| Autor: |
Roy, Chandan, Rahman, Md. Rejaur, Ghosh, Manoj Kumer, Biswas, Shoumen |
| Zdroj: |
Modeling Earth Systems and Environment; February 2024, Vol. 10 Issue: 1 p523-537, 15p |
| Abstrakt: |
Background: Effective management of tropical cyclone (TC) emergencies largely depends on accurate forecasting of TC intensity. Despite being essential for successful disaster warning/management, accurate forecasting of TC intensity has remained a challenging task still today, mainly because of inadequate knowledge about the processes associated with TC intensity change as well as lack of suitable data representing those processes. Objective: This study aims at employing a biologically inspired computational model with combined supervised and unsupervised learning capabilities to forecast tropical cyclone (TC) intensity 12– and 24 h ahead in the Bay of Bengal (BoB). Method: The model was simulated separately in train and test phases based on temporal sequences of infrared, sea surface temperature, sea-level pressure, wind direction and wind speed images of ten TCs formed between 2006 and 2021 in the BoB. Intensity forecasts were produced on a four-point scale used by the Bangladesh Meteorological Department and validated against the observed wind speeds in the TC best track datasets. Results: Intensity prediction accurately was over 90% when the model was tested using datasets consisting of temporal continuances of TC lifecycle images kept out of training. However, TC intensity forecasting accuracy remained between 36 and 48%, when the model was used to generate forecasts for the images of a completely new TC. Conclusions: These findings indicate, biologically inspired computational model may further be developed into a useful TC intensity forecasting technique through systematic training and testing using images of more TCs in the BoB. |
| Databáze: |
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