An automated nowcasting model of significant instability events in the flight terminal area of Rio de Janeiro, Brazil
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 t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-05-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/9/2335/2016/amt-9-2335-2016.pdf |
| Summary: | 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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| ISSN: | 1867-1381 1867-8548 |