Using A Convective Weather Forecast Product to Predict Weather Impact on Air Traffic: Methodology and Comparison with Actual Data

2007 ◽  
Author(s):  
Alexander Klein ◽  
Sadegh Kavoussi ◽  
David Hickman ◽  
David Simenauer ◽  
Mark Phaneuf ◽  
...  
Keyword(s):  
Weather Forecast ◽  
Air Traffic ◽  
Actual Data ◽  
Weather Impact ◽  
Convective Weather

Development of a Probabilistic Convective Weather Forecast Threshold Parameter for Flight-Routing Decisions

2013 ◽  
Vol 28 (5) ◽  
pp. 1175-1187 ◽  
Author(s):  
Kapil Sheth ◽  
Thomas Amis ◽  
Sebastian Gutierrez-Nolasco ◽  
Banavar Sridhar ◽  
Daniel Mulfinger
Keyword(s):  
Traffic Control ◽  
Weather Forecast ◽  
Threshold Value ◽  
Air Traffic ◽  
Weather Data ◽  
Traffic Data ◽  
Threshold Parameter ◽  
Probability Threshold ◽  
Convective Cells ◽  
Convective Weather

Abstract This paper presents a method for determining a threshold value of probabilistic convective weather forecast data. By synchronizing air traffic data and an experimental probabilistic convective weather forecast product, it was observed that aircraft avoid areas of specific forecasted probability. Both intensity and echo top of the forecasted weather were synchronized with air traffic data to derive the probability threshold parameter. This value can be used by dispatchers for flight planning and by air traffic managers to reroute streams of aircraft around convective cells. The main contribution of this paper is to provide a method to compute the probability threshold parameters using a specific experimental probabilistic convective forecast product providing hourly guidance up to 6 h. Air traffic and weather data for a 4-month period during the summer of 2007 were used to compute the parameters for the continental United States. The results are shown for different altitudes, times of day, aircraft types, and airspace users. Threshold values for each of the 20 Air Route Traffic Control Centers were also computed. Additional details are presented for seven high-altitude sectors in the Fort Worth, Texas, center. For the analysis reported here, flight intent was not considered and no assessment of flight deviation was conducted since only aircraft tracks were used.

Heterogeneous Convective Weather Forecast Translation into Airspace Permeability with Prediction Intervals

2016 ◽  
Vol 24 (2) ◽  
pp. 41-54 ◽  
Author(s):  
Michael P. Matthews ◽  
Mark S. Veillette ◽  
Joseph C. Venuti ◽  
Richard A. DeLaura ◽  
James K. Kuchar
Keyword(s):  
Weather Forecast ◽  
Prediction Intervals ◽  
Convective Weather

Brazilian aviation nowcasting system: current stage and some results

2021 ◽  
Author(s):  
Vinícius Almeida ◽  
Gutemberg França ◽  
Francisco Albuquerque Neto ◽  
Haroldo Campos Velho ◽  
Manoel Almeida ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Weather Data ◽  
Vertical Acceleration ◽  
In Situ Data ◽  
Forecasting System ◽  
Under Construction ◽  
Convective Weather

<p>Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.</p>

Predicting Convective Storm Characteristics using Machine Learning from Hi-Resolution NWP Forecasts

2021 ◽  
Author(s):  
Aniel Jardines
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Binary Classification ◽  
Air Traffic ◽  
Mitigation Strategies ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Traffic Flow Management ◽  
Storm Characteristics ◽  
Convective Weather

<p>Convective weather represents a significant disruption to air traffic flow management (ATFM) operations. Thunderstorms are the cause for a substantial amount of delay in both the en-route and airport environment. Before the day of operations, poor prediction capability of convective weather prohibits traffic managers from considering weather mitigation strategies during the pre-tactical phase of ATFM planning. As a result, convective weather is mitigated tactically, possibly leading to excessive delays.  </p><p>The skill of weather forecasting has greatly improved in recent years. Hi-resolution weather models can predict the future state of the atmosphere for some weather parameters. However, incorporating the output from these sophisticated weather products into an ATFM solution that provides easily interpreted information by the air traffic managers remains a challenge. </p><p>This paper combines data from high-resolution numerical weather predictions with actual storm observations from lightning detecting and satellite images. It applies supervised machine learning techniques such as binary classification, multiclass classification, and regression to train neural networks to predict the occurrence, severity, and altitude of thunderstorms. The model predictions are given up to 36hr in advance, within timeframes necessary for pre-tactical planning of ATFM, providing traffic managers with valuable information for developing weather mitigation plans. </p>

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