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.

A knowledge push technology based on applicable probability matching and multidimensional context driving

Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push＇, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

ON USE OF LHN METHOD TO ASSIMILATE THE INTENSIFIED SURFACE PRECIPITATIONS FOR GRAPES_MESO MODEL INITIALIZATION

The quantitative precipitation forecast(QPF) in very-short range(0-12 hours) has been investigated in this paper by using a convective-scale(3km) GRAPES_Meso model. At first, a latent heat nudging(LHN) scheme to assimilate the hourly intensified surface precipitation data was set up to enhance the initialization of GRAPES_Meso integration. And then based on the LHN scheme, a convective-scale prediction system was built up in considering the initial "triggering"uncertainties by means of multi-scale initial analysis(MSIA), such as the three-dimensional variational data assimilation(3DVAR), the traditional LHN method(VAR0LHN3), the cycling LHN method(CYCLING), the spatial filtering(SS) and the temporal filtering(DFI) LHN methods. Furthermore, the probability matching(PM) method was used to generate the QPF in very-short range by combining the precipitation forecasts of the five runs. The experiments for one month were carried out to validate the MSIA and PM method for QPF in very-short range.The numerical simulation results showed that:(1) in comparison with the control run, the CYCLING run could generate the smaller-scale initial moist increments and was better for reducing the spin-up time and triggering the convection in a very-short time;(2) the DFI runs could generate the initial analysis fields with relatively larger-scale initial increments and trigger the weaker convections at the beginning time(0-3h) of integration, but enhance them at latter time(6-12h);(3) by combining the five runs with different convection triggering features, the PM method could significantly improve the QPF in very-short range in comparison to any single run. Therefore, the QPF with a small size of combining members proposed here is quite prospective in operation for its lower computation cost and better performance.

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.