteristics of the above error model, which can judge whether the operation is out of control or not. If it is, then feedback is sent to the operations. The error model is modified by adjusting the operation out of control, and continually it is used to monitor operations. Finally, a machining instance containing two operations demonstrates the effectiveness of the machining error control method presented in this paper.

Analysis of stable components for extended-range (10-30 days) weather forecast:A case study of continuous overcast-rainy process in early 2009 over the mid-lower reaches of the Yangtze River

A continuous overcast-rainy weather(CORW) process occurred over the mid-lower reaches of the Yangtze River(MLRYR) in China from February 14 to March 9 in 2009,with a large stretch and long duration that was rarely seen in historical records.Using the empirical orthogonal function(EOF),we analyzed the geopotential height anomaly field of the NCEP-DOE Reanalysis II in the same period,and defined the stable components of extended-range(10-30 days) weather forecast(ERWF).Furthermore,we defined anomalous and climatic stable components based on the variation characteristics of the variance contribution ratio of EOF components.The climatic stable components were able to explain the impact of climatically averaged information on the ERWF,and the anomalous stable components revealed the abnormal characteristics of the continuous overcast-rainy days.Our results show that the stable components,especially the anomalous stable components,can maintain the stability for a longer time(more than 10 days) and manifest as monthly scale low-frequency variation and ultra-long-wave activities.They also behave as ultra-long waves of planetary scale with a stable and vertically coherent structure,reflect the variation of general circulation in mid-high latitudes,display the cycle of the zonal circulation and the movement and adjustment of the ultra-long waves,and are closely linked to the surface CORW process.