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
61
| vorticity_trajectory
listlengths 61
61
| alpha
float64 0.05
0.6
| beta
float64 0.02
0.3
| lat2
float64 0.53
2.59
| R
float64 1
1
| Omega
float64 0.26
0.26
| nu
float64 0
0
| g
float64 19.9
19.9
| H
float64 0
0
| Nphi
int64 128
128
| Ntheta
int64 64
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Shallow Water Equations Dataset
Numerical solutions to the viscous shallow water equations on a sphere using spectral methods.
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 pairsphi_coords: (Nphi, Ntheta) - Longitude gridtheta_coords: (Nphi, Ntheta) - Colatitude gridlat_coords: (Nphi, Ntheta) - Latitude gridtime_coordinates: (time_steps,) - Time points in hours
Initial Conditions
u_initial: (2, Nphi, Ntheta) - Initial velocity fieldh_initial: (Nphi, Ntheta) - Initial height perturbationvorticity_initial: (Nphi, Ntheta) - Initial vorticity field
Solution Trajectories
u_trajectory: (time_steps, 2, Nphi, Ntheta) - Velocity field evolutionh_trajectory: (time_steps, Nphi, Ntheta) - Height field evolutionvorticity_trajectory: (time_steps, Nphi, Ntheta) - Vorticity field evolution
Physical Parameters
alpha: Perturbation width parameterbeta: Perturbation latitude scale parameterR: Earth radius (6.37122×10⁶ m)Omega: Earth rotation rate (7.292×10⁻⁵ rad/s)nu: Kinematic viscosityg: Gravitational acceleration (9.80616 m/s²)H: Mean fluid height (10⁴ m)
Grid Parameters
Nphi: Number of longitude pointsNtheta: 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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