scholarly journals Reducing lead-times and lead-time variance in cooperative distribution networks

2016 ◽  
Vol 8 (1) ◽  
pp. 51 ◽  
Author(s):  
Hadi Ghaderi ◽  
Wout Dullaert ◽  
Walther Ploos Van Amstel
Keyword(s):  
Lead Time ◽  
Distribution Networks ◽  
Lead Times ◽  
Time Variance

scholarly journals A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting

2021 ◽  
Vol 9 (4) ◽  
pp. 383
Author(s):  
Ting Yu ◽  
Jichao Wang
Keyword(s):  
Lead Time ◽  
Wave Period ◽  
Data Driven ◽  
Lead Times ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Mean Wave Period ◽  
Scattering Index ◽  
Gated Recurrent Unit ◽  
Methods Numerical

Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.

scholarly journals A Preliminary Look at the Social Perspective of Warn-on-Forecast: Preferred Tornado Warning Lead Time and the General Public’s Perceptions of Weather Risks

2011 ◽  
Vol 3 (2) ◽  
pp. 128-140 ◽  
Author(s):  
S. Hoekstra ◽  
K. Klockow ◽  
R. Riley ◽  
J. Brotzge ◽  
H. Brooks ◽  
...  
Keyword(s):  
Lead Time ◽  
Stated Preference ◽  
The United States ◽  
Public Understanding ◽  
Lead Times ◽  
Present System ◽  
The Public ◽  
Tornado Warnings ◽  
Tornado Warning ◽  
Weather Risks

Abstract Tornado warnings are currently issued an average of 13 min in advance of a tornado and are based on a warn-on-detection paradigm. However, computer model improvements may allow for a new warning paradigm, warn-on-forecast, to be established in the future. This would mean that tornado warnings could be issued one to two hours in advance, prior to storm initiation. In anticipation of the technological innovation, this study inquires whether the warn-on-forecast paradigm for tornado warnings may be preferred by the public (i.e., individuals and households). The authors sample is drawn from visitors to the National Weather Center in Norman, Oklahoma. During the summer and fall of 2009, surveys were distributed to 320 participants to assess their understanding and perception of weather risks and preferred tornado warning lead time. Responses were analyzed according to several different parameters including age, region of residency, educational level, number of children, and prior tornado experience. A majority of the respondents answered many of the weather risk questions correctly. They seemed to be familiar with tornado seasons; however, they were unaware of the relative number of fatalities caused by tornadoes and several additional weather phenomena each year in the United States. The preferred lead time was 34.3 min according to average survey responses. This suggests that while the general public may currently prefer a longer average lead time than the present system offers, the preference does not extend to the 1–2-h time frame theoretically offered by the warn-on-forecast system. When asked what they would do if given a 1-h lead time, respondents reported that taking shelter was a lesser priority than when given a 15-min lead time, and fleeing the area became a slightly more popular alternative. A majority of respondents also reported the situation would feel less life threatening if given a 1-h lead time. These results suggest that how the public responds to longer lead times may be complex and situationally dependent, and further study must be conducted to ascertain the users for whom the longer lead times would carry the most value. These results form the basis of an informative stated-preference approach to predicting public response to long (>1 h) warning lead times, using public understanding of the risks posed by severe weather events to contextualize lead-time demand.

scholarly journals Tornado Warnings, Lead Times, and Tornado Casualties: An Empirical Investigation

2008 ◽  
Vol 23 (2) ◽  
pp. 246-258 ◽  
Author(s):  
Kevin M. Simmons ◽  
Daniel Sutter
Keyword(s):  
United States ◽  
Lead Time ◽  
Empirical Investigation ◽  
Conventional Wisdom ◽  
Lead Times ◽  
Time Intervals ◽  
Consistent Effect ◽  
Tornado Warnings ◽  
The Relationship

Abstract Conventional wisdom holds that improved tornado warnings will reduce tornado casualties, because longer lead times on warnings provide extra opportunities to alert residents who can then take precautions. The relationship between warnings and casualties is examined using a dataset of tornadoes in the contiguous United States between 1986 and 2002. Two questions are examined: Does a warning issued on a tornado reduce the resulting number of fatalities and injuries? Do longer lead times reduce casualties? It is found that warnings have had a significant and consistent effect on tornado injuries, with a reduction of over 40% at some lead time intervals. The results for fatalities are mixed. An increase in lead time up to about 15 min reduces fatalities, while lead times longer than 15 min increase fatalities compared with no warning. The fatality results beyond 15 min, however, depend on five killer tornadoes and consequently are not robust.

HYDROLOGICAL DROUGHT FORECASTS USING MARKOV CHAINS AND REGRESSION MODELS (A CASE STUDY FOR NORTHWEST OF ALGERIA)

2021 ◽  
pp. 90-103
Author(s):  
A. RAHMOUNI ◽  
◽  
M. MEDDI ◽  
A. HAMOUDI SAAED ◽  
◽  
...  
Keyword(s):  
Lead Time ◽  
Robust Regression ◽  
Forecast Accuracy ◽  
Hydrological Drought ◽  
Lead Times ◽  
Chain Process ◽  
Markov Switching Model ◽  
North West ◽  
Switching Model ◽  
The North

An effective drought forecast is an important measure to mitigate some of its most damaging impacts. In this study we compare the effectiveness of two models: Markov Switching Model (MSM) and Robust Regression Model (RRM) with three different approaches to forecast hydrological drought events in the north-west of Algeria using Standardized Runoff Index (SRI). The validation of these models is carried out by hydro-climatic series of 41 stations for the period of 1968-2009. The values of SRI 3, SRI 6, and SRI 12 have been forecasted over lead times of 1 and 6 months. The performance of forecast results is measured using R2 and RMSE. For the lead time of 1 month, the results are quite similar for both models with slight superiority for the Markov chain process. The addition of the SPI or RDI indices as independent variables improves this performance for some stations while it decreases accuracy for other stations. However, forecast accuracy declines significantly as the lead time increases to 6 months particularly for regression results.

scholarly journals The effect of increased resolution of geostationary satellite imageries on predictability of tropical thunderstorms over Southeast Asia

2018 ◽  
Author(s):  
Kwonmin Lee ◽  
Hye-Sil Kim ◽  
Yong-Sang Choi
Keyword(s):  
High Resolution ◽  
Southeast Asia ◽  
Lead Time ◽  
Geostationary Satellite ◽  
Disaster Mitigation ◽  
Mature Stage ◽  
Lead Times ◽  
Low Resolution ◽  
High Resolution Imagery ◽  
Heavy Damage

Abstract. Tropical thunderstorms cause heavy damage to property and lives, and there is a strong interest in advancing the predictability of thunderstorms with more precise satellite observations. Using high-resolution (2 km and 10 minutes) imageries from the geostationary satellite (Himawari-8) recently launched over Southeast Asia, we examine how early the thunderstorms can be predicted compared to the low-resolution (4 km and 30 minutes) imageries of the former satellite. We compare the lead times for eight thunderstorms that occurred in August 2017 between high- and low-resolution imageries. These thunderstorms are identified by pixels with a brightness temperature at 10.45 μm (BT11) gradually decreasing by more than 5 K per 10 minutes (15 K per 30 minutes) compared to the previous imagery. The lead time is then calculated as the time passed from the initial to the mature stage of the thunderstorm signal, based on the time series of a minimum BT11 of these pixels. The lead time is found to be 100–180 minutes for the high-resolution imagery, while it is only found to be 30 minutes if detectable at all for the low-resolution imagery. This result suggests that the high-resolution imagery is essential for substantial disaster mitigation because of its ability to note an alarm more than two hours ahead of a matured thunderstorm.

scholarly journals Lead Times – Their Behavior and the Impact on Planning and Control in Supply Chains

2017 ◽  
Vol 8 (2) ◽  
pp. 30-40 ◽  
Author(s):  
Peter Nielsen ◽  
Zbigniew Michna ◽  
Brian Bruhn Sørensen ◽  
Ngoc Do Anh Dung
Keyword(s):  
Supply Chains ◽  
Lead Time ◽  
Large Impact ◽  
Lead Times ◽  
Stochastic Nature ◽  
Planning And Control ◽  
And Control ◽  
The Impact ◽  
Independent And Identically Distributed

AbstractLead times and their nature have received limited interest in literature despite their large impact on the performance and the management of supply chains. This paper presents a method and a case implementation of the same, to establish the behavior of real lead times in supply chains. The paper explores the behavior of lead times and illustrates how in one particular case they can and should be considered to be independent and identically distributed (i.i.d.). The conclusion is also that the stochastic nature of the lead times contributes more to lead time demand variance than demand variance.

scholarly journals Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems

2021 ◽  
Vol 13 (21) ◽  
pp. 4459
Author(s):  
Aline Falck ◽  
Javier Tomasella ◽  
Fabrice Papa
Keyword(s):  
Data Collection ◽  
Lead Time ◽  
Flood Forecasting ◽  
Lead Times ◽  
Latency Time ◽  
Distributed Hydrological Model ◽  
Satellite Altimeter ◽  
Operational Characteristics ◽  
Potential Benefits ◽  
Forecasting System

This study investigates the potential of observations with improved frequency and latency time of upcoming altimetry missions on the accuracy of flood forecasting and early warnings. To achieve this, we assessed the skill of the forecasts of a distributed hydrological model by assimilating different historical discharge time frequencies and latencies in a framework that mimics an operational forecast system, using the European Ensemble Forecasting system as the forcing. Numerical experiments were performed in 22 sub-basins of the Tocantins-Araguaia Basin. Forecast skills were evaluated in terms of the Relative Operational Characteristics (ROC) as a function of the drainage area and the forecasts’ lead time. The results showed that increasing the frequency of data collection and reducing the latency time (especially 1 d update and low latency) had a significant impact on steep headwater sub-basins, where floods are usually more destructive. In larger basins, although the increased frequency of data collection improved the accuracy of the forecasts, the potential benefits were limited to the earlier lead times.

scholarly journals Skill of Rain–Snow Level Forecasts for Landfalling Atmospheric Rivers: A Multimodel Assessment Using California’s Network of Vertically Profiling Radars

2020 ◽  
Vol 21 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Brian Henn ◽  
Rachel Weihs ◽  
Andrew C. Martin ◽  
F. Martin Ralph ◽  
Tashiana Osborne
Keyword(s):  
Sierra Nevada ◽  
Lead Time ◽  
Flood Hazard ◽  
Critical Factor ◽  
Forecast Model ◽  
Lead Times ◽  
Grid Spacing ◽  
Atmospheric Rivers ◽  
Operational Forecasts ◽  
Resources Planning

AbstractThe partitioning of rain and snow during atmospheric river (AR) storms is a critical factor in flood forecasting, water resources planning, and reservoir operations. Forecasts of atmospheric rain–snow levels from December 2016 to March 2017, a period of active AR landfalls, are evaluated using 19 profiling radars in California. Three forecast model products are assessed: a global forecast model downscaled to 3-km grid spacing, 4-km river forecast center operational forecasts, and 50-km global ensemble reforecasts. Model forecasts of the rain–snow level are compared with observations of rain–snow melting-level brightband heights. Models produce mean bias magnitudes of less than 200 m across a range of forecast lead times. Error magnitudes increase with lead time and are similar between models, averaging 342 m for lead times of 24 h or less and growing to 700–800 m for lead times of greater than 144 h. Observed extremes in the rain–snow level are underestimated, particularly for warmer events, and the magnitude of errors increases with rain–snow level. Storms with high rain–snow levels are correlated with larger observed precipitation rates in Sierra Nevada watersheds. Flood risk increases with rain–snow levels, not only because a greater fraction of the watershed receives rain, but also because warmer storms carry greater water vapor and thus can produce heavier precipitation. The uncertainty of flood forecasts grows nonlinearly with the rain–snow level for these reasons as well. High rain–snow level ARs are a major flood hazard in California and are projected to be more prevalent with climate warming.

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