Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
In this study, a series of sensitivity experiments were performed for two tropical cyclones （TCs）, TC Longwang （2005） and TC Sinlaku （2008）, to explore the roles of locations and patterns of initial errors in un...In this study, a series of sensitivity experiments were performed for two tropical cyclones （TCs）, TC Longwang （2005） and TC Sinlaku （2008）, to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts. Specifically, three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation （CNOP）, first singular vector （FSV）, and composite singular vector （CSV） methods. Additionally, random initial errors in randomly selected areas were considered. Based on these four types of initial errors and areas, we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors, and to determine which type of initial errors and areas has the greatest impact on TC forecasts. Overall, results from the experiments indicate the following： （1） The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas. From the perspective of statisticul analysis, and by comparison, the impact of random errors introduced into the CNOP target area was greatest. （2） The initial errors with CNOP, CSV, or FSV patterns were likely to grow faster than random errors. （3） The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.展开更多
This study aimed to conduct measurement uncertainty assessment of a new method for determination of Sudan colorants（Sudan Ⅰ, Ⅱ, Ⅲ and Ⅳ） in food by high performance liquid chromatography（HPLC）. Samples were ex...This study aimed to conduct measurement uncertainty assessment of a new method for determination of Sudan colorants（Sudan Ⅰ, Ⅱ, Ⅲ and Ⅳ） in food by high performance liquid chromatography（HPLC）. Samples were extracted with organic solvents（hexane, 20% acetone） and first purified by magnesium trisilicate（2Mg O·3Si O2）. The Sudan colorants（Sudan Ⅰ–Ⅳ） were also initially separated on C8 by gradient elution using acetonitrile and 0.1%（v/v） formic acid aqueous solution as the mobile phases and detected with diode-array detector（DAD）. The uncertainty of mathematical model of Sudan Ⅰ, Ⅱ, Ⅲ, Ⅳ is based on EURACHEM guidelines. The sources and components of uncertainty were calculated. The experiment gave a good linear relationship over the concentration from 0.4 to 4.0 μg/m L and spiked recoveries were from 74.0% to 97.5%. The limits of determination（LOD） were 48, 61, 36, 58 μg/kg for the four analytes, respectively. The total uncertainty of Sudan colorants（Sudan Ⅰ, Ⅱ, Ⅲ and Ⅳ） was 810±30.8, 790±28.4, 750±27.0, 730±50.0 μg/kg, respectively. The recovery uncertainty was the most significant factor contributing to the total uncertainty. The developed method is simple, rapid, and highly sensitive. It can be used for the determination of trace Sudan dyes in food samples. The sources of uncertainty have been identified and uncertainty components have been simplified and considered.展开更多
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金sponsored by the National Natural Science Foundation of China(Grant Nos. 40830955)the China Meteorological Administration (Grant No. GYHY200906009)
文摘In this study, a series of sensitivity experiments were performed for two tropical cyclones （TCs）, TC Longwang （2005） and TC Sinlaku （2008）, to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts. Specifically, three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation （CNOP）, first singular vector （FSV）, and composite singular vector （CSV） methods. Additionally, random initial errors in randomly selected areas were considered. Based on these four types of initial errors and areas, we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors, and to determine which type of initial errors and areas has the greatest impact on TC forecasts. Overall, results from the experiments indicate the following： （1） The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas. From the perspective of statisticul analysis, and by comparison, the impact of random errors introduced into the CNOP target area was greatest. （2） The initial errors with CNOP, CSV, or FSV patterns were likely to grow faster than random errors. （3） The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.
基金supported by grants from Non-profit Projects of Ministry of Environmental Protection of the People’s Republic of China(No.201309044)the Foundation for Excellent Young Talents of Hubei Center for Disease Control and Prevention,and the Foundation for Medical Leading Personnel of Hubei Province
文摘This study aimed to conduct measurement uncertainty assessment of a new method for determination of Sudan colorants（Sudan Ⅰ, Ⅱ, Ⅲ and Ⅳ） in food by high performance liquid chromatography（HPLC）. Samples were extracted with organic solvents（hexane, 20% acetone） and first purified by magnesium trisilicate（2Mg O·3Si O2）. The Sudan colorants（Sudan Ⅰ–Ⅳ） were also initially separated on C8 by gradient elution using acetonitrile and 0.1%（v/v） formic acid aqueous solution as the mobile phases and detected with diode-array detector（DAD）. The uncertainty of mathematical model of Sudan Ⅰ, Ⅱ, Ⅲ, Ⅳ is based on EURACHEM guidelines. The sources and components of uncertainty were calculated. The experiment gave a good linear relationship over the concentration from 0.4 to 4.0 μg/m L and spiked recoveries were from 74.0% to 97.5%. The limits of determination（LOD） were 48, 61, 36, 58 μg/kg for the four analytes, respectively. The total uncertainty of Sudan colorants（Sudan Ⅰ, Ⅱ, Ⅲ and Ⅳ） was 810±30.8, 790±28.4, 750±27.0, 730±50.0 μg/kg, respectively. The recovery uncertainty was the most significant factor contributing to the total uncertainty. The developed method is simple, rapid, and highly sensitive. It can be used for the determination of trace Sudan dyes in food samples. The sources of uncertainty have been identified and uncertainty components have been simplified and considered.
