Multi-Parameter Assessment Protocol for Tropical Cyclone Rapid Intensification
Vortex Framework is a Multi-Parameter Assessment Protocol for Tropical Cyclone Rapid Intensification (RI) prediction. This comprehensive computational framework integrates 8 critical meteorological and oceanographic parameters to provide accurate forecasts of tropical cyclone intensification.
Comprehensive assessment combining ocean, atmospheric, and structural factors for RI prediction.
Enhanced time-stepping algorithms provide rapid intensity forecasts up to 24 hours.
Supports Atlantic, Pacific, and Indian Ocean basins with validated methodologies.
Complete installation guide for all platforms
pip install vortex-by-gitdeeper
git clone https://gitlab.com/gitdeeper3/vortex.git
cd vortex
pip install -e .
# Update packages
pkg update && pkg upgrade
# Install Python and dependencies
pkg install python python-pip git
# Install core dependencies
pip install numpy pyyaml
# Clone and install vortex
git clone https://gitlab.com/gitdeeper3/vortex.git
cd vortex
pip install -e .
# Install prerequisites
sudo apt-get update
sudo apt-get install python3 python3-pip git
# Install vortex
git clone https://gitlab.com/gitdeeper3/vortex.git
cd vortex
pip3 install -e .
# Install Homebrew if not present
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install Python
brew install python3 git
# Install vortex
git clone https://gitlab.com/gitdeeper3/vortex.git
cd vortex
pip3 install -e .
# Install Python from python.org or Microsoft Store
# Clone repository
git clone https://gitlab.com/gitdeeper3/vortex.git
cd vortex
# Install
pip install -e .
# Test import
python -c "import vortex; print('✅ Vortex installed successfully')"
# Run test suite
python run_tests.py
# Expected output: ✅ 14/14 tests passed
Get up and running in minutes
from src.algorithms.time_stepping import TimeStepper
from src.core.vortex_engine import VortexEngine
# Initialize engine for Atlantic basin
engine = VortexEngine(basin="atlantic")
# Create forecaster
forecaster = TimeStepper()
# Generate forecast
forecast = forecaster.forecast_intensity(
initial_intensity_kt=75.0,
mpi_trajectory=[80.0, 85.0, 90.0, 95.0],
environmental_trends={
"vws": [12.0, 10.0, 8.0, 6.0],
"sst": [28.5, 28.8, 29.0, 29.2]
},
time_horizon_hours=24
)
# Display results
print(f"RI Probability: {forecast['ri_probability_time_series'][-1]:.1%}")
from vortex.core.vortex_engine import VortexEngine
from vortex.algorithms.time_stepping import EnhancedTimeSteppingForecast
# Initialize with custom configuration
engine = VortexEngine(
basin="atlantic",
config_file="config/custom_config.yaml"
)
# Initialize enhanced forecaster
forecaster = EnhancedTimeSteppingForecast()
# Generate detailed forecast
forecast = forecaster.forecast_intensity(
initial_intensity_kt=75.0,
mpi_trajectory=[80.0, 85.0, 90.0, 95.0],
environmental_trends={
"vws": [12.0, 10.0, 8.0, 6.0],
"sst": [28.5, 28.8, 29.0, 29.2],
"rh_mid": [65.0, 68.0, 70.0, 72.0],
"ohc": [55.0, 60.0, 65.0, 70.0]
},
time_horizon_hours=24,
ensemble_members=20
)
# Access detailed results
print(f"RI Probability: {forecast['ri_probability_time_series'][-1]:.1%}")
print(f"Forecast Intensity: {forecast['intensity_forecast'][-1]:.1f} kt")
print(f"Confidence: {forecast['confidence_interval']}")
Core physical parameters driving Rapid Intensification in tropical cyclones
| # | Parameter | Symbol | Description | Role in RI |
|---|---|---|---|---|
| 1 | Ocean Heat Content | OHC |
Thermal energy in upper ocean layers | Primary energy source |
| 2 | Eyewall Symmetry | σ_sym |
Structural organization index | Indicates mature structure |
| 3 | Vertical Wind Shear | VWS |
850-200 hPa wind difference | Major inhibiting factor |
| 4 | Mid-Level Humidity | RH_mid |
700-500 hPa relative humidity | Supports convection |
| 5 | Low-Level Vorticity | ζ_850 |
850 hPa relative vorticity | Initial spin-up indicator |
| 6 | Convective Organization | Org_conv |
Inner-core convection coherence | Latent heat release efficiency |
| 7 | Outflow Efficiency | Eff_outflow |
Upper-level ventilation capacity | Removes rising air mass |
| 8 | Intensity Trend | ΔInt_trend |
6-12h intensity change patterns | Recent momentum indicator |
FAVORABLE_CONDITIONS = {
"OHC": ">50 kJ/cm²",
"VWS": "<10 kt",
"RH_mid": ">60%",
"ζ_850": ">1.5×10⁻⁵ s⁻¹",
"σ_sym": ">0.7",
"Org_conv": ">0.6",
"Eff_outflow": ">0.5",
"ΔInt_trend": "positive"
}
Modular and extensible framework design
vortex/
├── src/ # Source code
│ ├── algorithms/ # Numerical methods
│ ├── core/ # Core engine
│ ├── data/ # Data handling
│ └── utils/ # Utilities
├── data/ # Data directory
├── config/ # Configuration files
├── tests/ # Test suite
├── reports/ # Generated reports
├── examples/ # Usage examples
└── docs/ # Documentation
Main forecasting engine that coordinates all components and manages basin-specific configurations.
Advanced numerical methods for intensity forecasting with ensemble capabilities.
Handles input data processing, validation, and formatting for forecast generation.
Automated creation of comprehensive forecast reports with visualizations.
Available numerical methods and forecasting techniques
Best for: Operational forecasting, real-time applications, single deterministic forecast
from src.algorithms.time_stepping import TimeStepper
Best for: Research applications, ensemble forecasting, uncertainty quantification
from vortex.algorithms.time_stepping import EnhancedTimeSteppingForecast
Complete API documentation for all modules
Main forecasting engine
class VortexEngine:
"""
Main forecasting engine for tropical cyclone RI prediction
Args:
basin (str): Ocean basin ('atlantic', 'pacific', 'indian')
config_file (str, optional): Path to configuration file
"""
def __init__(self, basin, config_file=None):
pass
def forecast(self, **kwargs):
"""Generate RI forecast"""
pass
Time-stepping forecast algorithms
class EnhancedTimeSteppingForecast:
"""
Enhanced time-stepping forecaster with ensemble capabilities
"""
def forecast_intensity(
self,
initial_intensity_kt,
mpi_trajectory,
environmental_trends,
time_horizon_hours,
ensemble_members=1
):
"""
Generate intensity forecast
Returns:
dict: Forecast results with RI probabilities
"""
pass
RI is defined as an increase of maximum sustained wind speed ≥30 kt (≥55 km/h) in 24 hours. This phenomenon is one of the most challenging aspects of tropical cyclone forecasting.
Currently supports Atlantic, Eastern Pacific, Western Pacific, and North Indian Ocean basins with validated methodologies for each region.
SST data, atmospheric reanalysis (wind shear, humidity), satellite-derived parameters for eyewall structure, and ocean heat content measurements.
Yes, the framework is designed for operational use with real-time data inputs. It provides forecasts within seconds, making it suitable for operational centers.
# Solution: Reinstall in development mode
cd /path/to/vortex
pip install -e .
# Solution: Update NumPy
pip install --upgrade numpy>=1.21.0
# Solution: Check permissions and disk space
ls -la reports/
df -h
Thermal energy in the upper ocean layers, critical for cyclone intensification. Measured in kJ/cm².
850-200 hPa wind difference. Strong shear (>15 kt) inhibits development by disrupting storm structure.
Theoretical maximum intensity based on environmental conditions, primarily SST and atmospheric stability.
Measure of structural organization. Higher values (>0.7) indicate favorable conditions for intensification.
Likelihood of rapid intensification occurrence in the forecast period, expressed as percentage.
Numerical method advancing forecasts in discrete time intervals, accounting for environmental evolution.
If you use Vortex in your research, please cite:
@software{vortex_framework_2026,
author = {Baladi, Samir},
title = {Vortex Multi-Parameter Assessment Protocol for Tropical Cyclone Rapid Intensification},
year = {2026},
publisher = {GitLab},
version = {0.2.0},
url = {https://gitlab.com/gitdeeper3/vortex},
doi = {10.5281/zenodo.xxxxxxx}
}
@software{vortex_pypi_2026,
author = {Baladi, Samir},
title = {vortex-by-gitdeeper: Python package for tropical cyclone RI forecasting},
year = {2026},
publisher = {PyPI},
url = {https://pypi.org/project/vortex-by-gitdeeper/},
version = {0.2.0}
}
When reporting issues, include: