scholarly journals An Experiment to Measure the Value of Statistical Probability Forecasts for Airports

2007 ◽  
Vol 22 (4) ◽  
pp. 928-935 ◽  
Author(s):  
Ross Keith ◽  
Stephen M. Leyton
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Operating Costs ◽  
False Alarms ◽  
Decision Making Process ◽  
Decision Threshold ◽  
Probabilistic Forecasts ◽  
Adverse Weather ◽  
Additional Fuel ◽  
American Airlines

Abstract The economic value of weather forecasts for airports for commercial aviation is investigated by introducing financial data into the decision-making process for fuel carriage by aircraft. Using specific operating costs for a given flight, an optimal decision probability threshold can be calculated that identifies whether that flight should carry extra fuel, in case of adverse weather conditions and subsequent diversion. Forecasts of these adverse conditions can then be applied to a critical threshold to make a real-time decision regarding the carriage of additional fuel. This study focuses on forecasts of low ceiling and/or reduced visibility and their corresponding impact on forecast value for flights arriving at three major airports in the United States. Eighteen daily flights by American Airlines were examined during a 14-month period, a total of approximately 7500 flights. Using operating cost data from this period, a critical decision threshold was derived for each daily flight. Two sets of forecasts, statistically derived probabilistic forecasts and National Weather Service terminal aerodrome forecasts (TAFs), were then applied to each flight’s fuel carriage decision-making process. The probabilistic forecasts, which utilize regional surface observations, were generated for the destination airport with a lead time appropriate to the airline’s flight planning time. If the forecast probability of adverse weather was greater than the critical decision threshold for a given flight, then additional fuel was deemed necessary for that flight. The categorical TAFs that corresponded timewise to the developed probabilistic forecasts were obtained for each location. For this study, a categorical “yes” forecast denotes the expectation that the visibility and/or cloud ceiling conditions are such that extra fuel is required, while a categorical “no” forecast does not require extra fuel. The analysis presented herein indicates that by using statistical, probabilistic forecasts rather than categorical forecasts, a significant saving is made in operating costs. This is probably because of a more optimal balance between false alarms and misses for each flight, rather than more “accurate” forecasts per se. This is the mechanism by which probabilistic forecasts create value, rather than increasing the number of hits and correct rejections and/or decreasing the number of false alarms and misses. For each of the flights investigated in this study, the total cost of using probabilistic forecasts was less than that of using TAFs. An average of $23,000 is saved per flight during this 14-month period. Projecting these figures over all American Airlines flights, a potential annual savings of approximately $50 million in operating costs would be realized by using probabilistic forecasts of adverse landing weather conditions instead of the traditional TAF.

scholarly journals Weather conditions during a transatlantic flight of Lituanica on July 15–17, 1933

Baltica ◽  
2014 ◽  
Vol 27 (2) ◽  
pp. 119-130 ◽  
Author(s):  
Gražina Sviderskytė ◽  
Gintautas Stankūnavičius ◽  
Egidijus Rimkus
Keyword(s):  
Decision Making ◽  
New York ◽  
Weather Conditions ◽  
Vital Role ◽  
Flight Path ◽  
Decision Making Process ◽  
Fatal Crash ◽  
Forced Landing ◽  
Adverse Weather ◽  
Three Stages

Abstract This article focuses on the 1933 transatlantic flight of the airplane Lituanica and weather conditions en-route. Using reanalysis methods and comparative analysis of historiographical data, the authors aimed to restore the weather conditions and to evaluate pilots’ decision-making process in rapidly changing situation during a flight from New York to Kaunas. In this study, the apparent flight path of Lituanica (actual flight path remains undocumented) was divided into three stages, with weather conditions investigated for each segment. The findings suggest that weather-based decision making was essential throughout most of the flight and could have played a vital role in the final stage. Over the European mainland, deteriorated weather conditions became unfavourable to maintaining the heading to Lithuania. The adverse weather had forced pilots to abandon their flight plan and consequently led to an attempted forced landing and the fatal crash in Germany.

scholarly journals Decision making in adverse weather conditions

2016 ◽  
Vol 9 (5) ◽  
Author(s):  
Gustavo Rodrigues de Oliveira Silva ◽  
Humberto César Machado
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Adverse Weather

Relating Flight Experience and Pilots' Perceptions of Decision-Making Skill

2002 ◽  
Vol 46 (1) ◽  
pp. 81-85 ◽  
Author(s):  
Juliana Goh ◽  
Douglas A. Wiegmann
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Flight Path ◽  
General Aviation ◽  
Flight Training ◽  
Decision Making Process ◽  
Flight Experience ◽  
Underlying Causes

Relationships between flight experience and pilots' perceptions of their ability to perform various aspects of the decision-making process were examined in the present study. Pilots were asked to rate how good they were, compared to the average General Aviation pilot, at monitoring, recognizing, diagnosing, generating solutions and implementing solutions when encountering flight path deviations, changes in weather conditions, mechanical malfunctions and conflicting traffic. Numerous measures of flight experience were collected. Results indicate that more experienced pilots felt that they were better at recognizing problems and implementing solutions, however, they did not necessarily feel more confident in their abilities to diagnosis the underlying causes of the problems. The results have implications for aeronautical decision making theories in general, and the design of flight training curricula in particular.

scholarly journals Perceptions, Uses, and Interpretations of Uncertainty in Current Weather Forecasts by Spanish Undergraduate Students

2021 ◽  
Vol 13 (1) ◽  
pp. 83-94
Author(s):  
I. Gómez ◽  
S. Molina ◽  
J. Olcina ◽  
J. J. Galiana-Merino
Keyword(s):  
Decision Making ◽  
Wind Speed ◽  
Undergraduate Students ◽  
Protective Action ◽  
Precipitation Amount ◽  
Weather Conditions ◽  
Uncertainty Range ◽  
Temperature Forecast ◽  
Probabilistic Forecasts ◽  
Precautionary Action

AbstractThis quantitative study evaluates how 71 Spanish undergraduate students perceive and interpret the uncertainty inherent to deterministic forecasts. It is based on several questions that asked participants what they expect given a forecast presented under the deterministic paradigm for a specific lead time and a particular weather parameter. In this regard, both normal and extreme weather conditions were studied. Students’ responses to the temperature forecast as it is usually presented in the media expect an uncertainty range of ±1°–2°C. For wind speed, uncertainty shows a deviation of ±5–10 km h−1, and the uncertainty range assigned to the precipitation amount shows a deviation of ±30 mm from the specific value provided in a deterministic format. Participants perceive the minimum night temperatures as the least-biased parameter from the deterministic forecast, while the amount of rain is perceived as the most-biased one. In addition, participants were then asked about their probabilistic threshold for taking appropriate precautionary action under distinct decision-making scenarios of temperature, wind speed, and rain. Results indicate that participants have different probabilistic thresholds for taking protective action and that context and presentation influence forecast use. Participants were also asked about the meaning of the probability-of-precipitation (PoP) forecast. Around 40% of responses reformulated the default options, and around 20% selected the correct answer, following previous studies related to this research topic. As a general result, it has been found that participants infer uncertainty into deterministic forecasts, and they are mostly used to take action in the presence of decision-making scenarios. In contrast, more difficulties were found when interpreting probabilistic forecasts.

scholarly journals “Are we talking just a bit of water out of bank? Or is it Armageddon?” Front line perspectives on transitioning to probabilistic fluvial flood forecasts in England

2020 ◽  
Vol 3 (2) ◽  
pp. 203-232
Author(s):  
Louise Arnal ◽  
Liz Anspoks ◽  
Susan Manson ◽  
Jessica Neumann ◽  
Tim Norton ◽  
...  
Keyword(s):  
Decision Making ◽  
Early Warning ◽  
Scientific Practice ◽  
Decision Makers ◽  
Decision Making Process ◽  
Probabilistic Forecasting ◽  
Flood Warning ◽  
Probabilistic Forecasts ◽  
The Uk ◽  
Ideal Framework

Abstract. By showing the uncertainty surrounding a prediction, probabilistic forecasts can give an earlier indication of potential upcoming floods, increasing the amount of time available to prepare. However, making a decision based on probabilistic information is challenging. As part of the UK-wide policy's move towards forecast-based flood risk management, the Environment Agency (EA), responsible for managing risks of flooding in England, is transitioning towards the use of probabilistic fluvial forecasts for flood early warning. While science and decision-making are both individually progressing, there is still a lack of an ideal framework for the incorporation of new and probabilistic science into decision-making practices, and, respectively, the uptake of decision-makers' perspectives in the design of scientific practice. To address this, interviews were carried out with EA decision-makers (i.e. Duty Officers), key players in the EA's flood warning decision-making process, to understand how they perceive this transition might impact on their decision-making. The interviews highlight the complex landscape in which EA Duty Officers operate and the breadth of factors that inform their decisions, in addition to the forecast. Although EA Duty Officers already account for uncertainty and communicate their confidence in the forecast they currently use, the interviews revealed a decision-making process which is still very binary and linear to an extent, which appears at odds with probabilistic forecasting. Based on the interview results, we make recommendations to support a successful transition to probabilistic forecasting for flood early warning in England. These recommendations include the new system's co-design together with Duty Officers, the preparation of clear guidelines on how probabilistic forecast should be used for decision-making in practice, EA communication with all players in the decision-making chain (internal and external) that this transition will become operational practice and the documentation of this transition to help other institutes yet to face a similar challenge. We believe that this paper is of wide interest for a range of sectors at the intersection between geoscience and society. A glossary of technical terms is highlighted by asterisks in the text and included in Appendix A.

scholarly journals IEA Wind Task 36 “Probabilistic Forecasting Games and Experiments” Initiative

2021 ◽  
Author(s):  
Corinna Möhrlen ◽  
Ricardo Bessa ◽  
Gregor Giebel
Keyword(s):  
Decision Making ◽  
Power System ◽  
Electricity Markets ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
End Users ◽  
Interdisciplinary Teams ◽  
Probabilistic Forecasting ◽  
Max Planck ◽  
Probabilistic Forecasts

<p>One key strategy to fight climate change worldwide is to invest in renewable energy sources (RES) and increase their integration into the power system. In recent years, we observed how extreme weather conditions, together with growing penetration levels of RES, are increasingly affecting the power system operation and planning, as well as electricity markets. The inherent uncertainty of such events and the associated uncertainty in the power generation from RES can no longer be ignored by the energy industry. In other words, current deterministic methods have reached their limit due to the inherent inability to model and convey forecast uncertainties.</p><p>Probabilistic information and forecasts have been shown to improve decision-making in many weather-related processes. By dealing with uncertainties, the end-user takes responsibility, but also gets the possibility to harvest the benefits of knowing and being able to calculate what is at stake. Last but not least, knowing the uncertainty of an event in advance opens the possibility to act upon such uncertainty rather than acting on the event itself and thereby mitigating costly side effects or being able to secure safety.</p><p>In 2020, the IEA Wind Task 36 “Wind Energy Forecasting” has for this reason started an initiative “Probabilistic Forecasting Games and Experiments” in collaboration with the Max-Planck Institute for Human Development. The main goal of this initiative is to empirically investigate the psychological barriers to the adoption of probabilistic forecasts and to enable stakeholders to understand and explore their benefit and use.  With the initiative, the IEA Wind Task 36 wants to establish interdisciplinary teams to promote testing and playing with forecast games and experiments to give end-users a “feel” of where the hidden possibilities are to improve decisions and developers a platform to:</p><ul><li>Discuss</li> <li>Educate</li> <li>Inspire</li> </ul><p>the energy and meteorology community for the development, deployment and communication of uncertainties of weather and energy forecasts to end-users for better decision making.</p><p>The task leaders have started to setup a platform with a list of forecasting games and experiments  developed by the task, in cooperation or by cooperating institutions, researchers or companies as well as invite others outside the tasks community to share links or data to games and experiments.</p><p>The initiative will be presented and the first experience with the task’s own games and experiments briefly discussed. The many open questions and considerations when looking forward towards the establishment of training and educational tools for probabilistic forecasts will be formulated and posed to the meteorological and psychological/behaviorism research community to enhance the collaboration and establish a stronger link for this interdiciplinary work. </p>

scholarly journals Using probabilistic forecasts to effectively enhance operational marine industry decision-making

2021 ◽  
Author(s):  
Ken Mylne ◽  
Edward Steele ◽  
Hannah Brown ◽  
Christopher Bunney ◽  
Philip Gill ◽  
...  
Keyword(s):  
Decision Making ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Past Performance ◽  
Value Analysis ◽  
Probabilistic Forecasts ◽  
Adverse Weather ◽  
Probability Threshold ◽  
Offshore Industry ◽  
The Impact

<p>Ensemble Prediction Systems (EPSs) are now run routinely by many global weather centres but, despite the enormous potential these forecasts offer, their perceived complexity has long presented a barrier to effective adoption by many users; limiting the opportunity for early decision-making by industry. To facilitate the interpretation of a set of (potentially seemingly contradictory) forecasts, a sensible approach is to turn the prediction into a binary (yes/no) forecast by applying a user-relevant operational weather limit – with the decision to proceed with or postpone an operation based on whether a certain proportion of the members predict un-/favourable conditions. However, the question then remains as to how the appropriate probability threshold to achieve an optimum decision can be objectively defined. Here, we present two approaches for simplifying the interpretation of ensemble (probabilistic) ocean wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the North Sea. Categorical verification information was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) EPS in the form of Receiver Operating Characteristic and Relative Economic Value analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold for the interpretation of future forecasts to be determined. Trials on an unseen nine-month period of data from the same site (Spring to Autumn 2019) confirm the approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. offshore pipeline installation), but similarly relevant for maximising the exploitation of any ensemble forecast –  by any sector – providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.</p>

scholarly journals Think fast! The implications of emphasizing urgency in decision-making

2020 ◽  
Author(s):  
Nathan J. Evans
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Drift Rate ◽  
Decision Time ◽  
Decision Making Process ◽  
Data Sets ◽  
Decision Threshold ◽  
Evidence Accumulation ◽  
Rate Decrease ◽  
Linear Ballistic Accumulator

Evidence accumulation models (EAMs) have become the dominant explanation of how the decision-making process operates, proposing that decisions are the result of a process of evidence accumulation. The primary use of EAMs has been as "measurement tools" of the underlying decision-making process, where researchers apply EAMs to empirical data to estimate participants' task ability (i.e., the "drift rate"), response caution (i.e., the "decision threshold"), and the time taken for other processes (i.e., the "non-decision time"), making EAMs a powerful tool for discriminating between competing psychological theories. Recent studies have brought into question the mapping between the latent parameters of EAMs and the theoretical constructs that they are thought to represent, showing that emphasizing urgent responding -- which intuitively should selectively influence decision threshold -- may also influence drift rate and/or non-decision time. However, these findings have been mixed, leading to differences in opinion between experts in the field. The current study aims to provide a more conclusive answer to the implications of emphasizing urgent responding, providing a re-analyse of 6 data sets from previous studies using two different EAMs -- the diffusion model and the linear ballistic accumulator (LBA) -- with state-of-the-art methods for model selection based inference. The findings display clear evidence for a difference in conclusions between the two models, with the diffusion model suggesting that decision threshold and non-decision time decrease when urgency is emphasized, and the LBA suggesting that decision threshold and drift rate decrease when urgency is emphasized. Furthermore, although these models disagree regarding whether non-decision time or drift rate decrease under urgency emphasis, both show clear evidence that emphasizing urgency does not selectively influence decision threshold. These findings suggest that researchers should revise their assumptions about certain experimental manipulations, the specification of certain EAMs, or perhaps both.

Impact of weather conditions on airport arrival delay and throughput

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Álvaro Rodríguez-Sanz ◽  
Javier Cano ◽  
Beatriz Rubio Fernández
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Weather Data ◽  
Arrival Process ◽  
Data Set ◽  
Content Type ◽  
Weather Events ◽  
Adverse Weather ◽  
Arrival Processes ◽  
The Impact

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

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