Abstract
Abstract. Interactions between atmospheric forcing, topographic constraints to air and water flow, and resonant character of the basin make sea level modelling in the Adriatic a challenging problem. In this study we present an ensemble deep-neural-network-based sea level forecasting method HIDRA, which outperforms our set-up of the general ocean circulation model ensemble (NEMO v3.6) for all forecast lead times and at a minuscule fraction of the numerical cost (order of 2×10-6). HIDRA exhibits larger bias but lower RMSE than our set-up of NEMO over most of the residual sea level bins. It introduces a trainable atmospheric spatial encoder and employs fusion of atmospheric and sea level features into a self-contained network which enables discriminative feature learning. HIDRA architecture building blocks are experimentally analysed in detail and compared to alternative approaches. Results show the importance of sea level input for forecast lead times below 24 h and the importance of atmospheric input for longer lead times. The best performance is achieved by considering the input as the total sea level, split into disjoint sets of tidal and residual signals. This enables HIDRA to optimize the prediction fidelity with respect to atmospheric forcing while compensating for the errors in the tidal model. HIDRA is trained and analysed on a 10-year (2006–2016) time series of atmospheric surface fields from a single member of ECMWF atmospheric ensemble. In the testing phase, both HIDRA and NEMO ensemble systems are forced by the ECMWF atmospheric ensemble. Their performance is evaluated on a 1-year (2019) hourly time series from a tide gauge in Koper (Slovenia). Spectral and continuous wavelet analysis of the forecasts at the semi-diurnal frequency (12 h)−1 and at the ground-state basin seiche frequency (21.5 h)−1 is performed. The energy at the basin seiche in the HIDRA forecast is close to that observed, while our set-up of NEMO underestimates it. Analyses of the January 2015 and November 2019 storm surges indicate that HIDRA has learned to mimic the timing and amplitude of basin seiches.
Highlights
Climate change is inducing sea level rise and adversely affects coastal ecosystems, economies and civil safety
Performance analysis over different prediction lead times (Fig. 6a) shows that the sea-level-only model HIDRAsl makes much larger errors (41 % increase compared to HIDRA0) when predicting far into the future, which suggests that the network has trouble predicting the tidal component that far into the future using only the data from the last 24 h
HIDRA0 performs on par with the more complex temporal convolutional networks (TCN)-based HIDRATCN (RMSE of HIDRATCN is 3 % larger), while long short-term memory (LSTM)-based HIDRALSTM performs worse (RMSE is 14 % larger than HIDRA0)
Summary
Climate change is inducing sea level rise and adversely affects coastal ecosystems, economies and civil safety. In the shallow northern Adriatic, low sea levels influence port activities and inhibit marine cargo while high sea levels cause substantial coastal flooding, inundation and erosion (Ferrarin et al, 2020), presenting a serious threat to Venice, Chioggia, Piran and other coastal towns and businesses in the region. Low sea levels predominantly occur when periods of high atmospheric pressure coincide with spring tide sea level minimums. High sea levels typically occur as storm surges during passages of atmospheric cyclones which manifest themselves as substantial air pressure lows and related winds over the basin. Adriatic Sea has an elongated basin with northwest– southeast orientation, lies in the Northern Central Mediterranean and connects to the eastern Mediterranean basin through the Otranto strait at its southern end (see Fig. 1). The basin lies embedded between the Alps (to the north), the Apennines (to the west) and Dinaric Alps (to the east), and it Published by Copernicus Publications on behalf of the European Geosciences Union
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