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Improving NCEP’s global-scale wave ensemble averages using neural networks
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2020
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Source: Ocean Modelling, 149, 101617
[PDF-1.93 MB]
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Journal Title:Ocean Modelling
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Description:The quality of metocean forecasts at longer forecast ranges has a significant impact on maritime safety and offshore operations. A nonlinear ensemble averaging technique is demonstrated using neural networks applied to one year (2017) of Global ocean Wave Ensemble forecast System (GWES) data provided by NCEP. Post-processing algorithms are developed based on multilayer perceptron neural networks (NN) trained with altimeter data to improve the global forecast skill, from nowcast to forecast ranges up to 10 days, including significant wave height (Hs) and wind speed (U10). NNs are applied as an alternative to the typical use of the arithmetic ensemble mean (EM). NN models are constructed using six variables sourced from 21 ensemble members, plus latitude, sin/cos of longitude, sin/cos of time, forecast lead time, and GWES cycle. The NN outputs are the residues of Hs and U10, i.e., the difference from the EM to the observations. One hidden (intermediate) layer is evaluated in terms of the optimum number of neurons (complexity) to map the given problem. The sensitivity test considered 26 different numbers of neurons, 10 seeds for initial conditions, and 3 equally-divided datasets; for a total of 780 NN experiments. Assessments using 2,507,099 paired satellite/GWES fields show that a simple NN model with few neurons is able to reduce the systematic errors for short-range forecasts, while a NN with more neurons is required to minimize the scatter error at longer forecast ranges. The novel method shows that one single NN model with 140 neurons is able to improve the error metrics for the whole globe while covering all forecast ranges analyzed. The bias of the widely used EM of GWES that varies from -10% to 10% for Hs compared to altimeters can be reduced to values within 5%. The RMSE of day-10 forecasts from the NN simulations indicated a gain of two days in predictability when compared to the EM, using a reasonably simple post-processing model with low computational cost.
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Source:Ocean Modelling, 149, 101617
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ISSN:1463-5003
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Rights Information:Accepted Manuscript
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