Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long...Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long-term training samples not easily available,and(iii)local features are insufficiently reflected.In this work,a multi-model blending(MMB)algorithm with supplemental grid points(SGPs)is experimented to overcome these shortcomings.The MMB algorithm includes three steps:(1)single-model bias-correction,(2)dynamic weight MMB,and(3)light-precipitation elimination.In step 1,quantile mapping(QM)is used and SGPs are configured to expand the sample size.The SGPs are chosen based on similarity of topography,spatial distance,and climatic characteristics of local precipitation.In step 2,the dynamic weight MMB uses the idea of ensemble forecasting:a precipitation process can be forecast if more than 40% of the models predict such a case;moreover,threat score(TS)is used to update the weights of ensemble members.Finally,in step 3,the number of false alarms of light precipitation is reduced,thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.Verification results show that using the MMB algorithm has effectively improved the TS and bias score(BS)for blended 6-h QPF.The rate of increase in TS for heavy rainfall(25-mm threshold)reaches 20%-40%;in particular,the improvement has reached 47.6% for forecast lead time of 24 h,compared with the ECMWF model.Meanwhile,the BS is closer to 1,which is better than any single-model forecast.In sum,the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.展开更多
Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neur...Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.展开更多
This article aims to derive, analyse, and implement an efficient one-step implicit hybrid method with block extension comprised of seven off-step points to directly solve Initial Value Problems (IVPs) of general four-...This article aims to derive, analyse, and implement an efficient one-step implicit hybrid method with block extension comprised of seven off-step points to directly solve Initial Value Problems (IVPs) of general four-order ordinary differential equations. For the resolution of the fourth-order IVPs, the exact was approximated by a polynomial termed basis function. The partial sum of the basis function and its fourth derivative were interpolated and collocated at some selected grid and off-grid points for the unknown parameters to be determined. The derived method, when tested, is found to be consistent, convergent, and zero-stable. The method’s accuracy and usability were experimented with using specific sample problems, and the findings revealed that it surpassed some cited methods in terms of accuracy.展开更多
Landscape pattern is a widely used concept for the demonstration of landscape characteristic features. The integral spatial distribution trend of landscape elements is interested point in the landscape ecological rese...Landscape pattern is a widely used concept for the demonstration of landscape characteristic features. The integral spatial distribution trend of landscape elements is interested point in the landscape ecological research, especially in those of complex secondary forest regions with confusing mosaics of land cover. Trend surface analysis which used in community and population ecological researches was introduced to reveal the landscape pattern. A reasonable and reliable approach for application of trend surface analysis was provided in detail. As key steps of the approach, uniform grid point sampling method was developed. The efforts were also concentrated at an example of Guandishan forested landscape. Some basic rules of spatial distribution of landscape elements were exclaimed. These will be benefit to the further study in the area to enhance the forest sustainable management and landscape planning.展开更多
基金Supported by the National Key Research and Development Program of China(2017YFC1502004)Special Project for Forecasters of China Meteorological Administration(CMAYBY2020-162)Special Project for Forecasters of National Meteorological Center(Y202135)。
文摘Quantitative Precipitation Forecast(QPF)is a challenging issue in seamless prediction.QPF faces the following difficulties:(i)single rather than multiple model products are still used;(ii)most QPF methods require long-term training samples not easily available,and(iii)local features are insufficiently reflected.In this work,a multi-model blending(MMB)algorithm with supplemental grid points(SGPs)is experimented to overcome these shortcomings.The MMB algorithm includes three steps:(1)single-model bias-correction,(2)dynamic weight MMB,and(3)light-precipitation elimination.In step 1,quantile mapping(QM)is used and SGPs are configured to expand the sample size.The SGPs are chosen based on similarity of topography,spatial distance,and climatic characteristics of local precipitation.In step 2,the dynamic weight MMB uses the idea of ensemble forecasting:a precipitation process can be forecast if more than 40% of the models predict such a case;moreover,threat score(TS)is used to update the weights of ensemble members.Finally,in step 3,the number of false alarms of light precipitation is reduced,thus alleviating unreasonable expansion of the precipitation area caused by the blending of multiple models.Verification results show that using the MMB algorithm has effectively improved the TS and bias score(BS)for blended 6-h QPF.The rate of increase in TS for heavy rainfall(25-mm threshold)reaches 20%-40%;in particular,the improvement has reached 47.6% for forecast lead time of 24 h,compared with the ECMWF model.Meanwhile,the BS is closer to 1,which is better than any single-model forecast.In sum,the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.
基金supported by the National Key R&D Program of China(Grant No.2022YFA1604200)National Natural Science Foundation of China(Grant No.12261131495)+1 种基金Beijing Municipal Science and Technology Commission,Adminitrative Commission of Zhongguancun Science Park(Grant No.Z231100006623006)Institute of Systems Science,Beijing Wuzi University(Grant No.BWUISS21)。
文摘Optical solitons,as self-sustaining waveforms in a nonlinear medium where dispersion and nonlinear effects are balanced,have key applications in ultrafast laser systems and optical communications.Physics-informed neural networks(PINN)provide a new way to solve the nonlinear Schrodinger equation describing the soliton evolution by fusing data-driven and physical constraints.However,the grid point sampling strategy of traditional PINN suffers from high computational complexity and unstable gradient flow,which makes it difficult to capture the physical details efficiently.In this paper,we propose a residual-based adaptive multi-distribution(RAMD)sampling method to optimize the PINN training process by dynamically constructing a multi-modal loss distribution.With a 50%reduction in the number of grid points,RAMD significantly reduces the relative error of PINN and,in particular,optimizes the solution error of the(2+1)Ginzburg–Landau equation from 4.55%to 1.98%.RAMD breaks through the lack of physical constraints in the purely data-driven model by the innovative combination of multi-modal distribution modeling and autonomous sampling control for the design of all-optical communication devices.RAMD provides a high-precision numerical simulation tool for the design of all-optical communication devices,optimization of nonlinear laser devices,and other studies.
文摘This article aims to derive, analyse, and implement an efficient one-step implicit hybrid method with block extension comprised of seven off-step points to directly solve Initial Value Problems (IVPs) of general four-order ordinary differential equations. For the resolution of the fourth-order IVPs, the exact was approximated by a polynomial termed basis function. The partial sum of the basis function and its fourth derivative were interpolated and collocated at some selected grid and off-grid points for the unknown parameters to be determined. The derived method, when tested, is found to be consistent, convergent, and zero-stable. The method’s accuracy and usability were experimented with using specific sample problems, and the findings revealed that it surpassed some cited methods in terms of accuracy.
文摘Landscape pattern is a widely used concept for the demonstration of landscape characteristic features. The integral spatial distribution trend of landscape elements is interested point in the landscape ecological research, especially in those of complex secondary forest regions with confusing mosaics of land cover. Trend surface analysis which used in community and population ecological researches was introduced to reveal the landscape pattern. A reasonable and reliable approach for application of trend surface analysis was provided in detail. As key steps of the approach, uniform grid point sampling method was developed. The efforts were also concentrated at an example of Guandishan forested landscape. Some basic rules of spatial distribution of landscape elements were exclaimed. These will be benefit to the further study in the area to enhance the forest sustainable management and landscape planning.
