Application of the Probability Matching Correction Method in Precipitation Forecast

Based on the observation data of 24-hour cumulative precipitation from 92 ground meteorological observation stations in Jiangxi province from March to July during 2015-2016 and the high-resolution numerical forecast data of precipitation predicted within 24-72 h by the European Centre for Medium-Range Weather Forecasts（ ECMWF）,the Gamma function was used as the fitting function of probability distribution of cumulative precipitation to match the probability of predicted and observed precipitation. Moreover,the change of forecast score before and after the correction was tested. The results showed that the predicted values of heavy precipitation based on ECMWF model were smaller than the observed values,while the predicted values of light precipitation were larger than the observed values. The probability matching correction method could be used to effectively correct systematic errors of model forecast,and the correction effect of all grades of precipitation（ especially for rainstorm） was good.The shorter the period of validity was,the better the correction effect was. The correction method has a good application effect in the interpretation of model precipitation products,and can provide better security services for agricultural production.

Bayesian analysis for the Lomax model using noninformative priors

The Lomax distribution is an important member in the distribution family.In this paper,we systematically develop an objective Bayesian analysis of data from a Lomax distribution.Noninformative priors,including probability matching priors,the maximal data information(MDI)prior,Jeffreys prior and reference priors,are derived.The propriety of the posterior under each prior is subsequently validated.It is revealed that the MDI prior and one of the reference priors yield improper posteriors,and the other reference prior is a second-order probability matching prior.A simulation study is conducted to assess the frequentist performance of the proposed Bayesian approach.Finally,this approach along with the bootstrap method is applied to a real data set.

Proactive eviction of flow entry for SDN based on hidden Markov model

With the fast development of software defined network(SDN),numerous researches have been conducted for maximizing the performance of SDN.Currently,flow tables are utilized in OpenFlows witch for routing.Due to the space limitation of flow table and switch capacity,various issues exist in dealing with the flows.The existing schemes typically employ reactive approach such that the selection of evicted entries occurs when timeout or table miss occurs.In this paper a proactive approach is proposed based on the prediction of the probability of matching of the entries.Here eviction occurs proactively when the utilization of flow table exceeds a threshold,and the flow entry of the lowest matching probability is evicted.The matching probability is estimated using hidden Markov model(HMM).Computersimulation reveals that it significantly enhances the prediction accuracy and decreases the number of table misses compared to the standard Hard timeout scheme and Flow master scheme.