The charm of the legislative background data lies in its duality of being able to build a bridge between the practical field of legislation and judicature and the theoretical field of academic research. Proper applica...The charm of the legislative background data lies in its duality of being able to build a bridge between the practical field of legislation and judicature and the theoretical field of academic research. Proper application of legislative background data is an important criterion to distinguish between professionals and the public. On the application of legislative background data, from the legislative point of view, legislators need to use the conclusions and reasons of the value judgments formed around the opinions of the proponents as the basis for criticism and argumentation, reaching a minimal consensus through power; from the perspective of interpretivism, the judge needs to focus on the legislator's existing law text and legislative background information to give explanation as the basis for future amendments, reaching maximum consensus through power. On searching the legislative background data, different searching paths and methods could be exploited in the distinction between the text data recorded by the recorder and the audio-visual data recorded by the expositor. The enlightenment obtained from legislative background data should be used for the construction and perfection of the wisdom and intelligence of the court. The judges should be the ideological assistants of the legislators. The search for enlightenment from legislative background data can be conducted from three perspectives, namely cognition, arrangement, and disclosure. Therefore, legislative background data should be standardized by law.展开更多
The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is propose...The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.展开更多
Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper di...Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.展开更多
The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian ...The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean （hereafter AIPOcean） constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving, hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles （mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity-temperature-depth, station data）, remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993-2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.展开更多
基金the stage achievement of"A Study on the Local Legislation System of Municipalities,"Western Project of National Social Science Fund"The Dilemma and Resolution of the Local Legislative Power of the Municipal Government─Centering on the improvement of local legislation quality,"the 2016 annual key project of Sichuan Social Academy of Social Science on"13th Five-year Plan"
文摘The charm of the legislative background data lies in its duality of being able to build a bridge between the practical field of legislation and judicature and the theoretical field of academic research. Proper application of legislative background data is an important criterion to distinguish between professionals and the public. On the application of legislative background data, from the legislative point of view, legislators need to use the conclusions and reasons of the value judgments formed around the opinions of the proponents as the basis for criticism and argumentation, reaching a minimal consensus through power; from the perspective of interpretivism, the judge needs to focus on the legislator's existing law text and legislative background information to give explanation as the basis for future amendments, reaching maximum consensus through power. On searching the legislative background data, different searching paths and methods could be exploited in the distinction between the text data recorded by the recorder and the audio-visual data recorded by the expositor. The enlightenment obtained from legislative background data should be used for the construction and perfection of the wisdom and intelligence of the court. The judges should be the ideological assistants of the legislators. The search for enlightenment from legislative background data can be conducted from three perspectives, namely cognition, arrangement, and disclosure. Therefore, legislative background data should be standardized by law.
基金supported by the National Key Program for Developing Basic Sciences(G1999032801)the National Natural Science Foundation of China(Grant No.40005007,40233033,and 40221503)
文摘The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.
基金funded by the Korea Meteorological Administration Research and Development Program under Grant RACS 2010-2016supported by the Brain Korea 21 project of the Ministry of Education and Human Resources Development of the Korean government
文摘Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC （National Meteorological Center） method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.
基金supported by the 973 Program (Grant No.2010CB950401)the Chinese Academy of Sciences’ Project"Western Pacific Ocean System:Structure,Dynamics and Consequences"(Grant No.XDA11010405)the National Natural Science Foundation of China (Grant No.41176015)
文摘The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean （hereafter AIPOcean） constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving, hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles （mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity-temperature-depth, station data）, remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993-2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.
