The effect of day-ahead weather forecast uncertainty on power lines’ sag in DLR models

Author(s):  
Levente Racz ◽  
David Szabo ◽  
Gabor Gocsei ◽  
Balint Nemeth
2018 ◽  
pp. 95-105 ◽  
Author(s):  
Sean Ernst ◽  
Daphne LaDue ◽  
Alan Gerard

For Emergency Managers (EMs), preparations for severe weather have always relied on accurate, well-communicated National Weather Service (NWS) forecasts. As part of their constant work to improve these forecasts, the NWS has recently begun to develop impact-based products that share forecast uncertainty information with EMs, including the Probabilistic Hazard Information (PHI) tool. However, there is a lack of research investigating what forecast uncertainty information EMs understand, and what information needs exist in the current communication paradigm. This study used the Critical Incident Technique to identify themes from incidents involving weather forecast information that went well, or not so well, from the perspective of the EMs responding to them. In total, 11 EMs from a variety of locales east of the Rockies were interviewed—six of whom were county-level, two city, two state, and one from a school district. We found that EMs sought increased forecast detail as a potential event approached in time and built relational trust in the NWS through repeated interactions. EMs had difficulty preparing for events when they did not have details of the expected impacts, or the likelihood of those impacts, for their regions. In summary, EMs are already starting to work in an uncertainty-friendly frame and could be responsive to the impact details and increased forecaster relations proposed with the PHI tool.


2016 ◽  
Vol 24 (1) ◽  
pp. 18-28 ◽  
Author(s):  
Rafal Kicinger ◽  
Jit-Tat Chen ◽  
Matthias Steiner ◽  
James Pinto

2016 ◽  
Vol 142 (698) ◽  
pp. 2102-2118 ◽  
Author(s):  
J. G. McLay ◽  
C. A. Reynolds ◽  
E. Satterfield ◽  
D. Hodyss

2008 ◽  
Vol 23 (5) ◽  
pp. 974-991 ◽  
Author(s):  
Rebecca E. Morss ◽  
Julie L. Demuth ◽  
Jeffrey K. Lazo

Abstract Weather forecasts are inherently uncertain, and meteorologists have information about weather forecast uncertainty that is not readily available to most forecast users. Yet effectively communicating forecast uncertainty to nonmeteorologists remains challenging. Improving forecast uncertainty communication requires research-based knowledge that can inform decisions on what uncertainty information to communicate, when, and how to do so. To help build such knowledge, this article explores the public’s perspectives on everyday weather forecast uncertainty and uncertainty information using results from a nationwide survey. By contributing to the fundamental understanding of laypeople’s views on forecast uncertainty, the findings can inform both uncertainty communication and related research. The article uses empirical data from a nationwide survey of the U.S. public to investigate beliefs commonly held among meteorologists and to explore new topics. The results show that when given a deterministic temperature forecast, most respondents expected the temperature to fall within a range around the predicted value. In other words, most people inferred uncertainty into the deterministic forecast. People’s preferences for deterministic versus nondeterministic forecasts were examined in two situations; in both, a significant majority of respondents liked weather forecasts that expressed uncertainty, and many preferred such forecasts to single-valued forecasts. The article also discusses people’s confidence in different types of forecasts, their interpretations of the probability of precipitation forecasts, and their preferences for how forecast uncertainty is conveyed. Further empirical research is needed to study the article’s findings in other contexts and to continue exploring perception, interpretation, communication, and use of weather forecast uncertainty.


2011 ◽  
Vol 11 (9) ◽  
pp. 2419-2431 ◽  
Author(s):  
P. Bonelli ◽  
M. Lacavalla ◽  
P. Marcacci ◽  
G. Mariani ◽  
G. Stella

Abstract. Wet snow icing accretion on power lines is a real problem in Italy, causing failures on high and medium voltage power supplies during the cold season. The phenomenon is a process in which many large and local scale variables contribute in a complex way and not completely understood. A numerical weather forecast can be used to select areas where wet snow accretion has an high probability of occurring, but a specific accretion model must also be used to estimate the load of an ice sleeve and its hazard. All the information must be carefully selected and shown to the electric grid operator in order to warn him promptly. The authors describe a prototype of forecast and alert system, WOLF (Wet snow Overload aLert and Forecast), developed and applied in Italy. The prototype elaborates the output of a numerical weather prediction model, as temperature, precipitation, wind intensity and direction, to determine the areas of potential risk for the power lines. Then an accretion model computes the ice sleeves' load for different conductor diameters. The highest values are selected and displayed on a WEB-GIS application principally devoted to the electric operator, but also to more expert users. Some experimental field campaigns have been conducted to better parameterize the accretion model. Comparisons between real accidents and forecasted icing conditions are presented and discussed.


2020 ◽  
Vol 77 (2) ◽  
pp. 787-793
Author(s):  
Y. Qiang Sun ◽  
Fuqing Zhang ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Jan-Huey Chen ◽  
...  

Abstract In their comment, Žagar and Szunyogh raised concerns about a recent study by Zhang et al. that examined the predictability limit of midlatitude weather using two up-to-date global models. Zhang et al. showed that deterministic weather forecast may, at best, be extended by 5 days, assuming we could achieve minimal initial-condition uncertainty (e.g., 10% of current operational value) with a nearly perfect model. Žagar and Szunyogh questioned the methodology and the experiments of Zhang et al. Specifically, Žagar and Szunyogh raised issues regarding the effects of model error on the growth of the forecast uncertainty. They also suggested that estimates of the predictability limit could be obtained using a simple parametric model. This reply clarifies the misunderstandings in Žagar and Szunyogh and demonstrates that experiments conducted by Zhang et al. are reasonable. In our view, the model error concern in Žagar and Szunyogh does not apply to the intrinsic predictability limit, which is the key focus of Zhang et al. and the simple parametric model described in Žagar and Szunyogh does not serve the purpose of Zhang et al.


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