Ensemble-Guided Tropical Cyclone Track Forecasting for Optimal Satellite Remote Sensing

Within the realm of satellite remote sensing, optimal data acquisition to study natural phenomena under time, resource, and cost constraints is a well-known problem. Furthermore, since the sensors themselves are at remote locations with sparse ground connectivity, the optimal method must use a compu...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 59; no. 5; pp. 3607 - 3622
Main Authors: Ravindra, Vinay, Nag, Sreeja, Li, Alan
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Cyclones
Data
Data acquisition
Data collection
Data models
Forecasting
Hurricanes
Natural phenomena
Numerical models
Numerical weather forecasting
optimization
Prediction algorithms
Prediction models
Predictive models
Remote sensing
Remote sensors
Satellite broadcasting
satellite constellations
Satellite tracking
Satellites
Tropical climate
Tropical cyclones
tropical cyclones (TCs)
Weather forecasting
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Within the realm of satellite remote sensing, optimal data acquisition to study natural phenomena under time, resource, and cost constraints is a well-known problem. Furthermore, since the sensors themselves are at remote locations with sparse ground connectivity, the optimal method must use a computationally light forecasting algorithm, which assimilates information from the observations at possibly irregular intervals, in near real time. In this article, we propose and demonstrate the ensemble -guided cyclone track forecasting (EGCTF) method for application in remote tropical cyclone tracking. The algorithm uses ensemble data produced by numerical weather prediction models to guide the forecasting process while assimilating measured cyclone center positions. The algorithm was tested and analyzed with the Global Ensemble Forecasting System (GEFS) data and the National Hurricane Center data for the 2018 year hurricanes within the Atlantic basin. Compared with a baseline method that uses the GEFS-issued mean ensemble track (AEMN) for forecasting and no data assimilation, the proposed algorithm exhibited positive forecast skill for more than 290 test cases over forecast periods spanning 6-48 h. The skill is seen to improve with lengthening forecast periods, with five test cases showing greater than 75% skill for a forecast period of 6 h to 247 test cases for the forecast period of 48 h.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3010821