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Will a tropical cyclone make landfall?

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Abstract

In the different development phases of a tropical cyclone, the most exciting and complex phase is its landfall, which is when a tropical cyclone moves over to the land after crossing the ocean’s coast. The location, time, and intensity at landfall of a tropical cyclone determine the extent of the disaster caused by it. In this work, we investigate a fundamental question: will a tropical cyclone make a landfall? Knowing the answer to this question with high accuracy will have huge benefits as the preparedness for a potential landfall involves mobilizing substantial human and economic resources. To answer this fundamental question, we have used high-resolution reanalysis data ERA5 (ECMWF reanalysis \(5^{th}\) generation) and best track data IBTrACS (International Best Track Archive for Climate Stewardship) to develop a deep learning model that can predict the landfall event in the early phase of a tropical cyclone—in particular, using any 12 hours or 24 hours of data from the first 72 hours of its inception with very high accuracy. We tested the model for six ocean basins of the world and achieved a fivefold accuracy in the range of \(97.6\%\) to \(99.2\%\) across all basins. The model can be trained within 05 to 20 minutes depending on the ocean basin and can predict the above-stated problem within seconds, making it suitable for real-time application.

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Code availability

The datasets used in this study are public. The code and required procedures to download the required dataset and reproduce the results are available at GitHUB—https://github.com/skashodhiya/Will-a-Tropical-Cyclone-Make-Landfall-

Notes

  1. https://www.nhc.noaa.gov/modelsummary.shtml.

  2. https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access.

  3. https://cds.climate.copernicus.eu/.

Abbreviations

TCs:

Tropical cyclones

NA:

North Atlantic

NI:

North Indian

SI:

South Indian

WP:

West Pacific

SP:

South Pacific

EP:

East Pacific

ANN:

Artificial neural network

CNN:

Convolutional neural network

RNN:

Recurrent neural network

LSTM:

Long short-term memory networks

IMD:

Indian Meteorological Department

IBTrACS:

International Best Track Archive for Climate Stewardship

ECMEF:

European Centre for Medium-range Weather Forecasts

ERA5:

ECMWF reanalysis version-5

FAR:

False alarm rate

POD:

Probability of detection

FPR:

False positive rate

FNR:

False negative rate

AUC:

Area under the curve

ROC:

Receiver operating characteristics curve

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Authors and Affiliations

  1. Department of Mathematics, Shaheed Bhagat Singh College, University of Delhi, Delhi, India

    Sandeep Kumar

  2. Department of Computer Science, IIIT Delhi, Delhi, India

    Sandeep Kumar & Koushik Biswas

  3. Department of Mathematics, IIIT Delhi, Delhi, India

    Ashish Kumar Pandey

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Kumar, S., Biswas, K. & Pandey, A.K. Will a tropical cyclone make landfall?. Neural Comput & Applic 35, 5807–5818 (2023). https://doi.org/10.1007/s00521-022-07996-7

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