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>

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.

scholarly journals Seasonal Variations in Severe Weather Forecast Skill in an Experimental Convection-Allowing Model

2017 ◽  
Vol 32 (5) ◽  
pp. 1885-1902 ◽  
Author(s):  
Ryan A. Sobash ◽  
John S. Kain
Keyword(s):  
Warm Season ◽  
Weather Forecast ◽  
Severe Weather ◽  
Cool Season ◽  
Limited Sample ◽  
Seasonal Behavior ◽  
Severe Convection ◽  
Convective Weather ◽  
The Relationship ◽  
Do So

Abstract Eight years of daily, experimental, deterministic, convection-allowing model (CAM) forecasts, produced by the National Severe Storms Laboratory, were evaluated to assess their ability at predicting severe weather hazards over a diverse collection of seasons, regions, and environments. To do so, forecasts of severe weather hazards were produced and verified as in previous studies using CAM output, namely by thresholding the updraft helicity (UH) field, smoothing the resulting binary field to create surrogate severe probability forecasts (SSPFs), and verifying the SSPFs against observed storm reports. SSPFs were most skillful during the spring and fall, with a relative minimum in skill observed during the summer. SSPF skill during the winter months was more variable than during other seasons, partly due to the limited sample size of events, but was often less than that during the warm season. The seasonal behavior of SSPF skill was partly driven by the relationship between the UH threshold and the likelihood of obtaining severe storm reports. Varying UH thresholds by season and region produced SSPFs that were more skillful than using a fixed UH threshold to identify severe convection. Accounting for this variability was most important during the cool season, when a lower UH threshold produced larger SSPF skill compared to warm-season events, and during the summer, when large differences in skill occurred within different parts of the continental United States (CONUS), depending on the choice of UH threshold. This relationship between UH threshold and SSPF skill is discussed within the larger scope of generating skillful CAM-based guidance for hazardous convective weather and verifying CAM predictions.

scholarly journals Probabilistic Load Forecasting for Building Energy Models

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6525
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Smart Grids ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Building Energy ◽  
Energy Performance ◽  
Prediction Intervals ◽  
Gaussian Kernel ◽  
Weather Data ◽  
Load Forecast ◽  
Probabilistic Load

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data.

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