Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method

A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast （LAF） method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40％-50％ in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau.

SCGM(1,m)_c model with time-lag and its application in deformation analysis

Originated in the early 1990s, SCGM(1, m ) c model has enjoyed widespread application in the fields of urban planning, society economy prediction and modal control in recent years. However, none of these applications have taken account of time lag effects in the modeling process. Aiming at overcoming the defect, the authors introduced time lag items into SCGM(1, m ) c model and developed a SCGM(1, m ) c model with time lag, then discusses in detail some principal problems in the model, such as parameters estimation, model verifying, model prediction, etc. The model was used on a real slope monitoring project and compared with the conventional SCGM(1, m ) c model. The results show an improvement of average models precision from 1.321 to 0.238 and total average of relative prediction errors from 12.41% to 7.98% when the modeling data length ranges from 29 to 48 in the slope monitoring case.

A Nonlinear Time-lag Differential Equation Mdel for Predicting Monthly Precipitation

This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.

LINEAR CONTROL SYSTEMS WITH TIME-LAG:CONTROLLABILITY AND LOCAL PARAMETRICAL IDENTIFIABILITY WITH EXTENSIONS IN PRE-HILBERT SPACES

Conditions are given for controllability, output controllability and local identifiabitity of a parametrization in a subset of of linear differential systems with time-lags.

Time-Lag Effect of Dietary Fiber and Fat Intake Ratio on Japanese Colon Cancer Mortality

The daily intake of total dietary fiber (TDF) was evaluated from data of the National Nutrition Survey (NNS) in Japan for 41 years since 1947. An interrelationship between the nutrient intake, including TDF, and the mortality from colon cancer in Japanese people was calculated by a simple correlation coefficient and time-series correlation coeffcient.TDF intake per capita decreased rapidly from 27.4 g in 1947 to 15.8 g in 1963, and subsequently decreased by a lesser rate to 15.3 g in 1987. Fat intake increased rapidly from 18.0 g in 1950 to 56.6 g in 1987.The age-adjusted mortality from colon cancer shows a significant positive correlation with both the intakes of animal protein and of total fat, and the fat energy ratio. A time-series analysis indicates that the mortality from colon cancer was negatively correlated with TDF with a 15-27 year delay, the maximum correlation existing with a 23-year lag (r = -0.947). The TDF intake was less than 17.9 g in 1965. At the same time, the mortality from colon cancer increased rapidly. A fat/TDF ratio above 3.0 resulted in a rapid increase in colon cancer mortality.The non-adjusted mortality from colon cancer has much the same interrelationship with TDF and fat intake as the adjusted figures. It is suggested that the cause of the increased mortality from colon cancer in Japan is positively related to the increased intake of fat and protein. In addition, the decrease in TDF intake has accelerated the mortality of colon cancer after a delay of 23-24 years. The importance of fat/TDF as a nutritional criterion for the incidence of colon cancer needs to be better recognized

On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

The Relationship between Normalized Difference Vegetation Index(NDVI) and Climate Factors in the Semiarid Region:A Case Study in Yalu Tsangpo River Basin of Qinghai-Tibet Plateau

The Yalu Tsangpo River basin is a typical semi-arid and cold region in the Qinghai-Tibet Plateau, where significant climate change has been detected in the past decades. The objective of this paper is to demonstrate how the regional vegetation, especially the typical plant types, responds to the climate changes. In this study, the model of gravity center has been firstly introduced to analyze the spatial-temporal relationship between NDVI and climate factors considering the time-lag effect. The results show that the vegetation grown has been positively influenced by the rainfall and precipitation both in moving tracks of gravity center and time-lag effect especially for the growing season during the past thirteen years. The herbs and shrubs are inclined to be influenced by the change of rainfall and temperature, which is indicated by larger positive correlation coefficients at the 0.05 confidence level and shorter lagging time. For the soil moisture, the significantly negative relationship of NDV-PDI indicates that the growth and productivity of the vegetation are closely related to the short-term soil water, with the correlation coefficients reaching the maximum value of o.81 at Lag 0-1. Among the typicalvegetation types of plateau, the shrubs of low mountain, steppe and meadow are more sensitive to the change of soil moisture with coefficients of -0.95, -0.93, -0.92, respectively. These findings reveal that the spatial and temporal heterogeneity between NDVI and climatic factors are of great ecological significance and practical value for the protection of eco-environment in Qinghai-Tibet Plateau.

On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector ma- chines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to de- velop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged win- dow technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.

A CHARACTERISTIC CORRELATION ANALYSIS BETWEEN THE ASIA SUMMER MONSOON MEMBERS AND THE WEST PACIFIC SUBTROPICAL HIGH

In this paper, by using the pentad-mean NCAR/NCEP reanalysis data for the period of 1958-1997, some characteristic indices of describing the activity of Asian summer monsoon system members are defined and calculated. Based on the above works, a time-lag correlation analysis method is introduced for the correlation analysis between the Asian summer monsoon system and the west Pacific subtropical high (WPSH) area index, and some meaningful interaction processes and characteristic phenomena between them are revealed and discussed accordingly. It is shown that there exists some remarkable time-lag correlations in various degree between the Asian summer monsoon system members and the WPSH area index, and they interact and feedback with each other, which consists of the whole Asian summer monsoon system.

Time-lagged and cumulative effects of drought and anthropogenic activities on China's vegetation greening from 1990 to 2018

Previous studies have confirmed the time-lagged and cumulative effects of drought and anthropogenic activities on vegetation growth,but these studies focus on the time-lagged effect of drought and are poorly known how vegetation productivity responds to anthropogenic activities.Here,based on the reconstructed Normalized Difference Vegetation Index,the Standardized Precipitation Evapotranspiration Index and land use degree comprehensive index,we diagnosed the spatiotemporal pattern of vegetation and drought,investigated time-lagged and cumulative effects of drought and anthropogenic activities over China through the month where the maximum correlation coefficient occurred.It revealed that the browning trend of 32.21%of vegetated lands was covered by overall greening,especially northwestern China.Drought intensified with a rate of 0.0014/year.in 66.41%and 54.57%of the vegetated lands had time-lagged and cumulative response to drought,with a shorter timescales of 1–4 months,indicating the higher sensitivity of vegetation growth to drought.There was a U-shaped relationship between moisture conditions and vegetation response time.49.9%of China’s vegetation showed time-lagged effects to anthropogenic activities,with a longer timescales of 6–10 years,demonstrating that anthropogenic activities triggered ecological changes but vegetation ecosystems cannot keep pace.The accumulated and time-lagged years declined with increased land use intensity.

Considering time-lag effects can improve the accuracy of NPP simulation using a light use efficiency model

Most terrestrial models synchronously calculate net primary productivity(NPP)using the input climate variable,without the consideration of time-lag effects,which may increase the uncertainty of NPP simulation.Based on Normalized Difference Vegetation Index(NDVI)and climate data,we used the time lag cross-correlation method to investigate the time-lag effects of temperature,precipitation,and solar radiation in different seasons on NDVI values.Then,we selected the Carnegie-Ames-Stanford approach(CASA)model to estimate the NPP of China from 2002 to 2017.The results showed that the response of vegetation growth to climate factors had an obvious lag effect,with the longest time lag in solar radiation and the shortest time lag in temperature.The time lag of vegetation to the climate variable showed great tempo-spatial heterogeneities among vegetation types,climate types,and vegetation growth periods.Based on the validation using eddy covariance data,the results showed that the simulation accuracy of the CASA model considering the time-lag effects was effectively improved.By considering the time-lag effects,the average total amount of NPP modeled by CASA during 2001-2017 in China was 3.977 PgC a^(−1),which is 11.37%higher than that of the original model.This study highlights the importance of considering the time lag for the simulation of vegetation growth,and provides a useful tool for the improvement of the vegetation productivity model.

KEY AREAS OF AIR-SEA INTERACTION IN GLOBAL OCEANS AND STUDY OF THE CLIMATOLOGICAL FEATURES

Seven key areas of air-sea interaction in the global oceans are determined by comprehensive analysis of the global data of monthly mean sea surface temperature (SST), surface wind, temperature, humidity, sea surface sensible heat and latent heat fluxes. The time-lag correlation between SST and each atmospheric element in each key area are focally analyzed to expose the same and the different features of air-sea interaction in different key areas. The results show that the air-sea thermal interaction is strong in each area, SST, tem-perature and humidity can be fairly replaced with one another, particularly in the central eastern Pacific and the south India Ocean. The dynamic effect on SST is different in different areas and in the central western Pacific such effect is more important. The correlation between sensible heat, latent heat and SST is more significant in the eastern Pacific, the western Pacific and the two major monsoon areas — the northwestern Pacific and the south India Ocean. By analyzing the sustainable correlation probability of SST and every atmospheric element in each key area, we further know that the anomalies of which element, in which area and in which period are well sustained or easily destroyed. This is beneficial not only to prediction, but also to discussion of the physical mechanism of air-sea interaction.

Livestock Dynamic Responses to Climate Change in Alpine Grasslands on the Northern Tibetan Plateau:Forage Consumption and Time-lag Effects

Climate change and forage-intake are important components of livestock population systems,but our knowledge about the effects of changes in these properties on livestock is limited,particularly on the Northern Tibetan Plateau.Based on corresponding independent models（CASA and TEM）,a human-induced NPP（NPPH） value and forage-intake threshold were obtained to determine their influences on livestock population fluctuation and regrowth on the plateau.The intake threshold value provided compatible results with livestock population performance.If the forage-intake was greater than the critical value of 1.9（kg DM d^（-1） sheep^（-1））,the livestock population increased;otherwise,the livestock population decreased.It takes four years to transfer a disturbance in primary productivity to the next trophic level.The relationships between livestock population and NPP_H value determined population dynamics via the forage-intake value threshold.Improved knowledge on lag effects will advance our understanding of drivers of climatic changes on livestock population dynamics.

ON LIAPUNOV FUNCTIONAL IN THEORY OF STABILITY OF SYSTEMS WITH TIME-LAG

It is well known,that in the theory of stability in differential equations,Liapunov's second method may be the most important The center problem of Liapunov's second method is construction of Liapunov function for concrete problems.Beyond any doubt,construction of Liapunov functions is an art.In the case of functional differential equations,there were also many attempts to establish various kinds of Liapunov type theorems.Recently Burton[2]presented an excellent theorem using the Liapunov functional to solve the asymptotic stability of functional differential equation with bounded delay. However,the construction of such a Liapunov functional is still very hard for concrete problems. In this paper, by utilizing this theorem due to Burton,we construct concrete Liapunov functional for certain and nonlinear delay differential equations and derive new sufficient conditions for asymptotic stability.Those criteria improve the result of literature[1]and they are with simple forms,easily checked and applicable.

Nonlinear dynamics of a flapping rotary wing:Modeling and optimal wing kinematic analysis

The analysis of the passive rotation feature of a micro Flapping Rotary Wing（FRW）applicable for Micro Air Vehicle（MAV） design is presented in this paper. The dynamics of the wing and its influence on aerodynamic performance of FRW is studied at low Reynolds number（~10~3）.The FRW is modeled as a simplified system of three rigid bodies： a rotary base with two flapping wings. The multibody dynamic theory is employed to derive the motion equations for FRW. A quasi-steady aerodynamic model is utilized for the calculation of the aerodynamic forces and moments. The dynamic motion process and the effects of the kinematics of wings on the dynamic rotational equilibrium of FWR and the aerodynamic performances are studied. The results show that the passive rotation motion of the wings is a continuous dynamic process which converges into an equilibrium rotary velocity due to the interaction between aerodynamic thrust, drag force and wing inertia. This causes a unique dynamic time-lag phenomena of lift generation for FRW, unlike the normal flapping wing flight vehicle driven by its own motor to actively rotate its wings. The analysis also shows that in order to acquire a high positive lift generation with high power efficiency and small dynamic time-lag, a relative high mid-up stroke angle within 7–15° and low mid-down stroke angle within -40° to -35° are necessary. The results provide a quantified guidance for design option of FRW together with the optimal kinematics of motion according to flight performance requirement.

Spatiotemporal analysis of COVID-19 risk in Guangdong Province based on population migration

Population migration,especially population inflow from epidemic areas,is a key source of the risk related to the coronavirus disease 2019(COVID-19)epidemic.This paper selects Guangdong Province,China,for a case study.It utilizes big data on population migration and the geospatial analysis technique to develop a model to achieve spatiotemporal analysis of COVID-19 risk.The model takes into consideration the risk differential between the source cities of population migration as well as the heterogeneity in the socioeconomic characteristics of the destination cities of population migration.It further incorporates a time-lag process based on the time distribution of the onset of the imported cases.In theory,the model will be able to predict the evolutional trend and spatial distribution of the COVID-19 risk for a certain time period in the future and provide support for advanced planning and targeted prevention measures.The research findings indicate the following:(1)The COVID-19 epidemic in Guangdong Province reached a turning point on January 29,2020,after which it showed a gradual decreasing trend.(2)Based on the time-lag analysis of the onset of the imported cases,it is common fora time interval to exist between case importation and illness onset,and the proportion of the cases with an interval of 1-14 days is relatively high.(3)There is evident spatial heterogeneity in the epidemic risk;the risk varies significantly between different areas based on their imported risk,susceptibility risk,and ability to prevent the spread.(4)The degree of connectedness and the scale of population migration between Guangdong’s prefecture-level cities and their counterparts in the source regions of the epidemic,as well as the transportation and location factors of the cities in Guangdong,have a significant impact on the risk classification of the cities in Guangdong.The first-tier cities-Shenzhen and Guangzhou-are high-risk regions.The cities in the Pearl River Delta that are adjacent to Shenzhen and Guangzhou,including Dongguan,Foshan,Huizhou,Zhuhai,Zhongshan,are medium-risk cities.The eastern,northern,and western parts of Guangdong,which are outside of the metropolitan areas of the Pearl River Delta,are considered to have low risks.Therefore,the government should develop prevention and control measures that are specific to different regions based on their risk classification to enable targeted prevention and ensure the smooth operation of society.

Impact of ERP Investment on Company Performance: Evidence from Manufacturing Firms in China

Information Technology （IT） investments, especially Enterprise Resource Planning （ERP） systems, are critical for the survival and development of companies. Therefore, understanding the impact of ERP in- vestment is of great importance to managers and researchers. As a corporate performance indicator, Tobin＇s Q has some inherent advantages compared to other accounting indicators, and it can better reflect the con- tribution of ERP investment to company performance. This study employs multiple regression models to examine the impact of ERP investment on Tobin＇s Q. The sample consists of 126 manufacturing companies listed on the Shanghai and Shenzhen Stock Exchanges from 1999 to 2007. Empirical results show that in the first three years after ERP implementation, there is no significant change in Tobin＇s Q; however, in the fourth year, Tobin＇s Q increases significantly. The results indicate that, as a strategic long-term investment accompanied by large-scale business process reengineering and organizational learning, ERP implementa- tion has time-lagged effects; nonetheless, it eventually produces significant benefits.