Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation o...Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation of the PLANAR algorithm,a software framework designed for fast and exact decoding of quantum codes with a planar structure.The algorithm first converts the optimal decoding of quantum codes into a partition function computation problem of an Ising spin glass model.Then it utilizes the exact Kac–Ward formula to solve it.In this way,PLANAR offers the exact maximum likelihood decoding in polynomial complexity for quantum codes with a planar structure,including the surface code with independent code-capacity noise and the quantum repetition code with circuit-level noise.Unlike traditional minimumweight decoders such as minimum-weight perfect matching(MWPM),PLANAR achieves theoretically optimal performance while maintaining polynomial-time efficiency.In addition,to demonstrate its capabilities,we exemplify the implementation using the rotated surface code,a commonly used quantum error correction code with a planar structure,and show that PLANAR achieves a threshold of approximately p_(uc)≈0.109 under the depolarizing error model,with a time complexity scaling of O(N^(0.69)),where N is the number of spins in the Ising model.展开更多
By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Huna...By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.展开更多
An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given t...An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.展开更多
In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy usi...In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.展开更多
Based on the hydrodynamic model and the Xinanjiang model, the fiver stage forecasting model has been proposed. But its performance is not satisfactory as applied to estuary areas. River roughness is a sensitive parame...Based on the hydrodynamic model and the Xinanjiang model, the fiver stage forecasting model has been proposed. But its performance is not satisfactory as applied to estuary areas. River roughness is a sensitive parameter in the hydrodynamic model, and its value is related to some substantial uncertainties in the tidal fiver. According to roughness tests, a new method of roughness dynamic correction was developed to improve the performance of the stage model. The method was focused on the usage of observed data for the studied section, and its parameters were analyzed. Nested with the dynamic correction of roughness, the stage model was applied to the tidal reach of the Caoe River. The results demonstrate that the roughness dynamic correction can improve the simulation accuracy of the stage model, and especially has the capacity of reducing the errors at peak stages.展开更多
Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.T...Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.Through statistical analysis of the time series of the LS+AR model's short-term prediction residuals,we found that there is a good correlation of model prediction residuals between adjacent terms.These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results,thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction.Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction.In fact,the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.展开更多
The primary objective of Chinese grammatical error correction(CGEC)is to detect and correct errors in Chinese sentences.Recent research shows that large language models(LLMs)have been applied to CGEC with significant ...The primary objective of Chinese grammatical error correction(CGEC)is to detect and correct errors in Chinese sentences.Recent research shows that large language models(LLMs)have been applied to CGEC with significant results.For LLMs,selecting appropriate reference examples can help improve their performance.However,existing methods predominantly rely on text similarity for example retrieval,a strategy that frequently mismatches actual error patterns and retrieves lexically similar yet grammatically irrelevant sentences.To address this problem,we propose a method named RE^(2),which retrieves appropriate examples with explanations of grammatical errors.Instead of using text similarity of the input sentence,we use explanations of grammatical errors to select reference examples,which are used by LLMs to improve the performance of CGEC.We conduct experiments on two CGEC datasets and create a high-quality grammatical error explanation(GEE)dataset,which is not only used in our research but also serves as a valuable resource for future studies in both CGEC and GEE.The experimental results on the two datasets indicate that our proposed method effectively improves the performance of CGEC.展开更多
Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the pred...Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.展开更多
Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a cor...Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can eff...In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.展开更多
Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation tec...Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.展开更多
Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use o...Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.展开更多
Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the cent...Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.展开更多
The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correct...The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.展开更多
Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topo...Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.展开更多
Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up base...Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12325501,12047503,and 12247104)the Chinese Academy of Sciences(Grant No.ZDRW-XX-2022-3-02)P.Z.is partially supported by the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301900).
文摘Quantum error correction is essential for realizing fault-tolerant quantum computing,where both the efficiency and accuracy of the decoding algorithms play critical roles.In this work,we introduce the implementation of the PLANAR algorithm,a software framework designed for fast and exact decoding of quantum codes with a planar structure.The algorithm first converts the optimal decoding of quantum codes into a partition function computation problem of an Ising spin glass model.Then it utilizes the exact Kac–Ward formula to solve it.In this way,PLANAR offers the exact maximum likelihood decoding in polynomial complexity for quantum codes with a planar structure,including the surface code with independent code-capacity noise and the quantum repetition code with circuit-level noise.Unlike traditional minimumweight decoders such as minimum-weight perfect matching(MWPM),PLANAR achieves theoretically optimal performance while maintaining polynomial-time efficiency.In addition,to demonstrate its capabilities,we exemplify the implementation using the rotated surface code,a commonly used quantum error correction code with a planar structure,and show that PLANAR achieves a threshold of approximately p_(uc)≈0.109 under the depolarizing error model,with a time complexity scaling of O(N^(0.69)),where N is the number of spins in the Ising model.
基金Supported by the Scientific Research Subject of Department of Education in Hunan Province(10C0556)
文摘By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.
基金funded by the National Natural Science Foundation Science Fund for Youth (Grant No.41405095)the Key Projects in the National Science and Technology Pillar Program during the Twelfth Fiveyear Plan Period (Grant No.2012BAC22B02)the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)
文摘An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.
文摘In this paper, we conduct research on the large precision instrument error correction model under the perspectives of stability androbustness. It is one of the effective methods to improve the instruments accuracy using error correction technology, but at present, a lot of errorcorrection is limited to the system error modifi cation, only a small number of the instruments to an error in the dynamic error correction timely,device on the instrument precision sensors, apparently complicate the instrument structure. To fully system error correction that will affect theprecision of instrument mainly random error. Instrument is the main task of error correction is to use a certain method to compensate separableinstruments each component part of a deterministic system error, so the key problems of error correction as is the requirement of equipmentstructure stability is good, with this to ensure that the instrument error of the uncertainty, so that the fundamental fl aw. Under this basis, this paperproposes the novel countermeasure of the issues that is innovative.
基金Project supported by the National Natural Science Foundation of China(Grant No.50679024)the Program"Eleven-Five"for Science and Technology of China(Grant No.2006BAC05B02).
文摘Based on the hydrodynamic model and the Xinanjiang model, the fiver stage forecasting model has been proposed. But its performance is not satisfactory as applied to estuary areas. River roughness is a sensitive parameter in the hydrodynamic model, and its value is related to some substantial uncertainties in the tidal fiver. According to roughness tests, a new method of roughness dynamic correction was developed to improve the performance of the stage model. The method was focused on the usage of observed data for the studied section, and its parameters were analyzed. Nested with the dynamic correction of roughness, the stage model was applied to the tidal reach of the Caoe River. The results demonstrate that the roughness dynamic correction can improve the simulation accuracy of the stage model, and especially has the capacity of reducing the errors at peak stages.
基金supported by the National Natural Science Foundation of China(Grant Nos.41021061&41174012)
文摘Polar motion depicts the slow changes in the locations of the poles due to the earth's internal instantaneous axis of rotation. The LS+AR model is recognized as one of the best models for polar motion prediction.Through statistical analysis of the time series of the LS+AR model's short-term prediction residuals,we found that there is a good correlation of model prediction residuals between adjacent terms.These indicate that the preceding model prediction residuals and experiential adjustment matrixes can be used to correct the next prediction results,thereby forming a new LS+AR model with additional error correction that applies to polar motion prediction.Simulated predictions using this new model revealed that the proposed method can improve the accuracy and reliability of polar motion prediction.In fact,the accuracies of ultra short-term and short-term predictions using the new model were equal to the international best level at present.
基金support of the National Natural Science Foundation of China(NSFC)(Grant Nos.62236004,62206078,and 62476073).
文摘The primary objective of Chinese grammatical error correction(CGEC)is to detect and correct errors in Chinese sentences.Recent research shows that large language models(LLMs)have been applied to CGEC with significant results.For LLMs,selecting appropriate reference examples can help improve their performance.However,existing methods predominantly rely on text similarity for example retrieval,a strategy that frequently mismatches actual error patterns and retrieves lexically similar yet grammatically irrelevant sentences.To address this problem,we propose a method named RE^(2),which retrieves appropriate examples with explanations of grammatical errors.Instead of using text similarity of the input sentence,we use explanations of grammatical errors to select reference examples,which are used by LLMs to improve the performance of CGEC.We conduct experiments on two CGEC datasets and create a high-quality grammatical error explanation(GEE)dataset,which is not only used in our research but also serves as a valuable resource for future studies in both CGEC and GEE.The experimental results on the two datasets indicate that our proposed method effectively improves the performance of CGEC.
基金funded by the National Natural Science Foundation Science Fund for Youth (Grant No.41405095)the Key Projects in the National Science and Technology Pillar Program during the Twelfth Fiveyear Plan Period (Grant No.2012BAC22B02)the National Natural Science Foundation Science Fund for Creative Research Groups (Grant No.41221064)
文摘Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.
文摘Following the publication of Zeng et al.(2023),an inadvertent error was recently identified in Figure 1B and Supplementary Figure S3.To ensure the accuracy and integrity of our published work,we formally request a correction to address this issue and apologize for any confusion this error may have caused.For details,please refer to the modified Supplementary Materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金Project supported by the National Natural Science Foundation of China （Grant Nos 40575036 and 40325015）.Acknowledgement The authors thank Drs Zhang Pei-Qun and Bao Ming very much for their valuable comments on the present paper.
文摘In this paper, an analogue correction method of errors （ACE） based on a complicated atmospheric model is further developed and applied to numerical weather prediction （NWP）. The analysis shows that the ACE can effectively reduce model errors by combining the statistical analogue method with the dynamical model together in order that the information of plenty of historical data is utilized in the current complicated NWP model, Furthermore, in the ACE, the differences of the similarities between different historical analogues and the current initial state are considered as the weights for estimating model errors. The results of daily, decad and monthly prediction experiments on a complicated T63 atmospheric model show that the performance of the ACE by correcting model errors based on the estimation of the errors of 4 historical analogue predictions is not only better than that of the scheme of only introducing the correction of the errors of every single analogue prediction, but is also better than that of the T63 model.
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund(GYHY201206009)National Basic Research Program of China(2012CB955301)China University Research Talents Recruitment Program(B13045)
文摘Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.
文摘Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.
基金Supported by the Fund for Heilongjiang Province Philosophy and Social Sciences Project (08E015)Social Sciences Fund of the Heilongjiang Provincial Education Department (11542014)Scientific Research Fund of Northeast Agricultural University
文摘Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.
基金This project was supported by the National Natural Science Foundation (No. 79800012 and No. 79400014).
文摘The concept of cointegration describes an equilibrium relationship among a set of time-varying variables, and the cointegrated relationship can be represented through an error-correction model (ECM). The error-correction variable, which represents the short-run discrepancy from the equilibrium state in a cointegrated system, plays an important role in the ECM. It is natural to ask how the error-correction mechanism works, or equivalently, how the short-run discrepancy affects the development of the cointegrated system? This paper examines the effect or local influence on the error-correction variable in an error-correction model. Following the argument of the second-order approach to local influence suggested by reference [5], we develop a diagnostic statistic to examine the local influence on the estimation of the parameter associated with the error-correction variable in an ECM. An empirical example is presented to illustrate the application of the proposed diagnostic. We find that the short-run discre pancy may have strong influence on the estimation of the parameter associated with the error-correction model. It is the error-correction variable that the short-run discrepancies can be incorporated through the error-correction mechanism.
基金Program of Science and Technology Department of Sichuan Province(2022YFS0541-02)Program of Heavy Rain and Drought-flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SCQXKJQN202121)Innovative Development Program of the China Meteorological Administration(CXFZ2021Z007)。
文摘Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.
基金supported by the Major Program of the National Natural Science Foundation of China(Grant No.51190091)the National Natural Science Foundation of China(Grant No.51009045)the Open Research Fund Program of the State Key Laboratory of Water Resources and Hydropower Engineering Science of Wuhan University(Grant No.2012B094)
文摘Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.
