A new method of constructing a sea level pressure field from satellite microwave scatterometer measurements is presented. It is based on variational assimilation in combination with a regularization method using geost...A new method of constructing a sea level pressure field from satellite microwave scatterometer measurements is presented. It is based on variational assimilation in combination with a regularization method using geostrophic vorticity to construct a sea level pressure field from scatterometer data that are given in this paper, which offers a new idea for the application of scatterometer measurements. Firstly, the geostrophic vorticity from the scatterometer data is computed to construct the observation field, and the vorticity field in an area and the sea level pressure on the borders are assimilated. Secondly, the gradient of sea level pressure （semi-norm） is used as the stable functional to educe the adjoint system, the adjoint boundary condition and the gradient of the cost functional in which a weight parameter is introduced for the harmony of the system and the Tikhonov regularization techniques in inverse problem are used to overcome the ill-posedness of the assimilation. Finally, the iteration method of the sea level pressure field is developed.展开更多
The sea level pressure field can be computed from sea surface winds retrieved from satellite microwave scatterometer measurements, based on variational assimilation in combination with a regularization method given in...The sea level pressure field can be computed from sea surface winds retrieved from satellite microwave scatterometer measurements, based on variational assimilation in combination with a regularization method given in part I of this paper. First, the validity of the new method is proved with a simulation experiment. Then, a new processing procedure for the sea level pressure retrieval is built by combining the geostrophic wind, which is computed from the scatterometer 10-meter wind using the University of Washington planetary boundary layer model using this method. Finally, the feasibility of the method is proved using an actual case study.展开更多
In order to realize monitoring and early warning and comprehensive management of wheat stripe rust and to reduce its occurrence in Nanchong City, the occurrence and epidemic regularity of wheat stripe rust in Nanchong...In order to realize monitoring and early warning and comprehensive management of wheat stripe rust and to reduce its occurrence in Nanchong City, the occurrence and epidemic regularity of wheat stripe rust in Nanchong was studied by system monitoring and general survey, resistance identification, physiological race monitoring and meteorological data analysis. The initial occurrence location and spreading pathway of Puccinia striiformis f. sp. tritici （Pst） were first verified; there were two infection peaks of wheat stripe rust in Nanchong and one to three epidemic peaks in fields, in which the occurrence area of the first epidemic peak played a pivotal role in disease prevalence in that year; the cumulative occurrence area in late January was positively correlated with annual occurrence area, with the correlation coefficient of 0.769 ; the prediction model for infected field rate, diseased plant rate and annual occurrence area was established. The internal reason for heavy occurrence and prevalence of wheat stripe rust in Nanchong was the decline or loss of wheat resistance against stripe rust, as well as the appearance of physiological races of Pst, which later became dominant races. Large fluctuation of temperature in warm winter and spring and more fog and dew days were external reasons responsible for prevalence of stripe rust. From 2002 to 2014 ,the accuracy rate of short-term prediction of wheat stripe rust reached 100%, while that of me- dium-term and long-term prediction reached over 98% and 95%, respectively, 5% -15% higher than that of the years before 1998.展开更多
Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:S...Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.41175025)
文摘A new method of constructing a sea level pressure field from satellite microwave scatterometer measurements is presented. It is based on variational assimilation in combination with a regularization method using geostrophic vorticity to construct a sea level pressure field from scatterometer data that are given in this paper, which offers a new idea for the application of scatterometer measurements. Firstly, the geostrophic vorticity from the scatterometer data is computed to construct the observation field, and the vorticity field in an area and the sea level pressure on the borders are assimilated. Secondly, the gradient of sea level pressure （semi-norm） is used as the stable functional to educe the adjoint system, the adjoint boundary condition and the gradient of the cost functional in which a weight parameter is introduced for the harmony of the system and the Tikhonov regularization techniques in inverse problem are used to overcome the ill-posedness of the assimilation. Finally, the iteration method of the sea level pressure field is developed.
基金Project supported by the National Natural Science Foundation of China (Grant No. 41175025)
文摘The sea level pressure field can be computed from sea surface winds retrieved from satellite microwave scatterometer measurements, based on variational assimilation in combination with a regularization method given in part I of this paper. First, the validity of the new method is proved with a simulation experiment. Then, a new processing procedure for the sea level pressure retrieval is built by combining the geostrophic wind, which is computed from the scatterometer 10-meter wind using the University of Washington planetary boundary layer model using this method. Finally, the feasibility of the method is proved using an actual case study.
基金Supported by Comprehensive Prevention and Treatment Monitoring Station of Inoculum Source of Wheat Stripe Rust in Nanchong City(NYBNJH[2003]104)Notice of the Ministry of Agriculture on Identification of the First Batch of National Modern Agricultural Demonstration Zone(NJF[2010]22)+2 种基金Occurrence and Epidemic Regularity of Wheat Stripe Rust and Its Integrated Control Technology in Nanchong City(N1998-ZC018)Fundamental Research Funds for the Central Universities(XDJK2015C060,SWU114046,2362015xk04)Open Project Program of State Key Laboratory of Crop Stress Biology for Arid Areas(CSBAA2015009)
文摘In order to realize monitoring and early warning and comprehensive management of wheat stripe rust and to reduce its occurrence in Nanchong City, the occurrence and epidemic regularity of wheat stripe rust in Nanchong was studied by system monitoring and general survey, resistance identification, physiological race monitoring and meteorological data analysis. The initial occurrence location and spreading pathway of Puccinia striiformis f. sp. tritici （Pst） were first verified; there were two infection peaks of wheat stripe rust in Nanchong and one to three epidemic peaks in fields, in which the occurrence area of the first epidemic peak played a pivotal role in disease prevalence in that year; the cumulative occurrence area in late January was positively correlated with annual occurrence area, with the correlation coefficient of 0.769 ; the prediction model for infected field rate, diseased plant rate and annual occurrence area was established. The internal reason for heavy occurrence and prevalence of wheat stripe rust in Nanchong was the decline or loss of wheat resistance against stripe rust, as well as the appearance of physiological races of Pst, which later became dominant races. Large fluctuation of temperature in warm winter and spring and more fog and dew days were external reasons responsible for prevalence of stripe rust. From 2002 to 2014 ,the accuracy rate of short-term prediction of wheat stripe rust reached 100%, while that of me- dium-term and long-term prediction reached over 98% and 95%, respectively, 5% -15% higher than that of the years before 1998.
基金supported by the National Social Science Foundation of China(Grant No.12CTQ032)
文摘Purpose:In order to annotate the semantic information and extract the research level information of research papers,we attempt to seek a method to develop an information extraction system.Design/methodology/approach:Semantic dictionary and conditional random field model(CRFM)were used to annotate the semantic information of research papers.Based on the annotation results,the research level information was extracted through regular expression.All the functions were implemented on Sybase platform.Findings:According to the result of our experiment in carbon nanotube research,the precision and recall rates reached 65.13%and 57.75%,respectively after the semantic properties of word class have been labeled,and F-measure increased dramatically from less than 50%to60.18%while added with semantic features.Our experiment also showed that the information extraction system for research level(IESRL)can extract performance indicators from research papers rapidly and effectively.Research limitations:Some text information,such as that of format and chart,might have been lost due to the extraction processing of text format from PDF to TXT files.Semantic labeling on sentences could be insufficient due to the rich meaning of lexicons in the semantic dictionary.Research implications:The established system can help researchers rapidly compare the level of different research papers and find out their implicit innovation values.It could also be used as an auxiliary tool for analyzing research levels of various research institutions.Originality/value:In this work,we have successfully established an information extraction system for research papers by a revised semantic annotation method based on CRFM and the semantic dictionary.Our system can analyze the information extraction problem from two levels,i.e.from the sentence level and noun(phrase)level of research papers.Compared with the extraction method based on knowledge engineering and that on machine learning,our system shows advantages of the both.
