arcat 0.1.0

Atmospheric River Categorization Toolkit

ARCat: Atmospheric River Categorization Toolkit

ARCat is a Python package for categorizing Atmospheric River (AR) events based on Integrated Vapor Transport (IVT) data. It provides:

• Event-based AR categorization: Collapses each AR event to its peak intensity.
• Evolution-based AR categorization: Preserves the intensity evolution within AR events.

Additionally, ARCat computes:

• Cumulative IVT per AR event.
• IVT-only values for each AR event.
• Duration-only values (number of timesteps per event).

It is fully compatible with NumPy arrays, xarray, and Dask, allowing analysis of large datasets efficiently.

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Features

• Event-based and evolution-based AR categorization.
• Automatic handling of duration rules:
  • Events < 1 day → downgrade category
  • Events 1–2 days → unchanged
  • Events ≥ 2 days → upgrade category
• Maximum category enforcement.
• Option to extract IVT-only and duration-only arrays.
• Easy integration with xarray/Dask for large datasets.
• Fully documented and tested, with CI support.

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Installation

Clone the repository and install locally:

git clone https://github.com/yourusername/arcat.git
cd arcat
pip install -e .

Quick Start

Import the package

import numpy as np
from arcat.core import AR_categorization_scheme, AR_categorization_evolution_scheme

Prepare your IVT array

# Example: random IVT values for demonstration
ivt = np.random.rand(20) * 1500  # IVT in kg/m/s

Event-based AR categorization

# Run the event-based AR scheme
final_cat, cum_ivt, ivt_event, duration_event = AR_categorization_scheme(
    ivt,
    time_resolution_hours=6,  # Data resolution in hours
    bin_width=250.0,           # IVT bin size
    max_category=6             # Maximum AR category
)

print("Categorized AR events:", final_cat)
print("Cumulative IVT:", cum_ivt)
print("IVT-only values:", ivt_event)
print("Duration per timestep:", duration_event)

Evolution-based AR categorization

# Run the evolution-based AR scheme
final_cat, cum_ivt, ivt_event, duration_event = AR_categorization_evolution_scheme(
    ivt,
    time_resolution_hours=6,  # Data resolution in hours
    bin_width=250.0,           # IVT bin size
    max_category=6             # Maximum AR category
)

print("Evolution categories:", final_cat)
print("Cumulative IVT:", cum_ivt)
print("IVT-only values:", ivt_event)
print("Duration per timestep:", duration_event)

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Parameters

Parameter	Description
ivt_array	1D NumPy array of IVT values (kg/m/s)
time_resolution_hours	Temporal resolution of your data in hours (default: 6)
bin_width	Width of each IVT bin for categorization (default: 250)
max_category	Maximum AR category (default: 6)

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Returns

1. final_categories: Array of AR categories (1–6) after duration adjustments.
2. cumulative_ivt: Cumulative IVT during AR events.
3. ivt_event: Original IVT values during AR events (0 elsewhere).
4. duration_event: Number of timesteps per AR event (0 elsewhere).

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Usage Notes

• Event-based scheme collapses each AR event to its maximum intensity (max_bounded_replace).
• Evolution-based scheme preserves temporal evolution of IVT within AR events.
• Duration rules are applied per continuous AR segment.
• Works with any NumPy array, and can be integrated with xarray or Dask arrays for large datasets.

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Adopting for xarray data

final_cat, cum_ivt, ivt_event, duration_event = xr.apply_ufunc(AR_categorization_scheme, 
                             Ivt_ds['IVT'].as_numpy(),
                             input_core_dims = [['time']],
                             output_core_dims = [['time'],['time'],['time'],['time']],
                             vectorize=True,  # Auto-vectorize over lat/lon
                             dask='parallelized',  # Enable parallelization using Dask
                             output_dtypes=[np.int8,np.float32,np.float32, np.int8])

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a new branch: git checkout -b feature-name
3. Make your changes
4. Run tests
5. Submit a pull request

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License

This project is licensed under the Creative Commons License.

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References

• Ralph, F. M., Rutz, J. J., Cordeira, J. M., Dettinger, M., Anderson, M., Reynolds, D., ... & Smallcomb, C. (2019). A scale to characterize the strength and impacts of atmospheric rivers. Bulletin of the American Meteorological Society, 100(2), 269-289.
• Visit the Atmospheric River Tracking Model Intercomparison Project (ARTMIP) to follow Atmospheric River community research and publications.