Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Impacts of Increasing Model Resolutions and Shortening Forecast Lead Times on QPFs in South China During the Rainy Season

This study investigated the impacts of increasing model resolutions and shortening forecast lead times on the quantitative precipitation forecast(QPF)for heavy-rainfall events over south China during the rainy seasons in 2013-2020.The control experiment,where the analysis-forecast cycles run with model resolutions of about 3 km,was compared to a lower-resolution experiment with model resolutions of about 9 km,and a longer-term experiment activated 12 hours earlier.Rainfall forecasting in the presummer rainy season was significantly improved by improving model resolutions,with more improvements in cases with stronger synoptic-scale forcings.This is partially attributed to the improved initial conditions(ICs)and subsequent forecasts for low-level jets(LLJs).Forecasts of heavy rainfall induced by landfalling tropical cyclones(TCs)benefited from increasing model resolutions in the first 6 hours.Forecast improvements in rainfall due to shortening forecast lead times were more significant at earlier(1-6 h)and later(7-12 h)lead times for cases with stronger and weaker synoptic-scale forcings,respectively,due to the area-and case-dependent improvements in ICs for nonprecipitation variables.Specifically,significant improvements mainly presented over the northern South China Sea for low-level onshore wind of weak-forcing cases but over south China for LLJs of strong-forcing cases during the presummer rainy season,and over south China for all the nonprecipitation variables above the surface during the TC season.However,some disadvantages of higher-resolution and shorter-term forecasts in QPFs highlight the importance of developing ensemble forecasting with proper IC perturbations,which include the complementary advantages of lower-resolution and longer-term forecasts.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Infinitesimal dividing modeling method for dual suppliers inventory model with random lead times

As one of the basic inventory cost models, the （Q, τ）inventory cost model of dual suppliers with random procurement lead time is mostly formulated by using the concepts of ＂effective lead time＂ and ＂lead time demand＂, which may lead to an imprecise inventory cost. Through the real-time statistic of the inventory quantities, this paper considers the precise （Q, τ） inventory cost model of dual supplier procurement by using an infinitesimal dividing method. The traditional modeling method of the inventory cost for dual supplier procurement includes complex procedures. To reduce the complexity effectively, the presented method investigates the statistics properties in real-time of the inventory quantities with the application of the infinitesimal dividing method. It is proved that the optimal holding and shortage costs of dual supplier procurement are less than those of single supplier procurement respectively. With the assumption that both suppliers have the same distribution of lead times, the convexity of the cost function per unit time is proved. So the optimal solution can be easily obtained by applying the classical convex optimization methods. The numerical examples are given to verify the main conclusions.

Probabilistic earthquake early warning times in Fujian Province

Earthquake early warning （EEW） systems are a new and effective way to mitigate the damage associated with earthquakes. A prototype EEW system is currently being constructed in the Fujian Province, a region along the Southeast coast of China. It is anticipated that the system will be completed in time to be tested at the end of this year （2013）. In order to evaluate how much advanced warning the EEW system will be able to provide different cities in Fujian, we established an EEW information release scheme based on the seismic monitoring stations distributed in the region. Based on this scheme, we selected 71 historical earthquakes. We then obtained the delineation of the region＇s potential seismic source data in order to estimate the highest potential seismic intensities for each city as well as the EEW system warning times. For most of the Fujian Province, EEW alarms would sound several seconds prior to the arrival of the destructive wave. This window of time gives city inhabitants the opportunity to take protective measures before the full intensity of the earthquake strikes.

ANALYSIS AND IMPROVEMENT OF LEAD TIME FOR JOB SHOP UNDER MIXED PRODUCTION SYSTEM

Firstly an overview of the potential impact on work-in-process （WIP） and lead time is provided when transfer lot sizes are undifferentiated from processing lot sizes. Simple performance examples are compared to those from a shop with one-piece transfer lots. Next, a mathematical programming model for minimizing lead time in the mixed-model job shop is presented, in which one-piece transfer lots are used. Key factors affecting lead time are found by analyzing the sum of the longest setup time of individual items among the shared processes （SLST） and the longest processing time of individual items among processes （LPT）. And lead time can be minimized by cutting down the SLST and LPT. Reduction of the SLST is described as a traveling salesman problem （TSP）, and the minimum of the SLST is solved through job shop scheduling. Removing the bottleneck and leveling the production line optimize the LPT. If the number of items produced is small, the routings are relatively short, and items and facilities are changed infrequently, the optimal schedule will remain valid. Finally a brief example serves to illustrate the method.

Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.

Verification of an operational ocean circulation-surface wave coupled forecasting system for the China＇s seas

An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.

Quantitative Comparison of Predictabilities of Warm and Cold Events Using the Backward Nonlinear Local Lyapunov Exponent Method

The backward nonlinear local Lyapunov exponent method(BNLLE)is applied to quantify the predictability of warm and cold events in the Lorenz model.Results show that the maximum prediction lead times of warm and cold events present obvious layered structures in phase space.The maximum prediction lead times of each warm(cold)event on individual circles concentric with the distribution of warm(cold)regime events are roughly the same,whereas the maximum prediction lead time of events on other circles are different.Statistical results show that warm events are more predictable than cold events.

Ordering Cost Depletion in Inventory Policy with Imperfect Products and Backorder Rebate

This study presents an inventory model for imperfect products with depletion in ordering costs and constant lead time where the price discount in the backorder is permitted.The imperfect products are refused or modified or if they reached to the customer,returned and thus some extra costs are experienced.Lately some of the researchers explicitly present on the significant association between size of lot and quality imperfection.In practical situations,the unsatisfied demands increase the period of lead time and decrease the backorders.To control customers'problems and losses,the supplier provides a price discount in backorders during shortages.Also,an order’s policies may result in including some imperfect products in arrival lots.A discount on price may be offered by the supplier on the out-of-stock products to manage the backorder problems.The study aims to develop a model with imperfect products by permitting the price discount in backorders,and the cost of ordering is considered a decision variable.First,it is assumed that the demand for lead time is followed by a normal distribution and then stops it and assumed that the first two moments of demand for lead time are known.Further,the minimax distribution method is used to solve this model,and a separate algorithm is designed.In this study,two models are discussed with and without a normally distributed rate of demand.The current study verified with the help of some numerical examples over various model parameters.

Prediction of Wintertime Northern Hemisphere Blocking by the NCEP Climate Forecast System

Daily output from the hindcasts by the NCEP Climate Forecast System version 2 （CFSv2） is analyzed to understand CFSv2＇s skill in forecasting wintertime atmospheric blocking in the Northern Hemisphere. Prediction skills of sector blocking, sector-blocking episodes, and blocking onset/decay are assessed with a focus on the Euro-Atlantic sector （20°W-45°E） and the Pacific sector （160°E 135°W）. Features of associated circulation and climate patterns are also examined. The CFSv2 well captures the observed features of longitudinal distribution of blocking activity, but underestimates blocking frequency and intensity and shows a decreasing trend in blocking frequency with increasing forecast lead time. Within 14-day lead time, the Euro-Atlantic sector blocking receives a higher skill than the Pacific sector blocking. Skillful forecast （taking the hit rate of 50~ as a criterion） can be obtained up to 9 days in the Euro-Atlantic sector, which is slightly longer than that in the Pacific sector （7 days）. The forecast skill of sector-blocking episodes is slightly lower than that of sector blocking in both sectors, and it is slightly higher in the Euro-Atlantic sector than in the Pacific sector. Compared to block onset, the skill for block decay is lower in the Euro-Atlantic sector, slightly higher in the Pacific sector during the early three days but lower after three days in lead time. In both the Euro-Atlantic and the Pacific sectors, a local dipole pattern in 500-hPa geopotential height associated with blocking is well presented in the CFSv2 prediction, but the wave-train like pattern that is far away from the blocking sector can only maintain in the forecast of relative short lead time. The CFSv2 well reproduces the observed characteristics of local temperature and precipitation anomalies associated with blocking.

A PERISHABLE INVENTORY SYSTEM WITH SERVICE FACILITIES, MAP ARRIVALS AND PH- SERVICE TIMES

We consider a continuous review perishable （s,S） inventory system with a service facility consisting of a waiting hall of finite capacity and a single server. We assume two types of customers, ordinary and negative, arrive according to a Markovian Arrival Process （MAP）. An ordinary customer joins the queue and a negative customer instead of joining the queue removes one ordinary customer from the queue. The removal rule adopted in this paper is RCE （removal of a customer from the end）. The individual customer＇s unit demand is satisfied after a random time of service which is assumed to have a phase-type distribution. The life time of each item and the lead time of the reorders have been assumed to be independent exponential distributions. The joint probability distribution of the number of customers in the system and the inventory level is obtained for the steady state case. Various stationary system performance measures are computed and the total expected cost rate is calculated. The results are illustrated numerically.

A supply chain model for imperfect production system with stochastic lead time demand

This study deals an integrated manufacturer-buyer supply chain system for imperfect production under stochastic lead time demand.Here,defective rate has been followed as a function of production rate.Also,the produced units have been inspected in order to screen the defective units but screening rate is less than the production rate and greater than the demand rate.Buyer purchases the products from the manufacturer.Also,we assume that shortage during the lead time is permitted and demand during the shortage period is fully backordered.The objective is to derive the optimal production rate,ordering quantity and to maximize joint total profit.Basically,two different models for different probability distribution functions of stochastic lead time demand have been developed.Some numerical examples are provided to show the applicability of the proposed models comparing the optimum average profits.Finally,sensitive analysis,conclusion and future researches are presented.

PLANNED LEAD TIME OPTIMIZATION IN MATERIAL REQUIREMENT PLANNING ENVIRONMENT FOR MULTILEVEL PRODUCTION SYSTEMS

This paper deals with the problem of planned lead time calculation in a Material Requirement Planning （MRP） environment under stochastic lead times. The objective is to minimize the sum of holding and backlogging costs. The proposed approach is based on discrete time inventory control where the decision variables are integer. Two types of systems are considered： multi-level serial-production and assembly systems. For the serial production systems （one type of component at each level）, a mathematical model is suggested. Then, it is proven that this model is equivalent to the well known discrete Newsboy Model. This directly provides the optimal values for the planned lead times. For multilevel assembly systems, a dedicated model is proposed and some properties of the decision variables and objective function are proven. These properties are used to calculate lower and upper limits on the decision variables and lower and upper bounds on the objective function. The obtained limits and bounds open the possibility to develop an efficient optimization algorithm using, for example, a Branch and Bound approach. The paper presents the proposed models in detail with corresponding proofs and se＇~eral numerical examples. Some advantages of the suggested models and perspectives of this research are discussed.

Optimal structure of joint inventory-pricing management with dual suppliers and different lead times

We consider a joint inventory-pricing control problem in a single-product,periodic-review,dual-supplier inventory system.The two suppliers have different lead times.One expedited supplier offers instantaneous replenishment,and one regular supplier requires an L-period lead time for delivery.The supply quantity is stochastic and the demand is price-dependent.For the expedited inventory replenishment,we characterize the optimal policy as a state-dependent almost-threshold policy by extending the stochastically linear in mid-point to a multidimensional setting.To investigate the optimal regular inventory replenishment and pricing policy,we propose the notions of partially stochastic translation(PST)and increasing partially stochastic translation(IPST),which help in obtaining the antimultimodularity preservation in dynamic programming problems.We provide properties,sufficient conditions,and examples for PST and IPST functions.By applying PST and IPST,we obtain the antimultimodularity of the profit functions.The antimultimodular profit functions ensure that the optimal regular ordering quantity and the optimal price are monotone in the current inventory level and outstanding order quantities.Moreover,we reveal that as the time interval increases,the effects of previous outstanding orders on the optimal regular ordering and pricing decisions are decreasing and increasing,respectively.PST and IPST also enable us to further characterize the optimal expedited ordering quantity as decreasing in the inventory level.However,the optimal expedited ordering quantity can be non-monotone with respect to the outstanding order quantities,as shown in the example.

Inflation and time value of money in a vendor-buyer inventory system with transportation cost and ordering cost reduction

The article presents a single-vendor and a single-buyer integrated production inventory system with inflation and the time value of money.The main focus is on the effect of ordering cost reduction dependent on lead time and lead time reduction.Two integrated continuous review models are developed.For both cases,our objective is to minimise the total integrated system cost by simultaneously optimising the order quantity,safety factor,lead time and number of shipments.We take the transportation cost as a function of the shipment lot size and it is taken to be in an all unit-discount cost format.Thus we incorporate transportation cost explicitly into the model and develop optimal solution procedure for solving the proposed inventory system.An efficient algorithm for finding the optimal solution is developed and numerical examples are given to illustrate the model.

Dynamic robust optimal reorder point with uncertain lead time and changeable demand distribution

In fixed order quantity systems,uncertainty in lead time is expressed as a set of scenarios with occurrence probabilities,and the mean and variance in demand distribution are supposed to be changeable according to a known pattern.A new concept of "dynamic robust optimal reorder point" is proposed in this paper and its value is calculated as a "robust optimal reorder point function with respect to reorder time".Two approaches were employed in determining the dynamic optimal reorder point.The first is a shortage rate satisfaction approach and the second is a backorder cost minimization approach.The former aims at finding the minimum value of reorder point at each reorder time which satisfies the condition that the cumulative distribution function (CDF) of shortage rate under a given set of scenarios in lead time is greater than or equal to a basic CDF of shortage rate predetermined by a decision-maker.In the latter approach,the CDF of closeness of reorder point is defined at each reorder time to express how close to the optimal reorder points under the set of scenarios,and the dynamic optimal reorder point is defined according to stochastic ordering.Some numerical examples demonstrate the features of these dynamic robust optimal reorder points.

A two-tier supply chain model under two distributions with MTTF,rework,variable production rate and lead time

This article considers the two-level supply chain model incorporating an imperfect production process under a variable lead time.The cost of producing a unit item is calculated as a function of the production rate.In addition,two alternative production functions(linear and quadratic functions)are used to relate process quality and production rate.Lead time demand follows two different distributions,based on which two mathematical formulations are described in this paper.In the first model,the lead time demand follows a normal distribution.In the second model,the lead time demand doesn’t follow any particular distribution,but the mean and the standard deviation are known.The lead time length is minimized by incorporating the lead time crashing cost.This research aims to analyze the optimized total cost of the supply chain under two different distributions.

The implications of blockchain-coordinated information sharing within a supply chain: A simulation study

The profitability of a supply chain(SC)is proportional to the stability of all its stakeholders as well as their consistent information sharing with an effective and efficient communication mechanism.Various inefficiencies,such as the bullwhip effect(BWE)and product unavailability,may be caused by a lack of coordination in an SC.The importance of sharing consumer demand has been quantified by comprehensive studies under the assumption that all SC participants will access the same information.However,only a few studies have studied the effect of minimal coordination or limited visibility of information while considering their effect on the overall efficiency of an SC.This work primarily leverages blockchain technology(BCT)to create a simulation model.To do this,an SC BWE-based model is initially developed.Following that,a blockchain-based robust information sharing system is simulated.Furthermore,information sharing is challenging,and SC stakeholders may not really trust each other and hence be reluctant to share sensitive information.Considering that,this paper propose an improved proof-ofauthority(PoA)consensus algorithm that will increase trust in a decentralized SC model.Multiple experiments are carried out to demonstrate the effectiveness of our approach,and the simulation results clearly demonstrate the effectiveness of information sharing in a supply chain via blockchain,as well as that trust between partners tends to increase overall SC efficiency and reduce BWE.

Numerical Simulations of an Advection Fog Event over Shanghai Pudong International Airport with the WRF Model

A series of numerical simulations is conducted to understand the formation, evolution, and dissipation of an advec- tion fog event over Shanghai Pudong International Airport （ZSPD） with the Weather Research and Forecasting （WRF） model. Using the current operational settings at the Meteorological Center of East China Air Traffic Manage- ment Bureau, the WRF model successfully predicts the fog event at ZSPD. Additional numerical experiments are per- formed to examine the physical processes associated with the fog event. The results indicate that prediction of this particular fog event is sensitive to microphysical schemes for the time of fog dissipation but not for the time of fog onset. The simulated timing of the arrival and dissipation of the fog, as well as the cloud distribution, is substantially sensitive to the planetary boundary layer and radiation （both longwave and shortwave） processes. Moreover, varying forecast lead times also produces different simulation results for the fog event regarding its onset and duration, sug- gesting a trade-off between more accurate initial conditions and a proper forecast lead time that allows model physi- cal processes to spin up adequately during the fog simulation. The overall outcomes from this study imply that the complexity of physical processes and their interactions within the WRF model during fog evolution and dissipation is a key area of future research.