Sea-Level Forecasting on ViCoS Lab

Deep-learning transformer-based sea level modeling ensemble for the Adriatic basin

Mon, 01 Jan 0001 00:00:00 +0000

Storm surges and coastal floods are persistent threats to civil and economic safety in the Northern Adriatic. Meteorologically induced sea level signal is, however, often difficult to forecast deterministically due to the resonant character of the Adriatic basin. A standard solution is therefore resorting to ensembles of numerical ocean models, which are numerically expensive. In recent years, deep-learning-based methods have shown significant potential for numerically cheap alternatives. This is the venue followed in our work. We propose a new deep-learning transformer-based architecture HIDRA-T, a continuation of our recent model HIDRA2 (Rus et al., GMD 2023), which outperformed both state-of-the-art deep-learning network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine and a SCHISM ocean modeling system). HIDRA-T is our latest attempt at sea level forecasting, employing novel transformer-based atmospheric and sea level encoders. Transformers are designed for sequential data, and in HIDRA-T we use self-attention blocks to extract features from the atmospheric data firstly by tokenizing over spatial dimension, then over temporal dimension. HIDRA-T was trained on surface wind and pressure fields from the ECMWF atmospheric ensemble and on Koper tide gauge observations. On an independent and challenging test set, HIDRA-T outperforms all other models, reducing previous best mean absolute forecast error in storm events of HIDRA2 by 2.6 %.

HIDRA 1.0: deep-learning-based ensemble sea level forecasting in the northern Adriatic

Mon, 01 Jan 0001 00:00:00 +0000

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.

HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data

Mon, 01 Jan 0001 00:00:00 +0000

his paper introduces HIDRA-D, a novel deep-learning model for basin scale dense (gridded) sea level prediction using sparse satellite altimetry and in situ tide gauge data. Accurate sea level prediction is crucial for coastal risk management, marine operations, and sustainable development. While traditional numerical ocean models are computationally expensive, especially for probabilistic forecasts over many ensemble members, HIDRA-D offers a faster, numerically cheaper, observation-driven alternative. Unlike previous HIDRA models (HIDRA1, HIDRA2 and HIDRA3) that focused on point predictions at tide gauges, HIDRA-D provides dense, two-dimensional, gridded sea level forecasts. The core innovation lies in a new algorithm that effectively leverages sparse and unevenly distributed satellite altimetry data in combination with tide gauge observations, to learn the complex basin-scale dynamics of sea level. HIDRA-D achieves this by integrating a HIDRA3 module for point predictions at tide gauges with a novel Dense decoder module, which generates low-frequency spatial components of the sea level field in the Fourier domain, whose Fourier inverse is an hourly sea level forecast over a 3 d horizon. When comparing 3 d forecasts against satellite absolute dynamic topography (ADT) data in the Adriatic, HIDRA-D achieves a 28.0 % reduction in mean absolute error relative to the NEMO general circulation model. However, while HIDRA-D performs well in open waters, leave-one-out cross-validation at tide gauges indicates limitations in areas with complex bathymetry, such as the Neretva estuary located in a narrow bay, and in regions with sparse satellite ADT data, like the northern Adriatic. Importantly, the model shows robustness to spatially-limited tide gauge coverage, maintaining acceptable performance even when trained using data from distant stations. This suggests its potential for broader applicability in areas with limited in situ observations.

HIDRA-T – A Transformer-Based Sea Level Forecasting Method

Mon, 01 Jan 0001 00:00:00 +0000

Sea surface height forecasting is critical for timely prediction of coastal flooding and mitigation of is impact on coastal comminities. Traditional numerical ocean models are limited in terms of computational cost and accuracy, while deep learning models have shown promising results in this area. However, there is still a need for more accurate and efficient deep learning architectures for sea level and storm surge modeling. In this context, we propose a new deep-learning architecture HIDRA-T for sea level and storm tide modeling, which is based on transformers and outperforms both state-of-the-art deep-learning network designs HIDRA1 and HIDRA2 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. Compared to its predecessor HIDRA2, HIDRA-T employs novel transformer-based atmospheric and sea level encoders, as well as a novel feature fusion and regression block. HIDRA-T was trained on surface wind and pressure fields from ECMWF atmospheric ensemble and on Koper tide gauge observations. Compared to other models, a consistent superior performance over all other models is observed in the extreme tail of the sea level distribution.

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Mon, 01 Jan 0001 00:00:00 +0000

We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

HIDRA2: deep-learning ensemble sea level and storm tide forecasting in the presence of seiches – the case of the northern Adriatic

Mon, 01 Jan 0001 00:00:00 +0000

We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. Power spectrum analysis indicates that HIDRA2 most accurately represents the energy density peak centered on the ground state sea surface eigenmode (seiche) and comes a close second to SCHISM in the energy band of the first excited eigenmode. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures

Mon, 01 Jan 0001 00:00:00 +0000

Accurate modeling of sea level and storm surge dynamics with several days of temporal horizons is essential for effective coastal flood responses and the protection of coastal communities and economies. The classical approach to this challenge involves computationally intensive ocean models that typically calculate sea levels relative to the geoid, which must then be correlated with local tide gauge observations of sea surface height (SSH). A recently proposed deep-learning model, HIDRA2 (HIgh-performance Deep tidal Residual estimation method using Atmospheric data, version 2), avoids numerical simulations while delivering competitive forecasts. Its forecast accuracy depends on the availability of a sufficiently long history of recorded SSH observations used in training. This makes HIDRA2 less reliable for locations with less abundant SSH training data. Furthermore, since the inference requires immediate past SSH measurements as input, forecasts cannot be made during temporary tide gauge failures. We address the aforementioned issues using a new architecture, HIDRA3, that considers observations from multiple locations, shares the geophysical encoder across the locations, and constructs a joint latent state that is decoded into forecasts at individual locations. The new architecture brings several benefits: (i) it improves training at locations with scarce historical SSH data, (ii) it enables predictions even at locations with sensor failures, and (iii) it reliably estimates prediction uncertainties. HIDRA3 is evaluated by jointly training on 11 tide gauge locations along the Adriatic. Results show that HIDRA3 outperforms HIDRA2 and the Mediterranean basin Nucleus for European Modelling of the Ocean (NEMO) setup of the Copernicus Marine Environment Monitoring Service (CMEMS) by ∼ 15 % and ∼ 13 % mean absolute error (MAE) reductions at high SSH values, creating a solid new state of the art. The forecasting skill does not deteriorate even in the case of simultaneous failure of multiple sensors in the basin or when predicting solely from the tide gauges far outside the Rossby radius of a failed sensor. Furthermore, HIDRA3 shows remarkable performance with substantially smaller amounts of training data compared with HIDRA2, making it appropriate for sea level forecasting in basins with high regional variability in the available tide gauge data.

HIDRA3: A Robust Deep-Learning Model for Multi-Point Sea-Surface Height and Storm Surges Forecasting

Mon, 01 Jan 0001 00:00:00 +0000

Accurate forecasting of storm surges and extreme sea levels is crucial for mitigating coastal flooding and safeguarding communities. While recent advancements have seen machine learning models surpass state-of-the-art physics-based numerical models in sea surface height (SSH) prediction, challenges persist, particularly in areas with limited SSH measurement history and instances of sensor failures. In this study, we developed HIDRA3, a novel deep-learning approach designed to address these challenges by jointly predicting SSH at multiple locations, allowing the training even in the presence of data scarcity and enabling predictions at locations with sensor failures. Compared to the state-of-the-art model HIDRA2 and the numerical model NEMO, HIDRA3 demonstrates notable improvements, achieving, on average, 5.0% lower Mean Absolute Error (MAE) and 11.3% lower MAE on extreme sea surface heights.

HIDRA3: A Robust Deep-Learning Model for Multi-Point Sea-Surface Height Forecasting

Mon, 01 Jan 0001 00:00:00 +0000

Accurate sea surface height (SSH) forecasting is crucial for predicting coastal flooding and protecting communities. Recently, state-of-the-art physics-based numerical models have been outperformed by machine learning models, which rely on atmospheric forecasts and the immediate past measurements obtained from the prediction location. The reliance on past measurements brings several drawbacks. While the atmospheric training data is abundantly available, some locations have only a short history of SSH measurement, which limits the training quality. Furthermore, predictions cannot be made in cases of sensor failure even at locations with abundant past training data. To address these issues, we introduce a new deep learning method HIDRA3, that jointly predicts SSH at multiple locations. This allows improved training even in the presence of data scarcity at some locations and enables making predictions at locations with failed sensors. HIDRA3 surpasses the state-of-the-art model HIDRA2 and the numerical model NEMO, on average obtaining a 5.0% lower Mean Absolute Error (MAE) and an 11.3% lower MAE on extreme sea surface heights.

Improvements of the Adriatic Deep-Learning Sea Level Modeling Network HIDRA

Mon, 01 Jan 0001 00:00:00 +0000