Outstanding problems for the probabilistic prediction of the performance of HF terrestrial services

2012 ◽  
Author(s):  
L. Barclay
Keyword(s):  
Probabilistic Prediction

scholarly journals Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 29
Author(s):  
Stanley Förster ◽  
Michael Schultz ◽  
Hartmut Fricke
Keyword(s):  
Quantile Regression ◽  
Prediction Interval ◽  
Quantile Function ◽  
Air Traffic ◽  
Point Estimate ◽  
Probabilistic Prediction ◽  
Traffic System ◽  
Final Approach ◽  
Model Training ◽  
Prediction Approach

The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, in particular, the time-to-fly from the turn onto the final approach course to the threshold. The predictions are then used to determine advice for the controller regarding time-to-lose or time-to-gain for optimizing the separation within a sequence of aircraft. Most prediction methods developed so far provide only a point estimate for the time-to-fly. Complementary, we see the need to further account for the uncertain nature of aircraft movement based on a probabilistic prediction approach. This becomes very important in cases where the air traffic system is operated at its limits to prevent safety-critical incidents, e.g., separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that can provide a measure of uncertainty of the prediction not only in form of a prediction interval but also by generating a probability distribution over the dependent variable. While the data preparation, model training, and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate the interpretation of the results and provide a way for deriving advice to the controller from it. With this contribution, there is now a tool available that allows a more sophisticated prediction of time-to-fly, depending on the specific needs of the use case and which helps to separate arriving aircraft more efficiently.

scholarly journals Probability of Roadside Accidents for Curved Sections on Highways

2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Guozhu Cheng ◽  
Rui Cheng ◽  
Yulong Pei ◽  
Liang Xu
Keyword(s):  
Risk Factors ◽  
Decision Rules ◽  
Significant Risk ◽  
Vehicle Speed ◽  
Probabilistic Prediction ◽  
Interaction Detection ◽  
Probability Prediction ◽  
Chi Squared ◽  
Longitudinal Slope ◽  
Curve Radius

To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.

Ensemble Construction and Verification of the Probabilistic ENSO Prediction in the LDEO5 Model

2010 ◽  
Vol 23 (20) ◽  
pp. 5476-5497 ◽  
Author(s):  
Yanjie Cheng ◽  
Youmin Tang ◽  
Peter Jackson ◽  
Dake Chen ◽  
Ziwang Deng
Keyword(s):  
High Frequency ◽  
Singular Vector ◽  
Southern Oscillation ◽  
Initial Perturbation ◽  
Probabilistic Prediction ◽  
Construction Methods ◽  
Sea Surface Temperature Anomaly ◽  
Probabilistic Forecasts ◽  
Construction Strategy ◽  
Construction Strategies

Abstract El Niño–Southern Oscillation (ENSO) retrospective ensemble-based probabilistic predictions were performed for the period of 1856–2003 using the Lamont-Doherty Earth Observatory, version 5 (LDEO5), model. To obtain more reliable and skillful ENSO probabilistic predictions, first, four ensemble construction strategies were investigated: (i) the optimal initial perturbation with singular vector of sea surface temperature anomaly (SSTA), (ii) the realistic high-frequency anomalous winds, (iii) the stochastic optimal pattern of anomalous winds, and (iv) a combination of the first and the third strategy. Second, verifications were conducted to examine the reliability and resolution of the probabilistic forecasts provided by the four methods. Results suggest that reliability of ENSO probabilistic forecast is more sensitive to the choice of ensemble construction strategy than the resolution, and a reliable and skillful ENSO probabilistic prediction system may not necessarily have the best deterministic prediction skills. Among these ensemble construction methods, the fourth strategy produces the most reliable and skillful ENSO probabilistic prediction, benefiting from the joint contributions of the stochastic optimal winds and the singular vector of SSTA. In particular, the stochastic optimal winds play an important role in improving the ENSO probabilistic predictability for the LDEO5 model.

scholarly journals Uncertainty in weather and climate prediction

2011 ◽  
Vol 369 (1956) ◽  
pp. 4751-4767 ◽  
Author(s):  
Julia Slingo ◽  
Tim Palmer
Keyword(s):  
Chaos Theory ◽  
Model Uncertainty ◽  
Lead Times ◽  
Probabilistic Prediction ◽  
Climate Forecasting ◽  
Weather And Climate ◽  
Prediction Systems ◽  
The 1960S ◽  
Support Decision Making ◽  
Adaptation And Mitigation

Following Lorenz's seminal work on chaos theory in the 1960s, probabilistic approaches to prediction have come to dominate the science of weather and climate forecasting. This paper gives a perspective on Lorenz's work and how it has influenced the ways in which we seek to represent uncertainty in forecasts on all lead times from hours to decades. It looks at how model uncertainty has been represented in probabilistic prediction systems and considers the challenges posed by a changing climate. Finally, the paper considers how the uncertainty in projections of climate change can be addressed to deliver more reliable and confident assessments that support decision-making on adaptation and mitigation.

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