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

摘要

This paper presents a novel model, based on neural network techniques, to produceshort-term and local-specific forecasts of significant instability for flights in the terminal area of GaleãoAirport, Rio de Janeiro, Brazil. Twelve years of data were used for neuralnetwork training/validation and test. Data are originally from four sources:(1) hourly meteorological observations from surface meteorological stationsat five airports distributed around the study area; (2) atmospheric profilescollected twice a day at the meteorological station at Galeão Airport;(3) rain rate data collected from a network of 29 rain gauges in the studyarea; and (4) lightning data regularly collected by national detectionnetworks. An investigation was undertaken regarding the capability of aneural network to produce early warning signs – or as a nowcasting tool –for significant instability events in the study area. The automatednowcasting 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, and0.25). Possible sources of error related to the test procedure are presentedand discussed. The test showed that the proposed model (or neural network)can grab the physical content inside the data set, and its performance isquite encouraging for the first and second hours to nowcast significantinstability events in the study area.