Airport Vicinity Icing and Snow Advisor (AVISA)

Isaac, George A.; Bailey, Monika; Cober, Stewart G.; Donaldson, Norman; Driedger, N.; Glazer, Anna; Gültepe, Ismail; Hudak, David; Korolev, Alexei; Reid, Janti; Rodriguez, Peter; Strapp, J. W.; Fabry, Frédéric

doi:10.2514/6.2006-1219

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…Mueller et al (2003) used joint data from surface weather stations, radars, satellites, and numerical modeling to develop a 1-h forecast method to locate thunderstorms in two regions of the United States. Later, Isaac et al (2006Isaac et al ( , 2011Isaac et al ( , and 2014 proposed a number of works related to weather forecasting for Canada's major airports, using information from numerical models, observations from surface stations, radar, satellite, and a microwave radiometer to produce forecasts of up to about six hours. In southwest Germany, Kunz (2007) assessed the ability of meteorological variables obtained from 12Z upper air sounding to predict convective systems and found that the survey instability index, in particular, is capable of doing so with a prediction accuracy of about 80%.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models for Convective Meteorological Events

Pereira-Nunes,

França,

de Almeida

2024

Pure Appl. Geophys.

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This research developed models, based on machine learning (MA), for forecasting 16 hours and 4 hours of occurrence of a convective meteorological event (CME), 4 hours for forecasting severity and evaluating the applicability of the optimal models of 12 UTC using thermodynamic instability indices (TII) data extracted from the WRF model with two different types of parameterization con guration in an attempt to develop a30-hour CME forecast model. In the training and testing of the MA algorithms, the classic TIIs (input) were used, obtained from the atmospheric pro les of the Brasilia upper air sounding and atmospheric discharges (output) detected in the study area for the characterization of CME, considering the period from 2012 to 2017. The optimal models applied to the modeled TIIs were evaluated through statistical metrics with con guration II obtaining signi cant results. For CME detection, the results showed that the best models obtained POD, 1-FAR, F-MEASURE and KAPPA with values respectively greater than 0.90, 0.80, 0.90, 0.80 and BIAS ranging from 0 .89 and 1.12. For the detection of event severity, the model presented the following statistical values (in parentheses): POD (0.82), 1-FAR (0.78), F-MEASURE (0.82), KAPPA (0.59 ) and BIAS (0.97). The results of 16-h and 4-h CME prediction hindcasts (30 days) with developed models demonstrated acceptable performance in identifying the occurrence or non-occurrence of CME and its severity for the study area.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models for Convective Meteorological Events

Pereira-Nunes,

França,

de Almeida

2024

Pure Appl. Geophys.

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“…Mass (2012) provided a comprehensive review of nowcasting including current developments and future challenges. Considering the aviation application, Isaac et al (2006Isaac et al ( , 2011 2014) presented a sequence of works that resulted in a refined nowcasting system for aviation that uses data from numerical models, surface observations, radar, satellite, and a microwave radiometer to generate nowcasts for principal airports in Canada up to approximately 6 h. In contrast, in Brazil, a meteorologist is currently using his experience to integrate different in situ meteorological observations and/or atmospheric model outputs using conceptual models on how the atmosphere works to generate nowcast at principal airports. In particular, the TA of Rio de Janeiro, the focus of this study, has five airports (see Fig.…”

Section: Introductionmentioning

confidence: 99%

An automated nowcasting model of significant instability events in the flight terminal area of Rio de Janeiro, Brazil

2016

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Abstract. This paper presents a novel model, based on neural network techniques, to produce short-term and local-specific forecasts of significant instability for flights in the terminal area of Galeão Airport, Rio de Janeiro, Brazil. Twelve years of data were used for neural network training/validation and test. Data are originally from four sources: (1) hourly meteorological observations from surface meteorological stations at five airports distributed around the study area; (2) atmospheric profiles collected twice a day at the meteorological station at Galeão Airport; (3) rain rate data collected from a network of 29 rain gauges in the study area; and (4) lightning data regularly collected by national detection networks. An investigation was undertaken regarding the capability of a neural network to produce early warning signs -or as a nowcasting tool -for significant instability events in the study area. The automated nowcasting model was tested using results from five categorical statistics, indicated in parentheses in forecasts of the first, second, and third hours, respectively, namely proportion correct (0.99, 0.97, and 0.94), BIAS (1.10, 1.42, and 2.31), the probability of detection (0.79, 0.78, and 0.67), false-alarm ratio (0.28, 0.45, and 0.73), and threat score (0.61, 0.47, and 0.25). Possible sources of error related to the test procedure are presented and discussed. The test showed that the proposed model (or neural network) can grab the physical content inside the data set, and its performance is quite encouraging for the first and second hours to nowcast significant instability events in the study area.

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“…Nowcasting is weather forecasting on such short time scales, typically less than 6 h. The Canadian Airport Nowcasting (CAN‐Now) project was initiated in 2006 to help address the need for improved short term weather forecasting. It is a follow‐on to an earlier project, the Airport Vicinity Icing and Snow Advisor (AVISA; Isaac et al , ). AVISA focused on icing at the airport but it was recognized that such a system would not be implemented unless it considered all potential weather hazards during every season.…”

Section: Introductionmentioning

confidence: 99%

The Canadian Airport Nowcasting System (CAN‐Now)

Isaac

Bailey

Boudala

et al. 2012

Meteorological Applications

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ABSTRACT:The Canadian Airport Nowcasting Project (CAN-Now) has developed an advanced prototype all-season weather forecasting and nowcasting system that can be used at major airports. This system uses numerical model data, pilot reports, ground in situ sensor observations (precipitation, icing, ceiling, visibility, winds), on-site remote sensing (such as vertically pointing radar and microwave radiometer) and off-site remote sensing (satellite and radar) information to provide detailed nowcasts out to approximately 6 h. The nowcasts, or short term weather forecasts, should allow decision makers such as pilots, dispatchers, de-icing crews, ground personnel or air traffic controllers to make plans with increased margins of safety and improved efficiency. The system has been developed and tested at Toronto Pearson International Airport (CYYZ) and Vancouver International Airport (CYVR). A Situation Chart has been developed to allow users to have a high glance value product which identifies significant weather related problems at the airport. New products combining observations and numerical model output into nowcasts have been tested. Some statistical verifications of forecast products, with comparisons to persistence, covering both a winter (2009/2010) and summer (2010) period have been made. Problems with the prediction of relative humidity and wind direction are outlined. The ability to forecast categorical variables such as ceiling, visibility, as well as precipitation rate and type accurately are discussed. Overall, for most variables, the nowcast systems can outperform persistence after the first 1 or 2 h, and provide more accurate forecasts than individual Numerical Weather Prediction models out to 6 h.

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Airport Vicinity Icing and Snow Advisor (AVISA)

Cited by 13 publications

References 10 publications

Application of Machine Learning Models for Convective Meteorological Events

Application of Machine Learning Models for Convective Meteorological Events

An automated nowcasting model of significant instability events in the flight terminal area of Rio de Janeiro, Brazil

The Canadian Airport Nowcasting System (CAN‐Now)

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