CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
Authors: Zelin Xu
Yupu Zhang
Tingsong Xiao
Maitane Olabarrieta Lizaso
Jose M. Gonzalez-Ondina
Zibo Liu
Shigang Chen
Zhe Jiang
Venue: ICML 2025 poster (2025)
Keywords: Coastal Processes
Dataset
Benchmark
Primary area: applications->chemistry_physics_and_earth_sciences

Abstract: Over 40\% of the global population lives within 100 kilometers of the coast, which contributes more than \$8 trillion annually to the global economy. Unfortunately, coastal ecosystems are increasingly vulnerable to more frequent and intense extreme weather events and rising sea levels. Coastal scientists use numerical models to simulate complex physical processes, but these models are often slow and expensive. In recent years, deep learning has become a promising alternative to reduce the cost of numerical models. However, progress has been hindered by the lack of a large-scale, high-resolution coastal simulation dataset to train and validate deep learning models. Existing studies often focus on relatively small datasets and simple processes. To fill this gap, we introduce a decade-long, high-resolution (<100m) coastal circulation modeling dataset on a real-world 3D mesh in southwest Florida with around 6 million cells. The dataset contains key oceanography variables (e.g., current velocities, free surface level, temperature, salinity) alongside external atmospheric and river forcings. We evaluated a customized Vision Transformer model that takes initial and boundary conditions and external forcings and predicts ocean variables at varying lead times. The dataset provides an opportunity to benchmark novel deep learning models for high-resolution coastal simulations (e.g., physics-informed machine learning, neural operator learning).
The code and dataset can be accessed at https://github.com/spatialdatasciencegroup/CoastalBench.

CoastalBench: A Decade-Long High-Resolution Dataset to Emulate Complex
Coastal Processes
Zelin Xu Yupu Zhang'
Tingsong Xiao
Maitane Olabarrieta Jose M. Gonzalez-Ondina Zibo Liu'
Shigang Chen
Zhe Jiang'
Abstract
1. Introduction
Over 40% of the global population lives within 100 kilometers of the coast, which contributes more than $8 trillion annually to the global economy. Unfortunately, coastal ecosystems are increasingly vulnerable to more frequent and intense extreme weather events and rising sea levels. Coastal scientists use numerical models simulate complex physical processes, but these models are often slow and expensive. In recent years, deep learning has become a promising alternative to reduce the cost of numerical models. However, progress has been hindered by the lack of large-scale, high-resolution coastal simulation dataset train and validate deep learning models. Existing studies often focus on relatively small datasets and simple processes. To fill this gap. we introduce decade-long, high- resolution (<1 (<00m) coastal circulation modeling dataset on real-world 3D mesh in southwest Florida with around million cells. The dataset contains key oceanography variables (c.g.. current velocities, free surface level, temperature, salinity) alongside external atmospheric and river forcings. We evaluated a customized Vision Transformer model that takes initial and boundary conditions and external forcings and predicts ocean variables varying lead times. The dataset provides an opportunity to benchmark novel deep learning models for high-resolution coastal simulations (e.g., physics-informed machine learning, neural operator learning). The code and dataset can be accessed at https: /gt..b... com/ wtt.aaaaaaasccinnnoooo.... Coastal Bench.
Over 40% of the global population lives within 100 kilo- meters of the coast, with these coastal regions contribut- ing more than $8 trillion annually the global econ- omy (Martínez et al., 2007; Nicholls, 2004). Coastal ecosys- tems provide multitude of services that support human pop- ulations and natural habitats, such as regulating the global climate, protecting against natural disasters, and sustain- ing food security through fisheries (Barbier et al., 201 1). Unfortunately, these ecosystems ble to more frequent and intense extreme weather events, rising sea levels, and additional stressors such as habitat degradation, invasive species, and pollution (He & Silli- man, 2019). Coastal scientists develop numeric models to simulate complex coastal processes such as wave propaga- tion, tidal dynamics, wind-driven circulation, temperature dynamics, and salinity dynamics in estuaries (Neumann et al., 2017). Unfortunately, such numerical models computationally expensive. Recently, deep learning has pro- vided a promising alternative to emulate complex coastal processes with neural networks, significantly reducing the computational costs (James et al., 2018; Wei & Davison, 2022).
increasingly vulnera-
However, progress has been hindered by the lack of large- scale, high-resolution coastal dataset to train and validate deep learning models. Effectively modeling coastal pro- cesses requires a high spatial (e.g.. 100m) and temporal (e.g., 30 minutes) resolution. The spatial resolution often needs to reach even a few meters near inlets in estuarics to capture complex land-water interactions.
As summarized in Table 1, existing studies on deep learning for coastal processes primarily focus on relatively small datasets for specific coastal processes, such as wave propa- gation (James et al., 2018; Wei & Davison, 2022; Xu et al., 2025), sea level rise (Nieves et al., 2021; Ishida et al., 2020), and sea surface temperature (Chen al., 2024; Song et al., 2024; Nieves al., 2021). There are also works on training physies-informed neural networks (PINNs) (Raissi al., 2019) for coastal flood and storm surge modeling (Feng et al., 2023; Chen et al., 2022). However, training PINN in real-world complex topography is very challenging, if
'Department of Computer & Information Science & Engineer- ing, University of Florida, Gainesville, FL, USA DDepartment of Civil & Coastal Engineering, University of Florida, Gainesville, FL, USA. Correspondence to: Zhe Jiang <zhe sche.jiang& @ x.
Proceedings of the =t International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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CoastalBench; A Decade-Long High-Resolltion Dataset to Emulate Complex Coastal Processes
Table I. Comparison of existing datasets used for deep learning for coastal processes.
Dataset & Reference
Spatial Resolution
Temporal
Time Span
Variables
Resolution
OO'Donchha et al., 2019)
~ km
hour
4 years (2013-2017)
Wave condition, wind speed
(Jiang al., 2021)
7 km
5 min 10 min
I year (2020)
Wind speed, pressure, sea surface level
Melo et al., 2023)
~~110 m
few days
Wave condition, wind speed Wave condition, sediment transport
(Kumar& & Leonardi, 2023)
~ km
500 storm simulations
(Shen et al., 2024)
100
I day
5 years (1991 1995)
Wave condition, wind speed
(Wu et al., 2024)
~25 km
month
29 years (1993-2021)
Sea surface air temperature, salinity, wind speed, Chlorophyll-a concentration, atmospheric pCO,, etc Wave condition, temperature, salinity, sea surface air pressure, air temperature, humidity, rainfall, sun radiation, wind speed, etc.
CoastalBench (Ours)
~~10
30 min
10 years (2008-2017)
possible. Another relevant body of research is deep learning for global weather forecasting (Lam et al., 2023; Bi et al., 2023; Nguyen et al., 2024; Hersbach et al., 2020; Rasp et al., 2024). The main difference is that these works focus low-resolution atmospheric simulations on global scale, while we focus on high-resolution regional simulations.
plex coastal processes at such a high spatiotemporal resolution and scale, providing a unique opportunity to benchmark novel deep-learning models in coastal oceanography.
We design a straightforward yet effective ViT-based model for regional coastal processes by leveraging the dataset and incorporating static features, externl forcings, initial and boundary conditions, and other key factors.
To fill these gaps, this paper introduces CoastalBench, a decade-long (2008-2017), high-resolution coastal simula- tion dataset generated using the Regional Ocean Modeling System (ROMS) (Shchepetkin & McWilliams, 2005). Cov- ering estuary near Charlotte Harbor, Florida, USA, the dataset features a 3D mesh with over 6 million cells, average horizontal grid resolution of less than 100m, and a 30-minute temporal resolution. It includes simulated ocean variables (e.g., current velocity, salinity, temperature, free surface elevation), external forcings (e.g., meteorological forcings, river forcings), and static physical features (c.g., bathymetry). CoastalBench contains physical variables lated to multiple processes, such as wave propagation, tidal dynamics, wind-driven circulation, temperature dynamics, and salinity dynamics in estuaries. For example, storm surge and coastal flood forecasting benefit from predictions of free surface elevation; water quality and stratification modeling rely on temperature and salinity; and sediment transport analysis depends on vertical diffusivity.
Through experimental results, demonstrate the promising performance of the deep learning model by comparing it with ROMS simulations. An ablation study validates the importance of components, while a scaling test analyzes the impact of model size on predictive accuracy.
2. Preliminaries
The coastal ocean is highly dynamic environment, in- fluenced by the complex interplay of atmospheric forcing, terrestrial runoff, oceanic boundary inputs, and bathymetry. This work formulates the problem of learning a deep learn- ing model to emulate ROMS simulations of coastal dynam- ics, given appropriate physical inputs. The model is condi- tioned key components that govern the ocean state evo- lution, including initial and boundary conditions, external forcings, and static spatial features. We begin by introducing relevant terminologies used throughout this work.
We further evaluate customized Vision Transformer (ViT) model (Dosovitskiy et al., 2020) for this dataset, which encodes input boundary conditions into conditional attention operated on patches of initial conditions and metcorological forcings. The model can predict coastal variables at varying lead times. In summary, the contributions of this paper are as follows:
Definition 2.1. Initial Conditions (IC) specify the three- dimensional ocean state at the start of the simulation (1 = to). These include fields such as velocity, temperature, and salinity, denoted X xxCxxxXX where C is the number of variables, and H, W. D are the height, width, and depth of the 3D study area, respectively. Formally, the initial conditions can be expressed as:
We release CoastalBench, a decade-long, high- resolution coastal circulation simulation dataset gen- erated from the ROMS model. To the best of our knowledge, this the first open dataset to capture com-
X1c = X(t));
(1)
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CoastalBench; A Decade-Long High-Resolltton Dataset to Emulate Complex Coastal Processes
Definition 2.2. Boundary Conditions (BC) specify the values of the state variables, X, at the lateral edges of the simulation domain, denoted as OΩ. These conditions repre- sent interactions with adjacent open ocean regions and are typically preseribed based on larger-scale circulation mod- els or observational datasets. For our purposes, we focus on lateral boundary conditions:
Table Overview of CoastalBench.
Property
Description
Temporal Coverage
2008-2017 (10 years)
Temporal Resolution
30 minutes
Spatial Domain
Charlotte Harbor, Florida,
USA (~800 km²)
Spatial Resolution
Varying, ~ ~20 m x 100 m Non-uniform 3D mesh
(tc X
Grid Type
Grid Dimensions
898 x 598 x 12
Definition 2.3. External Forcings are time-varying drivers that influence the internal evolution of the coastal ocean sys- tem. These include: Meteorological Forcings, including surface fields such as wind stress, air temperature, pressure, humidity, shortwaavelonggwae radiation, and precipitation, typically derived from meteorological reanalysis; River In- flow, including freshwater inflows from rivers that influence coastal stratification, salinity, and circulation.
Number of Variables
22
Data Format
NetCDF
Total Volume
18 TB
Simulation Model
Regional Ocean Modeling System (ROMS)
solves the Seynolds-Averagee Navier-Stokes (RANS) equa- tions under the hydrostatic and Boussinesq approximations, employing a currain-follower vertical coordinate system to accurately represent complex bathymetry and coastal pro- cesses (Haidvogel et al., 2008; Shchepetkin & McWilliams, 2005). ROMS incorporates advanced numerical schemes for advection and mixing, along with sophisticated turbu- lence closure models, making it well-suited for studying ocean circulation, estuarine dynamics, and ecosystem inter- actions. The model supports various physical parameteriza- tions, data assimilation techniques, and multi-scale nesting capabilities, enabling its application across a wide range of oceanographic and coastal studies.
Then, our problem can be formally defined as follows:
Input:
Initial conditions X1c E RC the state of variables at to-
Lead time At E RR,, the time interval for prediction relative to lo-
Boundary conditions Xto + At) E RC the state of variables at the lateral boundaries at to + At.
Meteorological forcings (49 + At) E Mx
where M is the number of meteorological variables.
River inflow fRiver(to + At) E R where N is the number of river variables and is the number of rivers.
Static time-invariant physical features s E RSHHx.
where S is the number of features.
Table 3. Summary of physical variables included in CoastalBench. The dataset provides comprehensive representation of ocean state variables, external forcings, and static spatial features, supporting machine learning models for coastal process modeling.
Output:
Predicted variables X(to + At) RC at to + At.
Objective:
Minimize the prediction error of the deep learning model.
Category
Variables
Ocean variables
Current velocity, water temperature, salinity, free surface elevation
3. CoastalBench
Meteorological forcings
Air pressure, air temperature, air heat flux, precipitation, wind speed, radiation
We release BonstalBench, decade-long, high-resolution dataset designed for modeling complex coastal processes. Table 2 provides an overview of CoastalBench. Coastal- Bench integrates diverse categories of variables, covering a large spatiotemporal domain with fine resolution. This dataset serves as benchmark for developing and evaluating deep learning models for coastal process emulation, physics- informed machine learning, neural operator learning, and spatiotemporal forecasting.
River forcings
River runoff mass transport vertical profile, temperature, salinity Bathymetry, Coriolis parameter, curvilinear coordinate metric
Static features
Variables. As summarized in Table 3, CoastalBench in- cludes key ocean state variables representing the evolving physical conditions of the coastal system. Additionally, it incorporates external forcings, such as meteorological fore- ings and river inflows, which drive regional spatiotemporal variability. Static spatial features, including bathymetry and
The dataset is generated using the Regional Ocean Modeling System (ROMS), a widely used diceesiimnsional hydro- static ocean circulation model designed for high- high-resolution simulations of coastal and regional ocean dynamics. It
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CoastalBench; A Decade-Long Hiig-Resollution Dataset to Emulate Complex Coastal Processes
grid size, are also provided to capture the fixed geographical properties that influence coastal dynamics. These compo- nents together form comprehensive dataset suitable for studying and modeling coastal ocean processes. Additional details on these variables can be found in Appendix A.
speed, surface pressure, air temperature, humidity, and radi- ation fluxes are obtained from the North American Regional Reanalysis (Mesinger et al., 2006), with a temporal reso- lution of 3 hours and a spatial resolution of 0.3 degrees. Freshwater inflows from the three main rivers in the mod- cled area (Myakka River, Peace River, and Caloosahatchee River) are acquired from US Geological Survey flow data'. Bathymetric data are sourced from NOAA's Continuously Updated Digital Elevation Model at 1/9 arc-second reso- lution and the US Geological Survey's s 2018 Lidar Digital Elevation Model (0.5 m horizontal resolution). Before gen- erating the final dataset, the model was calibrated through multiple short-term runs (~-months), with model parameters adjusted using observational data from the River, Estuary, and Coastal Observing Network (RECON)². It is noted that although RECON provides observational data, its sparse spa- tial coverage and irregular sampling make high high-resolution simulations necessary for comprehensive spatiotemporal modeling.
27.15%
600
600
27'N
500
26.85%
400-
300
300
200
100
26.5%M
2oo Gor yourt Cai Cells
$2.57M
82.2'M
81 6/W
(a)
(b)
Figure 1. (a) Grid resolution computed as the square root the area of grid cells (near Charlotte Harbor and Fort Myers). (b) The cumulative distribution of cell count at increasing resolutions.
Spatiotemporal Coverage and Resolution. CoastalBench provides high-frequency simulations with temporal resolu- tion of 30 minutes from 2008 to 2017. The dataset covers the Charlotte Harbor region, an area characterized by dynamic interactions between the atmosphere, land, and open ocean, spanning approximately 800 square kilometers. As shown in Figure 1, the model employs a horizontally non-uniform grid with varying resolution, ranging from ~300 m offshore to I m in estuarine regions and inlets, with an average grid resolution of 88 m. The finest resolution occurs in shallow and estuarine regions because complex land-sea interactions require higher accuracy. Figure lb presents the distribution of grid resolutions. The majority of cells have resolutions below 200 m, with only a small fraction exceeding 500 m. The vertical domain consists of 12 terrain-for follain-following layers, providing resolution of ~011 m in the shallowest regions of the estuary.
To ensure compatibility with deep learning models, post- processing is applied to the simulation outputs. Since certain ocean variables are defined on different grid locations (e.g.. free surface elevation is located at the center of each cell, whereas velocity components are located at the cell bound- aries), they are regridded onto the same coordinate system (center of grid cells). Additionally, atmospheric forcings, which originally have coarser spatiotemporal resolution, are resampled to match the resolution of the simulated ocean variables. After processing. the dataset is structured as a grid of size 898 598 x 12 (height width depth) for each snapshot.
Highlights. The main highlights of CoastalBench are as follows:
High Resolution. CoastalBench provides high- resolution real-world simulation data with a spatial resolution of <100 m, and a temporal resolution of 30 minutes, enabling the study of regional coastal ocean dynamics.
Dataset Construction. The dataset is generated using ROMS, which simulates ocean dynamics based on ini- tial conditions, boundary conditions, and external forcings. The simulation is run in hindcast mode, using high-quality reanalysis data calibrated against historical observations. ROMS is forced at the ocean boundaries with free surface elevation, velocity, water temperature, and salinity obtained from the HYbrid Coordinate Ocean Model with the Naval Research Lab's Coupled Ocean Data Assimilation Global analysis. These forcings have spatial resolution of 1/12 degree and a temporal resolution of 3 hours. For boundary conditions, the Chapman implicit condition is applied to free surface elevation, the Flather condition to depth-averagee currents, and radiation boundary conditions to temperature and salinity. Vertical mixing is parameterized using the Generic Length Scale turbulence closure scheme (Warner et al., 2005). Atmospheric forcings including 10 m wind
Large Scale. The dataset spans decade, covering approximately 800 km3, with over 100,000 time steps and 6 million cells per snapshot.
Unique Structure. Unlike traditional gridded datasets, CoastalBench is based on a non-uniform 3D mesh, which can be used for evaluating deep learning models designed for irregular spatial structures.
Complex Processes. The dataset contains multiple physical processes with all necessary variables, provid- ing a strong benchmark for evaluating machine learn- ing methods for modeling physical processes.
hutss//dashhoard. waterdata.usgs. wwg.saat..uggggooo/. hhtpss//reconnsscfffo

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CoastalBench; A Decade-Long High-Resolltton Dataset to Emulate Complex Coastal Processes
embedding. Each variable x; E RR whether ocean or meteorological, is independently tokenized representa- tion 2, E embedding dimension. Additionally, we inject a learnable vector v, E R for each variable to encode the identity of each variable, which is added to its token embeddings. This yields an intermediate tensor of shape # p P where C and M are the numbers of ocean and meteorologi- cal variables, respectively. To aggregate information across variables, we apply a single-layer cross- coss-attention mecha- nism over the variable dimension, using a learnable query to aggregate the embeddings. The resulting output, denoted as Zugg E R
Posza Embed
XIG;
Target
# x # xd where p is the patch size and d is the
M + ae)
A ttention
Iratill Cars
Blocks
x W. x (C+ d,
X(G
Lead Time Ar
Boundary Conditioons
River Inflow
Positon
Xaclto + dx)
fxove(to dr)
_ x * xd serves as the final patch embedding fed
Figure2 2. Overview of the model architecture.
into the following attention blocks. Our patch embedding module is specifically designed to capture the spatiotempo- ral complexity of regional coastal processes by integrating meteorological forcings alongside ocean variables and mod- eling their interactions within each patch.
4. Approach
As shown in Figure 2. we aim to train a deep learning model that predicts ocean variables at specific lead times, given the initial conditions, boundary conditions, external forcings, and static physical features. We design a simple ViT-based model and treat 3D variables at different depths separate channels, flattening the 3D spatial structure into multiple 2D variables.
Physics-aware positional embedding. Although the stan- dard positional embedding in ViTs can represent the rela- tive positions of image patches, it does not understand the geographical and physical properties critical to modeling coastal processes. To address this, we incorporate static physical features s E R5x where S is the number of physical variables (e.g., ocean depth, grid size, Coriolis parameter), into the positional embedding. These physi- cal features encode essential geophysical information about a specific regional coastal system, enabling the model to better understand the underlying spatiotemporal dynamics. We formalize the physics-aware positional embedding as follows. Let E R " > embedding. The static physical features s are first patchified into embeddings Zphy E R " x xd using standard patch embedding operation. Then we use a cross-attention mecha- nism to allow the physical embeddings Zphy to interact with the standard positional embedding p:
4.1. Model Architecture
We propose a simple ViT-based model customized for emu- lating regional coastal processes. First, instead of the stan- dard patch embedding in ViT, which struggles to capture vertical relationships among variables and fails to model the impacts of meteorological forcings, we tokenize each coastal ocean variable and meteorological variable inddene- dently and aggregate information across them. Second, to address the limitations of standard positional embedding, we incorporate spatially varying but temporally invariant physi- cal features (e.g.. bathymetry, grid size) into the positional embedding, enabling the model to understand critical geo- graphical properties. Finally, the model performs attention conditioned on lead time, boundary conditions, and river in- lead time allows predictions across
W xd denote the standard positional
Pphy = CrrsssAtttetionn Zphy P. P.P),
(3)
This approach ensures that the positional embedding reflects both the spatial layout of the patches and the geophysical properties of the coastal system.
flows. Conditioning multiple time intervals with a single model, while boundary conditions and river inflows capture key interactions with the external environment, which significantly influence the internal processes of the coastal system.
Conditional Attention Block. The model performs atten- tion operations conditioned on key factors influencing the regional coastal system, including lead time, boundary con- ditions, and river inflows. Conditioning on lead time enables the model to predict multiple time intervals with a single model, eliminating the need to train separate models for each lead time, which is particularly beneficial for tasks requiring high temporal resolution. Boundary conditions and river inflows are essential for capturing interactions between the system and its external environment, as these significantly influence the internal processes of the coastal
Patch embedding of initial conditions and meteorologi- cal forcings. Standard patch embedding operation in ViT, designed for image data, has limitations for our task: (1) it fails to model the vertical relationships between variables originally at different depths, and (2) tokenizing coastal ocean variables only ignores the impacts of external mete- orological forcings. To address these issues, inspired by (Nguyen et al., 2023; 2024), we propose a customized patch
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CoastalBench; A Decade-Long Higg-Resollutinn Dataset to Emulate Complex Coastal Processes
system. To incorporate these conditions into the VIT archi- tecture, encode lead time, boundary conditions, and river inflows into embeddings. The overall conditioning vector is obtained by summing the individual embeddings:
For long-lead forecasts, we adopt an autoregressive ap- proach where the model predicts AX iteratively and feeds its output back as input for subsequent steps. However, this iterative strategy introduces cumulative errors over extended lead times. To handle this, we employ an autoregressive training objective (Kurth et al., 2023; Lam et al., 2023) that incorporates multiple rollout steps during training. Specifi- cally, the model is rolled out over K steps during training, and the loss is averaged across these steps to reduce error accunulation:
(4) where Ziead Zbe. and ziverr represent the embeddings for lead time, boundary conditions, and river inflows, respectively. Lead time is transformed into an embedding using a single linear layer. River inflows, represented as features for multi- ple rivers, are flattened into a single vector and encoded with linear layer. For boundary conditions, the four boundary lines of the domain are flattened into ID representations and processed through two 1D convolutional layers followed by a linear layer to extract spatial patterns. These embeddings are then integrated into the attention and feed-forward layers using adaptive layer normalization (adaL.N) (Perez et al., 2018), a widely adopted method in generative models (Kar- ras al., 2019; Peebles & Xie, 2023) and global weather forecasting (Nguyen et al., 2024). The adaLN operation is defined as:
Zjend
+ Zoe + Zriver:
CC H W ΣΣΣΣ k=
A AX
L E
KCHW
where K is the number of rollout steps, C' the number of variables, and H and W are the horizontal dimensions. Our training procedure is divided into two stages. In the first stage, the model is trained to perform one-step predictions. In the second stage, the model is fine- fine-tuned using the multi- step loss, where the same sampled At is used for all steps within each rollout. This two-stage training strategy ensures that the model achieves both short-term accuracy and long- term stability, making it capable of effectively emulating coastal dynamics over various lead times.
AdaLN(Z,, Brco) 7(Zem)) 2 + ((2zo)) )
where Zagy + Pphy. (Zoco)) and (Zcoo) are scale and shift parameters dynamically generated from the con- ditioning vector 7con with linear projection. This dynamic conditioning framework enables the integration of spatial, temporal, and physical context, enriching the model's repre- sentation of coastal dynamics.
4.3. Inference
For inference, the deep learning model is used iteratively, with each forecasted result serving as the input for the next step. To reduce cumulative forecast errors, we adopt the greedy algorithm proposed in (Bi et al., 2023), which selects the largest available lead time model that fits within the remaining prediction horizon. This approach can reduce the number of iterations needed for long-lead forecasts. For instance, with a model capable of predicting intervals of 1, 3, 6, and 12 hours, a 26-hour forecast would require two 12-hour predictions followed by two 1-hour predictions.
4.2. Training Strategy
Accurately predicting coastal ocean variables for arbitrary lead times presents several challenges. Coastal dynamics are inherently complex, requiring high temporal resolution to capture subtle physical interactions. Additionally, long-lead forecasts demand a model capable of maintaining stability over iterative predictions.
Predicting absolute states X(1o+AA can be particularly dif- ficult when the changes between the initial condition X ((o) and the lead time condition relatively small. Therefore, we adopt training strategy (Nguyen et al., 2024; 2023) that predicts the temporal difference AX = AAt)
5. Evaluation
5.1. Experimental Setup
We split the dataset into training, validation, and test sets chronologically. Specifically, the first eight years of data are used for training, while the 9th and 10th years are used for validation and testing, respectively. The model follows the VIT- Base (Dosovitskiy al., 2020) configuration with hid- den dimension of 768, depth of 12, and 12 attention heads, using a patch size of 4. Evaluation metrics include Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (r) to assess prediction accuracy and correlation with ground truth. We evaluate key ocean variables: current velocity (u, v, w), water tempera- ture (temp), salinity (salt, A AKs, and free surface elevation
Moreover, to enhance generalization, we randomly sample At from a predefined set of time intervals during training. This randomized forecasting strategy provides two key ad- vantages: (1) it effectively augments the training data by exposing the model a diverse range of temporal intervals, (2) it enables a single trained model to handle predictions for arbitrary time intervals during inference, eliminating the need to train multiple models for different intervals, and (3) it allows the model to efficiently predict long lead times by leveraging large intervals while maintaining high temporal resolution using smaller intervals.
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CoastalBench; A Decade-Long High-Resolltion Dataset to Emulate Complex Coastal Processes
u (m/s)
v (m/s)
W (m/s)
temp (*C)
salt (PSU)
AKS (m²/s)
zeta (m)
RMSE
0.000550
1.6
0.00430
0.20
5.00425
0.236
0.026
0.000500
0.000525
1.0
1.4
2.00400
0.036
0..004755
0.00375
0.10
0.325
0.0300
0.7
0.00275
MAE
0.9275
0.6
5,00230
0.530
0.0250
0.5
0.5
3.00225
0.10
0.00015
0.5
2.09200
3.92
0.8
0.75
0.50
2.91
0.985
0.89
72 Lead Time (hours)
2,900
24
Figure 3. Overall performance for long-term forecasting (72-hour lead time) with 12-hour prediction interval.
U. (m/s)
V (m/s)
w (m/s)
temp (CC)
salt (PSU)
AKs (m²/s)
zeta (m)
RMSE
3.04
0.035
0.0035
0.100
0.6
0.0030
0.03
0.025
0.4
0.0025
0.0004
0.2
0.025
0.5
4.0
75
11.0
0.5
4.0
7.5
11.0
0.5
4.0
75
11.0
11.0 Lead Time (hours)
05
4.0
75
0.5 4.0
7.5
11.0
0.5
4.0
7.5
11.
0.5
4.0
11.0
Figure 4. Overall performance for short-term forecasting (12-hour lead time) with a 0.5-hour prediction interval.
(Q). Training is conducted on 8 x NVIDIA A100 80GB GPUs using Py PyTorhh's Distributed Data Parallel framework, with a per-GPU batch size of 1. The training process con- sists of two stages: initial training with one-step prediction, followed by fine-tuning with 4 autoregressive steps for improved long-term stability. Lead times sampled from {0.5, 3, 12} hours to enhance generalization across different temporal scales.
smoothly while correlation decreases with lead time. Over- all, the model effectively captures key spatiotemporal pat- terns but struggles with rare events (w) and low-variance fields (lemp), highlighting challenges in modeling complex coastal dynamics.
Computational time costs: We also conducted computa- tional experiments on the inference time of the proposed ViT model. Results show that our ViT model reduces the runtime of ROMS for a 72-hour forecast from 2,477 seconds (with 512 CPU cores on AMD EPYC 7742 64-Core Proces- sors) to 34. 14 seconds (on single A100 GPU), achieving over a 70x speedup.
5.2. Overall Model Performance
Figure 3 and 4 show the model's performance for long-term (72-hour) forecasting with a 12-hour interval and short-term (12-hour) forecasting with a 0.5-hour resolution. Due to page limits, we provide only RMSE results for short-term forecasting (See Appendix for more results). both cases, performance declines with increasing lead time due to accumulated errors. Anomalies appear in the vertical velocity component (w) at 24-hour and 2-hour lead times, where RMSE and MAE deviate from expected trends. This likely results from w being nonzero in only a small fraction of grid cells, making difficult for the model to capture meaningful patterns and leading to low r values. Similarly, temperature (temp) shows low RMSE and MAE but unsta- ble Pearson correlation values, likely due to its low spatial variance, where small absolute emrrr cause significant cor- relation fluctuations. For other variables, errors increase
5.3. Ablation Study
assess the impact of different inputs beyond simulated ocean state variables, we conduct an ablation study by re- moving (a) meteorological forcings, (b) static physical fea- tures, (c) boundary conditions, and (d) river inflow (Fig- ure 5). Excluding metcorological forcings (Figure 5a) sig- nificantly increases RMSE for salinity and temperature at longer lead times, confirming their role in capturing atmospheric-driven variability. Static physical features (Fig- ure 5b) primarily affect temperature and salinity, reinforcing their importance in constraining terrain- derenddde cireu- lation. Removing boundary conditions (Figure 5c) leads to sharp RMSE increases in velocity, emphasizing their

---

CoastalBench; A Decade-Long High-Resolutee Dataset to Emulate Complex Coastal Processes
Im/s)
temp (*C)
salt (PSU)
for complex processes with large-scale dataset.
2.25
Necernl fertees
1.1
2.00
5.5. Case Study
3.0450
1.0
1.50
5.4233
0.9
1.25
To qualitatively evaluate performance, we conduct a case study using January 2016, as the initial condition, gener- ating forecasts for 1, 12, and 24-hour lead times. Figure 7 visualizes temperature and salinity predictions, where rows represent lead times and columns show ground truth (La- bel), model output (Prediction), and the difference between them (Error, computed as Label Prediction). At hour, the model accurately captures spatial patterns with minimal error, especially in dynamic regions. At 12 hours, deviations emerge, particularly near coastal boundaries and estuarine inlets due to accumulated errors. By 24 hours, errors become more pronounced, highlighting the challenge of long-tem term forecasting.
24
60
36 48
do
Load Time (hours)
(a)
u [mVS]
temp (CC)
salt (PSU)
1.3
3.050
RMSE
2.1
3.045
1.0-
1.4
1:2
(hours)
(b)
(m/5)
temp (C)
salt (PSU)
0.32
6. Discussions and Future Works
2.00
0.10
RASE
This paper introduces CoastalBench, decade-long high- resolution dataset for modeling regional coastal processes, along with a Vision Transformer (ViT)-based approach for emulating these complex dynamics. Through extensive evaluations, we demonstrate that our model achieves com- petitive performance. The ablation study highlights the im- portance of meteorological forcings, boundary conditions, and river inflows in improving prediction accuracy, while the scaling test confirms that increasing model capacity hances predictive performance.
1.0-
1.50
0.08
0.9
38
(c)
temp
salt
Oat
1.3
RM1SE
3.2
3.1
3.045
1.5-
3.040
0.9
Despite these promising results, several limitations remain. First, error accumulation over long-term predictions remains challenge, particularly in highly dynamic regions such as estuarine zones and coastal boundaries. Second, due to computational constraints, are unable experiment with larger models such ViT-Large ViT-Huge, fine-tune the model with longer autoregressive steps, which may weaken its long-term forecasting capability. Third, our current deep learning model is relatively simple and lacks architectural modifications specifically designed to handle inputs like external forcings and boundary conditions effectively. Finally, the model is purely data-driven and does not incorporate physical constraints, which may lead to physically inconsistent predictions in certain cases.
12
35
60
50 Lead Time (hours)
24
48
72
12
36
60
72
(d)
Figure 5. Ablation study analyzing the impact of different input variables. (a) Without metcorological forcings, (b) without static physical features, (c) without boundary conditions, and (d) without river inflow.
role in advection processes. Finally, excluding river inflow (Figure 5d) most strongly affects salinity, with noticeable impact on temperature and smaller but evident degradation in velocity.
5.4. Scaling Test
For future work, we plan to expand the dataset to cover additional coastal regions and incorporate data assimilation techniques to enhance realism. Another potential extension of the dataset is to provide ensemble simulations for un- certainty quantification. On the modeling side, we aim explore larger-scal model to assess potential performance gains, well as develop specialized modules to better han- dle the unique inputs of regional coastal models.
To assess the effect of model scaling, we compare three ViT variants: ViT-Tiny (ViT-T), ViT-Small (ViT-S), and ViT-Base (ViT-B), differing in hidden dimension, attention heads, and depth (see Appendix B for details). Figure 6 shows RMSE across key ocean variables, where ViT-B con- sistently outperforms VIT-T and ViT-S. The performance gap widens at longer lead times 44872 hours), indicating that larger models improve predictive accuracy, especially
By releasing CoastalBench, we aim to provide a standard-
8

---

CoastalBench; A Decade-Long Higg-Resollttin Dataset to Emulate Complex Coastal Processes
u (m/s)
v (m/s)
W (m/s)
temp ( C)
salt (PSU)
AKS (m²/s)
zeta (m)
5.000925
0.250
0.265
1.20
0.0046
0.060
1.15
2.0
0. 0.0046
0.225
RMSE
0.255
0.045
3.000550
2.000755
1.10
1.8
1.6
0.00 0.0044
0.0042
0.375
0.200
0.050
0.040
3.000525
14
2.000800
0,0038
0.325
60
36
24
48 60
24 48 60
12
24
48
12 24
60
12
Lead Time (hours)
Figure 6. Effect of model scaling on performance.
Fnd mten
Erer
ized benchmark that helps further advancements in deep learning for coastal modeling, encouraging the broader re- search community to address the identified challenges.
27 22.55
35.79
Acknowledgments
25.59
This material is based upon work supported by the Na- tional Science Foundation (NSF) under Grant No. IIS- 2147908, IIS- OAC-2152085, OAC-2402946, and OAC-2410884.
273
Impact Statement
This work advances the integration of deep learning into coastal ocean modeling by introducing high-resolution simulation dataset tailored for surrogate modeling. Our dataset enables the development of more accurate and scal- able machine learning models for forecasting coastal dynam- ics. These advances have the potential to support critical applications such as storm surge prediction, water quality monitoring, and climate adaptation contributing to the resilience of coastal communities and economies.
(a) Temperature.
20.59
267HH
Nonetheless, as with all data-driven approaches, deep learning-based models remain sensitive to biases in training data and may exhibit uncertainties that impact their reliabil- ity high-stakes scenarios such as disaster response. To address these limitations, rigorous validation and thoughtful integration with physics- based numerical models essen- tial. Furthermore, the computational demands of training such models may pose barriers to accessibility, underscor- ing the importance of open-source tools and datasets to democratize progress in this field.
29357H
Overall, this work contributes to the broader landscape of AT for scientific discovery, highlighting both the opportu- nities and responsibilities involved in applying machine learning to Earth system modeling. Ensuring robust per- formance, transparent evaluation, and collaboration with domain experts will be critical to translating these advances into meaningful societal impact.
(b) Salinity.
Figure 7. Case study visualization of model predictions. (a) perature and (b) salinity forecasts for I, 12, and 2urhour lead times. Each row corresponds to different lead time, while columns rep- resent ground truth (Label), model prediction (Prediction), and the difference between them (Error, computed as Label Prediction).

---

CoastalBench; A Decade-Long High-Resolltion Dataset to Emulate Complex Coastal Processes
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---

CoastalBench; A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
A. Variables in CoastalBench
Category
Abbreviation
Description
Unit m/s m/s m/s "C
Horizontal velocity (east-west)
Horizontal velocity (north-south)
Vertical velocity
Ocean
temp
Water temperature
salt
Water salinity
PSU
AKs
Salinity vertical diffusion coefficient
m²/s
Free surface elevation
m
Pair
Surface air pressure
mbar "C
Tair
Surface air temperature
Qair
Surface air relative humidity
%
Rain
Rainfall rate
kg/m²s W/m ?
Meteorological
swrad
Sun't 's shortwave radiation
twrad
Sun's 's longwave radiation
W/m
Uwind Vwind
Surface u-wind component
m/s
Surface v-wind component
m/s
river transport
River runoff mass transport vertical profile
Scalar "C
River
river temp
River runoff potential temperature
river_salt
River runoff salinity
PSU
h
Bathymetry
f
Coriolis parameter
s
Static
pm
Curvilinear coordinate metric
m
pn
Curvilinear coordinate metric
m
Table Details of variables.
27.15%N
27.15"N
6.65
20
27"N
27'N
26.85
26,85"N
6.60
26.7"N
10
26.7"N
26.55
26.55'N
6.50
26.4"N
0
26.25'N
82.5'W
82.2"W
81.9"W
81.6"W
82.5'W
62.2'W
81.9"W
81.6"W
(a) Bathymetry
(b) Coriolis parameter f.
Figure 8, Visualization of two static features.
As shown in Table 4, CoastalBench includes key ocean state variables including current velocity components (u, v, w for two horizontal and vertical directions), temperature (temp), salinity (salt), salinity vertical diffusion coefficient (AKs), and free-surface elevation (<), providing a detailed view of the evolving state of the coastal system. Additionally, external forcings such as meteorological data including air pressure (Pair), air temperature (Tair), air heat (Qair), precipitation (Rain), sun's s short (swrad), and long wave radiation (lwrad), and river inflows are included to account for atmospheric and hydrological drivers that shape the spatiotemporal variability of the region. Static physical features, as shown in Figure 8 including bathymetry (h), Coriolis parameter (f), and curvilinear coordinate metric (pm and pn), are also incorporated, capturing the fixed geographical properties critical to coastal processes. Together, these components provide a comprehensive view of the coastal environment, ensuring the dataset's s utility for modeling the complex dynamic processes of the system.
12

---

CoastalBench; A Decade-Long High-Resolution Dataset to Emulate Complex Coastal Processes
B. VIT Model Configurations
To assess the impact of model scale, we conducted experiments using three ViT variants: ViT-Tiny, ViT-Small, and ViT-Base. We include the configurations for all three models in Table All models use patch size of 4 and maintain the same input and output.
Model
Hidden Dimension
Depth (Layers)
Attention Heads
ViT-Tiny 192
12
ViT-Small
384
12
6
ViT-Base
768
12
12
Table ViT model configurations used in scaling test.
C. Extra Results
Figure 9 and 10 show the results of short-term (12-hour) forecasting with a 0.5-hour resolution on MAE and Pearson Correlation Coefficient, where performance declines with increasing lead time due to accumulated errors.
(m/s)
(m/s)
W (m/s)
temp ( C)
salt (PSU)
AKs (m²/s)
zeta (m)
0.08
MAF
0.025
0.020
0.00175
0.020
0.80615
0.2
0.04
0.015
0.015
0.00125
0.00100
4.0
7.5
110 Lead Time (hours)
0.5
110
4.0
Figure Overall performance for short-term forecasting (12-hour lead time) with 0.5-hour prediction interval (MAB).
(m/s)
(m/s)
(m/s)
temp (*C)
salt (PSU)
AKs (m3/s)
(m)
0.915
0.7
0.95
0.950
0.850
0.95
0.998
0.85
0.500
0.900
0.80
0.85
0.875
0.80
0.5
7.5
11.0
0.5
7.5
11.0
75
11.0
0.5
7.5
11.0
0.5
7.5
11.0
9.5
7.5
11.0
0.5
7.5
11.0
Lead Time (hours)
Figure Overall performance short-term forecasting (12-hour time) with a 0.5-hour prediction interval (r).
13


Reviews:
[{"review_id": "SX76S6pVS3", "reviewer": "Reviewer_oeQD", "summary": "This paper introduces a decade-long, high-resolution dataset for modeling complex coastal processes in the area of Charlotte Harbor, Florida, USA.  The dataset is generated using a validated numerical model, ROMS. A flexible ViT model is designed to ingest a multitude of diverse data sources (e.g. initial, boundary, external and static conditions, river inflows, etc.) to predict ocean variables at various lead times. The ablation study reveals the importance of incorporating each of these data sources to improve prediction accuracy.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you very much for your valuable review; it is crucial for improving the quality of our manuscript.\n\n**Motivation and applications:** Our problem setting is motivated by the need to efficiently emulate complex coastal processes for practical applications. High-resolution numerical models such as ROMS are computationally expensive, while deep learning offers a fast and scalable alternative. The dataset includes variables critical for key downstream tasks: for example, storm surge and coastal flood forecasting benefit from predictions of free surface elevation; water quality and stratification modeling rely on temperature and salinity; and sediment transport analysis depends on vertical diffusivity.\n\n**Why not use ROMS:** The goal is to emulate ROMS, with the primary motivation being computational efficiency—numerical models like ROMS are significantly slower than deep learning. For example, our experiments show that the proposed model reduces the runtime of ROMS for a 72-hour forecast from 2,477 seconds (using 512 CPU cores) to 34.14 seconds on a single A100 GPU, achieving over a 70× speedup. We have added a detailed runtime benchmark to our manuscript.\n\n**Use of observational data:** We recognize the value of observational data, but they are often limited in spatial and temporal coverage. For instance, RECON provides measurements at sparse, fixed locations and irregular intervals. The ROMS model used in this study was previously validated against RECON observations (Hewageegana et al., 2023), showing strong agreement in key physical processes such as water level variability, currents, salinity, and temperature. This prior validation supports the use of ROMS outputs for training the deep learning model. While our focus is on emulating ROMS rather than evaluating it against observations, we agree that the reliance on ROMS simulations could be considered a limitation. We will clarify this in the main text.\n\nHewageegana, V. H., Olabarrieta, M., & Gonzalez-Ondina, J. M. (2023). Main Physical Processes Affecting the Residence Times of a Micro-Tidal Estuary. Journal of Marine Science and Engineering, 11(7), 1333. \n\n**Expand data to other regions:** Expanding the dataset to other regions is non-trivial. First, it requires region-specific input data such as boundary conditions and external forcings. Second, running high-resolution ROMS simulations is computationally intensive. Third, manual calibration by oceanographers and domain experts is essential to ensure simulation quality. \n\n**Evaluation procedures:** There is no universally accepted standard for determining whether a coastal ocean model is \"sufficiently good\" for practical use, as acceptable error thresholds depend on the specific downstream application (e.g., storm surge vs. long-term climatology). In our case, the goal is only to approximate ROMS outputs as closely as possible. \n\n**Uncertainty:** This is a great point. While forecasting inevitably introduces uncertainty, our dataset is based on a ROMS hindcast calibrated using real-world observations. However, we do not currently provide ensemble simulations or uncertainty quantification. Probabilistic models or metrics are not suitable for this scenario.\n\n**Future inputs:** This is a fair concern. In our setup, the inputs at the target time are assumed to be available from external forecast systems (e.g., global atmospheric forecasts) at coarse resolution. This mirrors real-world practice, where future forcings and boundary conditions are obtained from other (global) forecast models, and regional numerical models take them as input. Making predictions using only initial conditions is not reliable for regional systems due to their sensitivity to future external forcings and boundary conditions. This is actually one of the key motivations for proposing this regional dataset, which differs from global forecasting datasets.\n \n**Dataset split:** Our dataset is indeed split chronologically. Specifically, the first 8 years of data are used for training, while the 9th and 10th years are used for validation and testing.\n\n**Practical information:** Thanks for the suggestion. We have added practical dataset details in the paper. The dataset will be hosted on Hugging Face and provided in standard NetCDF (.nc) format. We also include a compressed version in PyTorch (.pth) format (converted from float64 to float16) for efficient use in ML pipelines. The full dataset is approximately 18 TB. To improve usability, we provide a lightweight subset and a base training script to help users quickly run models.\n\n**Q1:** The current dataset uses manually calibrated ROMS simulations based on observations, which helps ensure general adherence to realism. However, we do not apply data assimilation, and the outputs may not perfectly align with real-world observations.\n\n**Q2:** Apologies for the mistake — this is not absolute error, but the difference computed as $Label - Prediction$. We have corrected this in the manuscript.", "claims_and_evidence": "- The dataset seems to be carefully designed and validated. The ablations clearly show that incorporating all data sources is important for optimal predictability. The network architecture is well motivated and contextualised to prior work.", "methods_and_evaluation_criteria": "I'm missing more discussion on the problem setting/experiments tackled in the paper. Right now, the created dataset is interesting, but the experimentations and evaluation criteria lack motivation.\n- What motivates this problem setting? Why is it a good problem to tackle with deep learning? What are some specific downstream applications of your model? The impact statement reads well, but I'd like to better understand how it links to the specifics (i.e., data, problem setup, evaluation) in this paper.\n- Why can't you simply use ROMS for the same problem (or what's the issue with that)? Is the goal to essentially emulate ROMS? If so, is computational speed the main reason? If yes, please include a runtime benchmark.  \n- Why not include observational data in the dataset? E.g. RECON, which was already used to validate ROMS (see appendix); are there others? How exactly was ROMS validated against observations? How accurate are its simulations? If the reliance on ROMS simulations is a limitation (it seems to me), can you explicitly mention it in the main text?\n- The data only covers the region of Charlotte Harbor, Florida, USA. How complicated/feasible is it to expand it to other regions (or provide the necessary tools to users to expand it themselves to regions they're interested in)? \n- Is the current model, or its errors, sufficiently good already to be useful in practice? If not, which evaluation procedures/scores could enlighten potential users of the benchmark when this is the case? Is there any uncertainty in the data, i.e. would probabilistic models/metrics be potentially well-suited for this benchmark?\n- I don't understand why the boundary conditions, meteorological forcings, and river inflow inputs are all from the \"future\" (i.e. the same timestep, $t_0+\\Delta t$, for which you aim to predict the coastal ocean variables). How can this be useful in practice? Won't this make it impossible to perform real-world predictions (since you'd need to wait for the input data at time $t_0+\\Delta t$ before running your model)? \n- Is the 8:1:1 train/val/test split random? For temporal prediction tasks, it's recommended to split by time.", "theoretical_claims": "There are no such claims.", "experimental_designs_or_analyses": "Except for my aforementioned concerns/questions on the experimental setup (see the Methods and Evaluation Criteria section), the training and evaluation metrics/analysis seem sound to me.", "relation_to_broader_scientific_literature": "I am unfamiliar with the literature on coastal circulations/ecosystems, so this is hard for me to judge. From the perspective of the dataset itself and its structure, I think that these are potentially interesting to a broader community (see the Strengths section).", "essential_references_not_discussed": "n/a", "other_strengths_and_weaknesses": "Strengths:\n\nThe dataset structure is quite interesting, including various data sources and dimensionalities. It makes it an interesting problem to design an appropriate architecture that can ingest all these sources. The authors introduce an adaptation of ViT that's suitable for this data. Its design, especially for how to condition the model on various forcings, is very well motivated and makes sense. The design is quite general and could be useful for different problems/datasets. The ablations show clearly that incorporating all these forcings boosts performance.\n\nWeaknesses:\n\n1. See the Methods And Evaluation Criteria section.\n2. There's little practical information on the dataset that would be important for potential users. (How) Does it adhere to FAIR principles? What format do you use? Was any postprocessing done? Where will the data be hosted? Given the size/diversity of the data, did you take any steps to make it easier for ML practitioners to download it and easily get a simple model running? I'd recommend the authors to take a look at the NeurIPS call for datasets and benchmarks and ensure that such key pieces of information (for a benchmark dataset) are included in the paper/supplementary.", "other_comments_or_suggestions": "n/a\n\n****** After rebuttal: Updated score from 2 -> 3", "questions_for_authors": "- What do you mean by *\"we plan to expand the dataset to cover additional coastal regions and incorporate data assimilation techniques to enhance realism\"*? How is the current data lacking realism? What's missing?\n- Why does the colorbar in your absolute error plots (Fig. 7, right column) start at negative values? Can you fix this?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tpVmZY4TIn", "reviewer": "Reviewer_faRw", "summary": "This paper provides a large-scale, high-resolution coastal simulation dataset to train and evaluate deep learning models. The dataset contains various oceanography variables alongside external atmospheric and river forcings. Then, the author proposes a customized ViT model that takes initial and boundary conditions and external forcings as input and predicts ocean variables at varying lead times. The model achieves competitive performance.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your comments and thoughtful questions. Below are our responses:\n\n**Comparison with existing datasets:** We include a comparison table of representative works on regional coastal ocean modeling, highlighting that existing datasets are generally smaller in scale and focus on simpler processes with limited variable coverage.\n| Dataset Source & Reference                     | Region/Domain          | Spatial Resolution     | Temporal Resolution | Time Span               | Variables                             |\n|------------------------------------------------|------------------------|------------------------|---------------------|--------------------------|----------------------------------------|\n| Kumar & Leonardi (2023)                  | Morecambe Bay, UK      | ~1 km             | Hourly              | 500 storm simulations     | Wave height, depth, sediment           |\n| Ishida et al. (2020)| Japan          | 0.25° (~25 km)         | 1 hour              | 40 years (1979–2019)     | Wind speed, pressure             |\n| Melo et al. (2021)                        | Idealized XBeach | ~1–10 m                | 10 min              | 7 days                   | Bed level, flow, sediment              |\n| Wei et al. (2021)| South China Sea        | ~1 km                  | Hourly              | 1 year       | Wave fields, wind speed                      |\n| O'Donncha et al. (2019)| U.S. West Coast | ~1–2 km           | Hourly              | 1 year                   | Wave fields, wind speed                     |\n| **CoastalBench (Ours)** | Charlotte Harbor, USA  | ~100 m | 30 min            | 10 years (2008–2017)     | Wave fields, temperature, salinity, air pressure, temperature, humidity, rainfall, sun radiation, wind speed, etc. |\n\nKumar, P., & Leonardi, N. (2023). A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App.\n\nIshida, K., et al. (2020). Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network. \n\nMelo, C. B., et al. (2023). Coastal morphodynamic emulator for early warning short-term forecasts. \n\nWei, Z., et al. (2022). A convolutional neural network based model to predict nearshore waves and hydrodynamics.  \n\nO'Donncha, F., et al. (2019). Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves. \n\n\n**Baselines:** Thank you for raising this important point. We agree that baselines are important. To our knowledge, no existing deep learning method is specifically designed for complex regional coastal processes, so we plan to include a 3D U-Net as a baseline in the final version. Due to the large scale of our dataset, we were unable to complete this comparison for the rebuttal. If there are specific baselines you recommend, we welcome your suggestions and will aim to include them.\n\n**Physics-aware positional embedding:** We have provided the ablation study to show the effectiveness of incorporating the physics information into positional embedding, as shown in Figure 5(b).\n\n**Dataset summary table:** Thank you for the suggestion. We added the following dataset summary table to clearly describe key properties of the dataset: \n\n| Property                  | Description|\n|--|--------|\n| **Temporal Coverage**     | 2008–2017 (10 years)|\n| **Temporal Resolution**   | 30 minutes|\n| **Spatial Domain**        | Charlotte Harbor, Florida, USA (~800 km²)|\n| **Grid Type**             | Non-uniform 3D curvilinear mesh|\n| **Grid Dimensions**       | 898 (lat) × 598 (lon) × 12 (vertical levels)|\n| **Average Horizontal Resolution** | ~120 m × 100 m|\n| **Number of Variables**   | 22 (See Table 2 for details)|\n| **Data Format**           | NetCDF|\n| **Total Volume**          | ~18 TB|\n| **Simulation Model**      | Regional Ocean Modeling System (ROMS)|\n\n\n**Other comments or suggestions:** Thanks for your suggestions. We have corrected the typo on Page 4, clarified the notations and descriptions following Equation (5), and added the missing citations in the “Dataset Construction” section on Page 3.", "claims_and_evidence": "The claims made in the submission are not all supported by evidence.\n\n1. The authors claim existing studies focus on small datasets and simple processes. They should include a thorough comparison with pervious datasets, highlighting their contributions and how their dataset is unique from others (preferably in a tabular format). \n\n2. This paper propose a physics-aware positional embedding, which is uncommon. Experiments are needed to prove the validity of this design.", "methods_and_evaluation_criteria": "The proposed methods and evaluation criteria make sense for the application.", "theoretical_claims": "There is no proof of theoretical claims in this submission.", "experimental_designs_or_analyses": "I checked the soundness/validity of experimental designs. The main problem is that baselines with numerical methods and existing deep learning methods on your dataset are missing, making it unclearly how well the proposed method is.", "relation_to_broader_scientific_literature": "The dataset is constructed via existing simulation method ROMS (Shchepetkin & McWilliams, 2005), and some variables (e.g., atmospheric forcings were obtained from the North American Regional Reanalysis (NARR) (Mesinger et al., 2006).", "essential_references_not_discussed": "Essential references are included.", "other_strengths_and_weaknesses": "Strengths:\n1. Large-scale dataset solving the problem of lack of large-scale public dataset\n\nWeakness:\n1. The description of the dataset is not clear or comprehensive. Apart from Table 1, a dataset summary table detailing the dataset volume, data shape, format, number of variables, etc. is missing.\n\n2. Key experimental results are missing. Moreover, a more comprehensive analysis of both the dataset and the proposed framework will be helpful.\n\n3. The writing and organization of the paper should be improved.", "other_comments_or_suggestions": "1. A small typo on Page4 Line193 left-side: \"which **an** be used\"\n\n2. Descriptions after Equation 5 (Page 4) are hard to follow, due to some notation missing or abusing.\n\n3. On Page 3, right side, under \"Dataset Construction,\" all citations are missing. These references appear in the Supp. but are missing from the main text.", "questions_for_authors": "Both as part of External Forcings, why does your deep learning model treat Meteorological Forcings and River Inflow differently? Will it significantly influence the model performance if the input Meteorological Forcings are used as an additional condition?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hAjqWzEyQ5", "reviewer": "Reviewer_NvM4", "summary": "This paper mainly focuses on the dataset construction of simulating coastal processes via the Regional Ocean Modeling System (ROMS), considering ocean, meteorological, river and static variables. The work builds a ViT-based (Vision Transformer) network to use the dataset for coastal ocean variable prediction.\n\n## update after rebuttal\nThanks for the responses about my question, and I upgrade my rating. I also suggest to provide the comprehensive literature with the analyses and comparisons to the proposed method, if it's finally accepted.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful feedback and hope our responses clarify the concerns.\n\n**Related Models:** Thank you for raising this important point. We agree that baselines are important. To our knowledge, no existing deep learning method is specifically designed for complex regional coastal processes, so we plan to include a 3D U-Net as a baseline in the final version. Due to the large scale of our dataset, we were unable to complete this comparison for the rebuttal. If there are specific baselines you recommend, we welcome your suggestions and will aim to include them.\n\n**Related Datasets:** We include a comparison table of representative works on regional coastal ocean modeling, highlighting that existing datasets are generally smaller in scale and focus on simpler processes with limited variable coverage.\n| Dataset Source & Reference                     | Region/Domain          | Spatial Resolution     | Temporal Resolution | Time Span               | Variables                             |\n|------------------------------------------------|------------------------|------------------------|---------------------|--------------------------|----------------------------------------|\n| Kumar & Leonardi (2023)                  | Morecambe Bay, UK      | ~1 km             | Hourly              | 500 storm simulations     | Wave height, depth, sediment           |\n| Ishida et al. (2020)| Japan          | 0.25° (~25 km)         | 1 hour              | 40 years (1979–2019)     | Wind speed, pressure             |\n| Melo et al. (2021)                        | Idealized XBeach | ~1–10 m                | 10 min              | 7 days                   | Bed level, flow, sediment              |\n| Wei et al. (2021)| South China Sea        | ~1 km                  | Hourly              | 1 year       | Wave fields, wind speed                      |\n| O'Donncha et al. (2019)| U.S. West Coast | ~1–2 km           | Hourly              | 1 year                   | Wave fields, wind speed                     |\n| **CoastalBench (Ours)** | Charlotte Harbor, USA  | ~100 m | 30 min            | 10 years (2008–2017)     | Wave fields, temperature, salinity, air pressure, temperature, humidity, rainfall, sun radiation, wind speed, etc. |\n\nKumar, P., & Leonardi, N. (2023). A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App.\n\nIshida, K., et al. (2020). Hourly-scale coastal sea level modeling in a changing climate using long short-term memory neural network. \n\nMelo, C. B., et al. (2023). Coastal morphodynamic emulator for early warning short-term forecasts. \n\nWei, Z., et al. (2022). A convolutional neural network based model to predict nearshore waves and hydrodynamics.  \n\nO'Donncha, F., et al. (2019). Ensemble model aggregation using a computationally lightweight machine-learning model to forecast ocean waves.", "claims_and_evidence": "The dataset is evaluated by customizing a ViT model, but more related models are not considered for comparison, thus the claims may not be convincing enough.", "methods_and_evaluation_criteria": "The evaluation is only conducted with the given ViT-based model without considering more related works about the oceanic and atmospheric variable prediction, so it's not convincing enough to evaluate the contributions of the dataset and model.", "theoretical_claims": "This is not available for this work.", "experimental_designs_or_analyses": "The experiments and analyses are conducted on the constructed dataset and the customized ViT-based model, but there is no comparison, thus the evaluation is insufficient to validate the contributions of this work.", "relation_to_broader_scientific_literature": "It's helpful to build the dataset for oceanic and atmospheric variable prediction.", "essential_references_not_discussed": "The oceanic and atmospheric variable prediction as well as the related datasets should be considered for discussion and comparison.", "other_strengths_and_weaknesses": "- The contributions are not well reflected in the current version.\n- The originality is about the dataset but there is no comprehensive validation on it, thus the significance is not convincing.\n- For the dataset, it's better to be validated via the other popular models for the correctness and soundness.", "other_comments_or_suggestions": "It's better to reconsider the work with comprehensive literature review.", "questions_for_authors": "What are the major differences compared to similar related works from the perspective of contributions?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces a data set with calculations from regional coastal circulation models. The data set covers a decade-long timescale with 3 temporal resolution of 30 minutes and spatial resolution of about 100 m (on average). The paper also provides preliminary results using a Transformer model. My impression from reading the paper, the reviews and the rebuttal discussions is that the contribution is meaningul and the paper is solid. I think the rebuttal has addressed the main questions by the reviewers and those who have engaged in the discussion have raised the score to acceptance. I largely agree with their assessment and I believe this contribution would be appreciated by part of the ICML audience. For these reasons, I recommend the acceptance of the submissions. Meanwhile, I strongly encourage the authors to incorporate some elements from the rebuttal discussion, such as certain clarifications and answers to questions and the comparison with existing data sets.
Decision: Accept (poster)