Maximum Lead Time Research Articles

An assessment of model capability on rapid intensification prediction of tropical cyclones in the South China Sea

The absence of robust quantitative evaluation methods has led to insufficient knowledge of models capability on the rapid intensification (RI) prediction of tropical cyclones (TCs). In this study, we propose a method and define some indicators aiming to evaluate model capability on predicting RI in a more accurate manner. An assessment of model predictive capability on RI of TCs based on 10 years of operational forecasts has been conducted using different RI criteria. The Tropical Regional Atmosphere Model for the South China Sea of China Meteorological Administration (CMA-TRAMS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) high resolution (HRES) operational forecasts were used. Analysis results revealed that the criterion of 6-hour sea level pressure (SLP) change is more appropriate to be used in RI operational forecast. The maximum lead time (MLT) of CMA-TRAMS and HRES was 72 and 78 h, and the maximum deviation of RI occurrence time of CMA-TRAMS and HRES was 48 h delay and 24 h ahead, respectively. Overall results suggest that the model predictive capability of RI is currently limited, and both models have inadequate capability in providing sufficient heat and energy to support RI in the long run. A tendency of CMA-TRAMS to have a lag in RI occurrence time was also demonstrated due to an air-sea interaction lag resulting from the fixed skin sea surface temperature used. Results of the present study provide insights and could be the basis for future efforts on improving parametrization schemes for properly describing RI process of TCs.

Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning

Abstract. Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 h), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 h. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (≤ 5 mm h−1), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 h) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.

Experimental evaluation and real-time forecast of smoke propagation in tunnels under intervention of sub-critical longitudinal ventilation

Sub-critical longitudinal ventilation can restrict the back-layering length in the upstream region and maintain suitable smoke stratification in the downstream region of a tunnel fire. In this study, experiments using a 1/15 scale, 80-m long, 1.2-m wide, and 0.5-m high model tunnel were conducted to investigate the effects of sub-critical longitudinal ventilation velocity and heat release rate (HRR) on the key parameters describing smoke propagation. The smoke front temperature near the fire source decreased significantly as the longitudinal ventilation velocity increased. The longitudinal ventilation velocity and HRR were incorporated into a regression model describing the smoke front velocity, which was applied to the evaluated fire scenarios using an ensemble Kalman filter to account for intervention of sub-critical longitudinal ventilation. The smoke front temperatures at discrete locations were assimilated to estimate the heat exchange correction coefficient and air entrainment ratio of smoke front in real-time and dynamically correct the forecasted smoke front temperature and velocity accordingly. When sub-critical longitudinal ventilation intervened, the smoke front parameters could be forecasted with a maximum lead time of 60 s and an average error within 5%. Therefore, the proposed forecasting method can inform the selection of disposal measures in early tunnel fire emergencies.

An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.

Comparison of Three Radar-Based Precipitation Nowcasts for the Extreme July 2021 Flooding Event in Germany

Abstract Quantitative precipitation nowcasts (QPN) can improve the accuracy of flood forecasts, especially for lead times up to 12 h, but their evaluation depends on a variety of factors, namely, the choice of the hydrological model and the benchmark. We tested three precipitation nowcasting techniques based on radar observations for the disastrous mid-July 2021 event in seven German catchments (140–1670 km2). Two deterministic [advection-based and spectral prognosis (S-PROG)] and one probabilistic [Short-Term Ensemble Prediction System (STEPS)] QPN with a maximum lead time of 3 h were used as input to two hydrological models: a physically based, 3D-distributed model (ParFlowCLM) and a conceptual, lumped model (GR4H). We quantified the hydrological added value of QPN compared with hydrological persistence and zero-precipitation nowcasts as benchmarks. For the 14 July 2021 event, we obtained the following key results. 1) According to the quality of the forecasted hydrographs, exploiting QPN improved the lead times by up to 4 h (8 h) compared with adopting zero-precipitation nowcasts (hydrological persistence) as a benchmark. Using a skill-based approach, obtained improvements were up to 7–12 h depending on the benchmark. 2) The three QPN techniques obtained similar performances regardless of the applied hydrological model. 3) Using zero-precipitation nowcasts instead of hydrological persistence as benchmark reduced the added value of QPN. These results highlight the need for combining a skill-based approach with an analysis of the quality of forecasted hydrographs to rigorously estimate the added value of QPN.

Long-term probabilistic streamflow forecast model with “inputs–structure–parameters” hierarchical optimization framework

Accurate, reliable, and stable streamflow forecasts are essential for risk assessment and decision making in water resources management. Owing to the limited value of deterministic forecasting, probabilistic forecasting incorporating probability and confidence intervals is achieved by optimizing the predictors and forecast equation while identifying forecast error features. The combination of deterministic forecasts and error identification can facilitate water resources management. This study improved the traditional forecast model’s inputs selection, structure designation, and error parameters calibration to propose a long-term probabilistic streamflow forecast model with hierarchical optimization of the predictors, forecast equation, and errors characteristics. The model develops information entropy theory to screen the driving predictor set, the long short-term memory (LSTM) model to conduct deterministic forecasts, and the generalized autoregressive conditional heteroskedasticity (GARCH) model to identify time-varying errors. The proposed model, used in some case studies to forecast the monthly streamflow of two lakes, Hongze and Luoma lakes in China, was evaluated using the multidimensional index method considering “accuracy–reliability–stability” performance. The results revealed the following: (1) Predictor screening based on information entropy investigates the statistical characteristics of predictors, thereby improving the reliability of streamflow forecasts. (2) The LSTM model exploits the response between the driving predictors and streamflow, whereas GARCH model identifies the time-varying features of forecast errors effectively, which reduces the probability of forecast failure and increases the accuracy and stability of probabilistic forecasts. (3) Under current meteorological observation conditions and forecasting capability, the proposed forecasts can extend the maximum forecast lead time from 1 month to 3 months. (4) The proposed model improves the accuracy (root mean square error: 6.7%–34.8%), reliability (Brier score: 15.3%–27.9%), and stability (mistaken distance: 36.4–52.6%) in comparison with the benchmark of the two case studies, indicating that “inputs–structure–parameters” hierarchical optimization provides effective forecast information for water resources management.

Strong surface winds in Storm Eunice. Part 1: storm overview and indications of sting jet activity from observations and model data

Strong surface winds in Storm <i>Eunice</i>. Part 1: storm overview and indications of sting jet activity from observations and model data

Real-time flashover prediction model for multi-compartment building structures using attention based recurrent neural networks

This paper presents the development of an attention based bi-directional gated recurrent unit model, P-Flashv2, for the prediction of potential occurrence of flashover in a traditional 111 m2 single story ranch-style family home. Synthetic temperature data for more than 110 000 fire cases with a wide range of fire and vent opening conditions are collected. Temperature limit to heat detectors is applied to mimic the loss of temperature data in real fire scenarios. P-Flashv2 is shown to be able to make predictions with a maximum lead time of 60 s and its performance is benchmarked against eight different model architectures. Results show that P-Flashv2 has an overall accuracy of ∼ 87.7 % and ∼ 89.5% for flashover predictions with a lead time setting of 30 s and 60 s, respectively. Additional model testing is conducted to assess P-Flashv2 prediction capability in real fire scenarios. Evaluating the model again with full-scale experimental data, P-Flashv2 has an overall prediction accuracy of ∼ 82.7 % and ∼ 85.6 % for cases with the lead time of setting 30 s and 60 s, respectively. Results from this study show that the proposed machine learning based model, P-Flashv2, can be used to facilitate data-driven fire fighting and reduce fire fighter deaths and injuries.

Deep generation network for multivariate spatio-temporal data based on separated attention

The multivariate spatio-temporal data contains complex spatio-temporal background and channel coupling information. Effective extraction of these features is crucial for data generation. In this paper, a separated multiple attention network is proposed, which can capture the correlation of multiple types of variables in the same space-time, different spaces at the same time, and different times in the same space. Meanwhile, a new multiscale loss processing method based on homoscedasticity uncertainty and the assumption of gaussian loss distribution is proposed to balance the numerical scale of each channel loss in training. The experiment shows that our method has better performance and robustness than the classical machine learning model and higher computational efficiency than the physical model. It can generate multivariate intermittent spatial-temporal fields with a maximum lead time of 3 days and multivariate continuous spatial-temporal fields with a maximum lead time of 7 days.

Replenishment strategies for lost sales inventory systems of perishables under demand and lead time uncertainty

We develop an inventory control policy for perishable products considering both random demand and random lead time. We consider a B2C retail environment where excess demand is lost. The policy dynamically determines the optimal replenishment quantity under a service level constraint in every period, allowing for order-crossing, a widely disregarded characteristic in the literature. Regarding perishability, we compare the two most extreme issuing policies, first-expired-first-out (FEFO) and last-expired-first-out (LEFO), and evaluate our policy to existing inventory policies for perishables that typically ignore lead time uncertainty.We obtain several interesting findings. First, we show that ignoring lead time uncertainty and planning based on the expected lead time significantly undershoots the target service level. Even planning with the maximum lead time, under LEFO, the achieved service level would still fall considerably below the target, which the lost-sales structure can explain. On the other hand, under FEFO, the achieved service level would overshoot the target service level, which leads to unnecessary waste. Second, a more reliable lead time can significantly reduce waste, especially under LEFO. Third, our model allows us to distinguish between past, present, and future lead time uncertainty and thus to consider partial lead time information. We show the value of lead time information on outstanding orders. Fourth, we evaluate the impact of a fast but unreliable delivery option and a slow but reliable delivery option on the retailer’s average waste and ordering process. We find that the optimal choice depends on the demand characteristics.

Research on Time Inventory Control Model of 3PL Warehousing of Furniture Products under Uncertain Lead Time

3PL warehousing has become an important link between suppliers and retailers in the cross-border e-commerce supply chain. During the COVID-19, product shortage became one of the biggest concerns with the overseas 3PL warehouse integration using ocean shipment. Therefore it has become the focus of the cross-border e-commerce industry and research. Due to the large volume and low-value density, furniture products are generally replenished by ocean transportation. The shortage of such products can be caused by ocean transportation which brings uncertain arrival times and difficult inventory control. This paper, considering the uncertain lead time, uncertain demand, and 3PL warehouse free storage period, introduces a replenishment inventory control model. Utilizing the mathematical dynamic simulation model, it takes refrigerators as a product example into the calculations to prove the validity and accuracy. The experimental results demonstrate that the warehouse free-storage period is a vital factor affecting inventory control for cross-border e-commerce enterprises. When the lead time is uncertain, it is the most optimized solution to use the maximum lead time to determine the maximum inventory.

Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.

Возможность прогнозирования стока рек России методом экстраполяции гидрографа в зависимости от характеристик их водосборов

The predictability of river runoff is determined by the maximum lead time of satisfactory forecasts of water discharge obtained by the hydrograph extrapolation method. This indicator characterizes the smoothness of changes in water discharge over time and determines a possibility of using the Hydrometcentre of Russia’s automated system for preparation and daily streamflow forecasting all year long. The dependency between the predictability of river runoff and the main factors of its formation and regime is investigated. In total 18 regions within the territory of Russia are identified; for each of them a dependence between the streamflow predictability indicator and the area and average slope of the catchment is obtained. These regions cover 79% of the entire country. Calculated regional dependencies made it possible to estimate threshold values of the area and average slope of the catchment beyond which satisfactory forecasts are possible with a sufficiently long lead time (8–10 days), or only with a short lead time (1–2 days), or are impossible at all. Keywords: streamflow predictability, hydrograph extrapolation method, maximum forecast lead time, morphometric characteristics of catchment, calculated regional dependencies

Прогнозирование стока рек России методом экстраполяции гидрографа

Forecasting the flow of Russian rivers by hydrograph extrapolation / Borsch S.V., Koliy V.M., Semenova N.K., Simonov Yu.A., Khristoforov A.V. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 77-94. An automated system has been developed based on the hydrograph extrapolation method, which allows the year-round daily forecasting of water level and streamflow for the Russian rivers with up to 10-day lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast issue and five previous days. The forecasting scheme limits the possible minimum and maximum values of the discharge or water level based on historical data. Forecast schemes were obtained for 2776 river gauges. The time period from 2010 to 2019 with daily observations of discharge and water level was used. The forecast verification shows that this method can be successfully applied to large rivers with smooth hydrographs. Keywords: daily discharge and water levels, short- and medium-term forecasts, hydrograph extrapolation method, forecast verification, maximum lead time of satisfactory forecasts, self-learning of an automated system for preparing and issuing forecasts

Methods for optimizing the delivery of goods to consumers by several vehicles

Freight transport is one of the most important sectors of the national economy. The increase in the efficiency of the logistics company is ensured by the accurate organization of supplies, as well as the timely delivery of goods to the points of the route. That is why there is a need to optimize the delivery of goods. A delivery task is a transport task of delivering oversized cargo from a distribution center to a multitude of recipients located in the area of operation of a transport company. It is a generalization of the well-known traveling salesman problem, from which it differs in the condition of the limited carrying capacity of the vehicle used and, as a consequence, the need to repeatedly return to the base to replenish the transported cargo. This article proposes to supplement the traditional formulation of the problem with the requirement to distribute customers among several simultaneously operating vehicles (TC) so that the maximum lead time is minimal. This takes into account not only the interests of the delivery contractor, but also the customers. The solution to the problem consists of two stages. On the first one, in some known way, rational ring routes for each vehicle are determined, minimizing the total mileage. Based on the results of the stage, the time for passing each route is calculated. At the second stage, the problem of reducing the maximum travel time of routes is solved by using several vehicles delivering at the same time. To do this, it is necessary to optimally distribute vehicles along individual routes. It is proposed to use the algorithm for solving the well-known problem "Packing items into containers" to solve this problem. This problem belongs to the class of NP-hard problems in the strong sense and does not have an exact solution algorithm for arbitrary input data. This paper proposes a complex solution method combining the well-known First fitted (FF) algorithm, the original First fitted with reordering (FFR) algorithm, and S. Martello's lower bounds and P. Thoth to control the optimality of the obtained solution. Test calculations have shown the effectiveness of this approach for a moderate dimension of the problem.

Feasibility study of earthquake early warning in Tehran, Iran

In this study, we examine the scientific feasibility of an Earthquake Early Warning System in Tehran, Iran, by the integration of the Tehran Disaster Mitigation and Management Organization (TDMMO) accelerometric network and the PRobabilistic and Evolutionary early warning SysTem (PRESTo). To evaluate the performance of the TDMMO-PRESTo system in providing the reliable estimations of earthquake parameters and the available lead-times for The Metropolis of Tehran, two different approaches were analyzed in this work. The first approach was assessed by applying the PRESTo algorithms on waveforms from 11 moderate instrumental earthquakes that occurred in the vicinity of Tehran during the period 2009–2020. Moreover, we conducted a simulation analysis using synthetic waveforms of 10 large historical earthquakes that occurred in the vicinity of Tehran. We demonstrated that the six worst-case earthquake scenarios can be considered for The Metropolis of Tehran, which are mostly related to the historical and instrumental events that occurred in the southern, eastern, and western parts of Tehran. Our results indicate that the TDMMO-PRESTo system could provide reliable and sufficient lead-times of about 1 to 15s and maximum lead-times of about 20s for civil protection purposes in The Metropolis of Tehran.

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.

Deterministic prediction of stratospheric sudden warming events in the Global/Regional Integrated Model system (GRIMs)

The boreal-winter stratospheric sudden warming (SSW) events and their prediction skills by an operational numerical weather prediction model are examined by applying the Global/Regional Integrated Model system (GRIMs) for 18 SSW events from 1980–2012. Based on the mean squared skill score of the 10-hPa geopotential height field, which considers the SSW spatial structure, most SSW events are predicted with a maximum forecast lead time of approximately 15 days. The vortex-displacement SSW events are slightly better predicted than the vortex-split SSW events, and the predictions are improved during El Nino or easterly quasi-biennial oscillation winters. However, the skill difference in vortex morphology and background state is statistically insignificant. The decomposition of model errors into zonal-mean and eddy errors reveals that the model errors mostly result from eddy components. In particular, eddy-amplitude errors, which originate from a misrepresentation of the planetary-scale wave amplitude (i.e., polar vortex strength), play an important role in determining the SSW prediction skill with a non-negligible contribution from eddy-phase errors. These errors are mostly caused by the misdetection of short-term wave activities immediately before the SSW onset. Furthermore, an improved SSW prediction through well-represented planetary-scale wave activities is related to an improved prediction in the troposphere on time scales of 10 days and longer. This result confirms that better representation of the stratosphere could lead to improved subseasonal-to-seasonal predictions in the northern extratropics.

Stratospheric Initial Condition for Skillful Surface Prediction in the ECMWF Model

AbstractThe subseasonal‐to‐seasonal prediction of boreal‐winter sea‐level pressure (SLP) is investigated by examining 19‐year reforecast data sets from the European Centre for Medium‐Range Weather Forecasts ensemble prediction system. Based on a pattern anomaly correlation coefficient of 0.6, the model reliably predicted the extratropical SLP anomalies with a maximum forecast lead time of 9.8 days. This prediction skill, however, varies significantly from one forecast to another. The forecasts with relatively high skills and those with low skills exhibit significant zonal Wavenumber 1 patterns in the stratospheric initial conditions with an opposite sign. The high‐skill forecasts present a relatively weak westward Wavenumber 1 phase tilt with height compared with the low‐skill forecasts, dampening an upward wave propagation in the stratosphere. This result suggests that not only the zonal mean but also the zonally asymmetric stratospheric conditions play an important role in extended surface prediction by modulating vertical wave propagation or wave reflection.

Integrated Scheduling of Production and Two-Stage Delivery of Make-to-Order Products: Offline and Online Algorithms

We study integrated production- and delivery-scheduling problems that arise in practical make-to-order settings in several industries. In these problems, make-to-order products are first processed in a plant and then delivered to customer sites through two stages of shipping: first, from the plant to a pool point (e.g., a port, a distribution, or a consolidation center) and, second, from the pool point to customer sites. The objective is to obtain a joint schedule of job processing at the plant and two-stage shipping of completed jobs to customer sites to optimize a performance measure that takes into account both delivery timeliness and total transportation costs. We consider two problems in which delivery timeliness is measured by total or maximum lead time of the jobs and study both offline and online versions of these problems. For the offline problems involving a single production line at the plant, we provide optimal dynamic programming algorithms. For the more general offline problems involving multiple production lines at the plant, we propose fast heuristics and analyze their worst-case and asymptotic performances. For the online problems, we propose online algorithms and analyze their competitive ratios. By comparing our offline heuristics with lower bounds using randomly generated test instances, it is shown that these heuristics are capable of generating near-optimal solutions quickly. Using real data from Baosteel’s Meishan plant, we also show that our corresponding offline heuristic generates significantly better solutions than Baosteel’s rule-based approach. In addition, our computational results on the performance of the online algorithms relative to the offline heuristics generate important methodological insights that can be used by practitioners in choosing a specific solution approach.