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spatial_coordinates
listlengths
8.39k
8.39k
phi_coords
listlengths
128
128
theta_coords
listlengths
1
1
lat_coords
listlengths
128
128
time_coordinates
listlengths
61
61
u_initial
listlengths
2
2
h_initial
listlengths
128
128
vorticity_initial
listlengths
128
128
u_trajectory
listlengths
61
61
h_trajectory
listlengths
61
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vorticity_trajectory
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61
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alpha
float64
0.05
0.6
beta
float64
0.02
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lat2
float64
0.53
2.59
R
float64
1
1
Omega
float64
0.26
0.26
nu
float64
0
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g
float64
19.9
19.9
H
float64
0
0
Nphi
int64
128
128
Ntheta
int64
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End of preview. Expand in Data Studio
YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

Shallow Water Equations Dataset

Numerical solutions to the viscous shallow water equations on a sphere using spectral methods.

Sample Plot

Equations

The dataset simulates the viscous shallow water equations on a rotating sphere:

Momentum equation:

∂u/∂t + ν∇⁴u + g∇h + 2Ω × u = -u·∇u

Continuity equation:

∂h/∂t + ν∇⁴h + H∇·u = -∇·(hu)

Where:

  • u is the velocity field (2-component vector)
  • h is the height perturbation field
  • ν is kinematic viscosity
  • g is gravitational acceleration
  • Ω is the Earth's rotation vector
  • H is the mean fluid height

Variables

The dataset returns a dictionary with the following fields:

Coordinates

  • spatial_coordinates: (Nphi×Ntheta, 2) - Flattened (phi, theta) coordinate pairs
  • phi_coords: (Nphi, Ntheta) - Longitude grid
  • theta_coords: (Nphi, Ntheta) - Colatitude grid
  • lat_coords: (Nphi, Ntheta) - Latitude grid
  • time_coordinates: (time_steps,) - Time points in hours

Initial Conditions

  • u_initial: (2, Nphi, Ntheta) - Initial velocity field
  • h_initial: (Nphi, Ntheta) - Initial height perturbation
  • vorticity_initial: (Nphi, Ntheta) - Initial vorticity field

Solution Trajectories

  • u_trajectory: (time_steps, 2, Nphi, Ntheta) - Velocity field evolution
  • h_trajectory: (time_steps, Nphi, Ntheta) - Height field evolution
  • vorticity_trajectory: (time_steps, Nphi, Ntheta) - Vorticity field evolution

Physical Parameters

  • alpha: Perturbation width parameter
  • beta: Perturbation latitude scale parameter
  • R: Earth radius (6.37122×10⁶ m)
  • Omega: Earth rotation rate (7.292×10⁻⁵ rad/s)
  • nu: Kinematic viscosity
  • g: Gravitational acceleration (9.80616 m/s²)
  • H: Mean fluid height (10⁴ m)

Grid Parameters

  • Nphi: Number of longitude points
  • Ntheta: Number of latitude points

Dataset Parameters

  • Domain: Spherical surface (φ ∈ [0, 2π], θ ∈ [0, π])
  • Default grid: 256×128 (longitude × latitude)
  • Time range: 0 to 360 hours (default)
  • Spatial resolution: Spectral (dealiased at 3/2 factor)
  • Temporal resolution: 600 seconds (10 minutes)
  • Save interval: Every 5 hours (default)

Physical Parameters

  • Earth radius: R = 6.37122×10⁶ m
  • Rotation rate: Ω = 7.292×10⁻⁵ rad/s
  • Viscosity: ν = 1×10⁵ m²/s / 32² (Reynolds number dependent)
  • Gravity: g = 9.80616 m/s²
  • Mean height: H = 10⁴ m
  • Jet maximum velocity: 80 m/s
  • Perturbation amplitude: 120 m

Random Parameters (per sample)

  • Alpha range: [0.05, 0.6] - Controls perturbation width in longitude
  • Beta range: [0.02, 0.3] - Controls perturbation width in latitude

Physical Context

This dataset simulates atmospheric or oceanic flows on a rotating sphere governed by the shallow water equations. The system models large-scale geophysical fluid dynamics, including:

  • Zonal jet streams with realistic velocity profiles
  • Geostrophic balance between pressure gradients and Coriolis forces
  • Rossby waves and other planetary-scale motions
  • Vorticity dynamics in rotating reference frames

Each sample begins with a balanced zonal jet (resembling atmospheric jet streams) perturbed by a localized height anomaly. The evolution showcases:

  • Jet instability and wave breaking
  • Vortex formation and interaction
  • Energy cascades in geophysical turbulence
  • Conservation of potential vorticity

This dataset is relevant for:

  • Atmospheric and oceanic modeling
  • Climate dynamics
  • Geophysical fluid dynamics research
  • Weather prediction algorithms
  • Machine learning for Earth system modeling

Usage

from dataset import ShallowWaterDataset

# Create dataset
dataset = ShallowWaterDataset(
    Nphi=256,           # Longitude points
    Ntheta=128,         # Latitude points
    stop_sim_time=360,  # Hours
    save_interval=5     # Save every 5 hours
)

# Generate a sample
sample = next(iter(dataset))

# Access solution data
u_evolution = sample["u_trajectory"]      # Velocity field
h_evolution = sample["h_trajectory"]      # Height field
vorticity = sample["vorticity_trajectory"] # Vorticity field
times = sample["time_coordinates"]        # Time points
alpha, beta = sample["alpha"], sample["beta"] # Parameters

Visualization

Run the plotting scripts to visualize samples:

python plot_sample.py      # 4-panel static visualization on sphere
python plot_animation.py   # Animated vorticity evolution

The plotting functions create 3D spherical visualizations showing the evolution of vorticity fields over time, with proper Earth-like pole orientation.

Data Generation

Generate the full dataset:

python generate_data.py

This creates train/test splits saved as chunked parquet files in the data/ directory. The generation uses smaller default parameters (128×64 grid, 100-hour simulations) for efficiency while maintaining the same physics.

File Structure

datasets-shallow-water-dedalus/
├── dataset.py              # Main dataset class
├── plot_sample.py           # 4-panel spherical visualization
├── plot_animation.py        # Animated GIF generation
├── generate_data.py         # Dataset generation script
├── requirements.txt         # Python dependencies
├── README.md               # This file
└── sample_plot.png         # Example visualization
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