In:
Ocean Science, Copernicus GmbH, Vol. 18, No. 2 ( 2022-03-28), p. 419-436
Abstract:
Abstract. A long short-term memory (LSTM) neural network is
proposed to predict hurricane-forced significant wave heights (SWHs) in the
Caribbean Sea (CS) based on a dataset of 20 CS, Gulf of Mexico, and western
Atlantic hurricane events collected from 10 buoys from 2010–2020. SWH
nowcasting and forecasting are initiated using LSTM on 0, 3, 6, 9, and
12 h horizons. Through examining study cases Hurricanes Dorian (2019),
Sandy (2012), and Igor (2010), results illustrate that the model is well
suited to forecast hurricane-forced wave heights much more
rapidly at a significantly cheaper computational cost compared to
numerical wave models, with much less required expertise. Forecasts are
highly accurate with regards to observations. For example, Hurricane Dorian
nowcasts had correlation (R), root mean square error (RMSE), and mean
absolute percentage error (MAPE) values of 0.99, 0.16 m, and 2.6 %,
respectively. Similarly, on the 3, 6, 9, and 12 h forecasts, results
produced R (RMSE; MAPE) values of 0.95 (0.51 m; 7.99 %), 0.92 (0.74 m;
10.83 %), 0.85 (1 m; 13.13 %), and 0.84 (1.24 m; 14.82 %),
respectively. In general, the model can provide accurate predictions within
12 h (R≥0.8) and errors can be maintained at under 1 m within
6 h of forecast lead time. However, the model also consistently
overpredicted the maximum observed SWHs. From a comparison of LSTM with a
third-generation wave model, Simulating Waves Nearshore (SWAN), it was
determined that when using Hurricane Dorian as a case example, nowcasts were
far more accurate with regards to the observations. This demonstrates that
LSTM can be used to supplement, but perhaps not replace, computationally
expensive numerical wave models for forecasting extreme wave heights. As
such, addressing the fundamental problem of phase shifting and other errors
in LSTM or other data-driven forecasting should receive greater scrutiny
from Small Island Developing States. To improve models results, additional
research should be geared towards improving single-point LSTM neural network
training datasets by considering hurricane track and identifying the
hurricane quadrant in which buoy observations are made.
Type of Medium:
Online Resource
ISSN:
1812-0792
DOI:
10.5194/os-18-419-2022
DOI:
10.5194/os-18-419-2022-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2022
detail.hit.zdb_id:
2183769-7
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