The impact of the subtropical high (STH) on precipitation was investigated on a daily timescale using matched NCEP and the Global Precipitation Climatology Project (GPCP) datasets.Comparison of the conditional probabi...The impact of the subtropical high (STH) on precipitation was investigated on a daily timescale using matched NCEP and the Global Precipitation Climatology Project (GPCP) datasets.Comparison of the conditional probability (intensity) of precipitation under STH condi-tions with that under non-STH conditions suggests that the presence of the STH conditions has a limited impact on local precipitation.In the West Pacific Subtropical High (WPSH) and the North Atlantic Subtropical High (NASH),precipitation was only 30% lower under STH conditions than under non-STH conditions.The STH conditions had somewhat more impact on precipitation intensity,but it was still 50% less than the intensity under non-STH conditions (mean of roughly 5 mm d 1).Pre-cipitation under STH conditions was found to be highly correlated with vertical motion.Active updrafts occurring even under STH conditions are essential for frequent oc-currences and moderate intensities of precipitation.展开更多
Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledg...Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.展开更多
基金supported by Special Funds for Public Welfare of China (Grant No.GYHY-QX-2007)the National Natural Science Foundation of China (Grant Nos.40730950,40675027,and 40805007)
文摘The impact of the subtropical high (STH) on precipitation was investigated on a daily timescale using matched NCEP and the Global Precipitation Climatology Project (GPCP) datasets.Comparison of the conditional probability (intensity) of precipitation under STH condi-tions with that under non-STH conditions suggests that the presence of the STH conditions has a limited impact on local precipitation.In the West Pacific Subtropical High (WPSH) and the North Atlantic Subtropical High (NASH),precipitation was only 30% lower under STH conditions than under non-STH conditions.The STH conditions had somewhat more impact on precipitation intensity,but it was still 50% less than the intensity under non-STH conditions (mean of roughly 5 mm d 1).Pre-cipitation under STH conditions was found to be highly correlated with vertical motion.Active updrafts occurring even under STH conditions are essential for frequent oc-currences and moderate intensities of precipitation.
基金Supported by the NSFC (No. 60772006, 60874105)the ZJNSF (Y1080422, R106745)Aviation Science Foundation (20070511001)
文摘Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method.
