EXCEL函数在数据化整中的应用

应用EXCEL函数,可以实现测量数据按规程要求进行化整。数据化整问题的解决,有助于应用EXCEL实现检定/检测/校准原始记录和证书的编制与无纸化作业。

电子式三相电能表示值误差测量结果的不确定评定

阐述了测量不重复性、标准装置误差、误差显示分辨力、误差数据化整间隔对电子式三 相电能表测量误差的影响,根据这四方面的影响,构建数学模型,对电子式三相电能表示值误差测量 结果的不确定度作了详细的评定,同时对选定的三相电能表标准装置的校准能力进行了检测。

Detection of gross errors using mixed integer optimization approach in process industry

A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information cri- terion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

An ensemble adjustment Kalman filter study for Argo data

An ensemble adjustment Kalman filter system is developed to assimilate Argo profiles into the Northwest Pacific MASNUM wave-circulation coupled model, which is based on the Princeton Ocean Model (POM). This model was recoded in FORTRAN-90 style, and some new data types were defined to improve the efficiency of system design and execution. This system is arranged for parallel computing by using UNIX shell scripts: it is easier with single models running separately with the required information exchanged through input/output files. Tests are carried out to check the performance of the system: one for checking the ensemble spread and another for the performance of assimilation of the Argo data in 2005. The first experiment shows that the assimilation system performs well. The comparison with the Satellite derived sea surface temperature (SST) shows that modeled SST errors are reduced after assimilation; at the same time, the spatial correlation between the simulated SST anomalies and the satellite data is improved because of Argo assimilation. Furthermore, the temporal evolution/trend of SST becomes much better than those results without data assimilation. The comparison against GTSPP profiles shows that the improvement is not only in the upper layers of ocean, but also in the deeper layers. All these results suggest that this system is potentially capable of reconstructing oceanic data sets that are of high quality and are temporally and spatially continuous.

CLUSTER-BASED REGULARIZED SLICED INVERSE REGRESSION FOR FORECASTING MACROECONOMIC VARIABLES

This paper concerns the dimension reduction in regression for large data set. The authors introduce a new method based on the sliced inverse regression approach, cMled cluster-based regularized sliced inverse regression. The proposed method not only keeps the merit of considering both response and predictors＇ information, but also enhances the capability of handling highly correlated variables. It is justified under certain linearity conditions. An empirical application on a macroeconomic data set shows that the proposed method has outperformed the dynamic factor model and other shrinkage methods.

Determining whether Qinghai–Tibet Plateau waterbodies have acted like carbon sinks or sources over the past 20 years

Half of all of China’s lakes are on the Qinghai–Tibet Plateau(QTP),which are mainly distributed at altitudes above 4000 m asl.Being under conditions of progressively intensifying anthropogenic activities and climate change,the debate on whether QTP lakes act as carbon(C)sinks or sources remains unresolved.This study explores QTP lake C exchange processes and characteristics over the past two decades through field monitoring and data integration.Results reveal high lake carbon dioxide(CO_(2))exchange flux distribution patterns in its western and southern regions and correspondingly low values in its eastern and northern regions.Lake CO_(2)exchange flux rates also show significant temporal differences where those in the 2000s and 2010s were significantly higher compared to the 2020s.Annual total CO_(2)emission flux from QTP lakes has increased from 1.60 Tg Ca^(-1)in the 2000s to 6.87 Tg Ca^(-1)in the 2010s before decreasing to 1.16 Tg Ca^(-1)in the 2020s.However,QTP lakes have generally acted as C sinks when annual ice-cover periods are included in the estimation of annual C budgets.Consequently,QTP lakes are gradually evolving towards C sinks.Some small-sized freshwater lakes on the QTP exhibit C sequestration characteristics while low-mid altitude saltwater lakes also act as C sinks.Therefore,owing to the high uncertainties in the estimation of C exchange flux,the QTP lake C sink capacity has been largely underestimated.

Ocean satellite data assimilation experiments in FIO-ESM using ensemble adjustment Kalman filter

Using Ensemble Adjustment Kalman Filter(EAKF), two types of ocean satellite datasets were assimilated into the First Institute of Oceanography Earth System Model(FIO-ESM), v1.0. One control experiment without data assimilation and four assimilation experiments were conducted. All the experiments were ensemble runs for 1-year period and each ensemble started from different initial conditions. One assimilation experiment was designed to assimilate sea level anomaly(SLA); another, to assimilate sea surface temperature(SST); and the other two assimilation experiments were designed to assimilate both SLA and SST but in different orders. To examine the effects of data assimilation, all the results were compared with an objective analysis dataset of EN3. Different from the ocean model without coupling, the momentum and heat fluxes were calculated via air-sea coupling in FIO-ESM, which makes the relations among variables closer to the reality. The outputs after the assimilation of satellite data were improved on the whole, especially at depth shallower than 1000 m. The effects due to the assimilation of different kinds of satellite datasets were somewhat different. The improvement due to SST assimilation was greater near the surface, while the improvement due to SLA assimilation was relatively great in the subsurface. The results after the assimilation of both SLA and SST were much better than those only assimilated one kind of dataset, but the difference due to the assimilation order of the two kinds of datasets was not significant.