tcpyVPI

A Python package to calculate the tropical cyclone ventilated Potential Intensity (vPI) and the Genesis Potential Index using vPI (GPIv) from gridded datafiles.

See Chavas, Camargo, & Tippett (2025, J. Clim.) for details.

Author: Dan Chavas (2025)
Collaborators: Aaron Kruskie, Jose Ocegueda Sanchez (2025)

Installation

pip install tcpyVPI

Or install from source:

git clone https://github.com/drchavas/tcpyVPI.git
cd tcpyVPI
pip install -e .

Features

• Monthly Mean Data: Compute GPIv from ERA5 monthly mean reanalysis (d633001)
• Hourly Data: Compute GPIv from ERA5 hourly reanalysis (d633000) via THREDDS remote access
• Climatology: Compute and store monthly climatologies of GPIv and its components
• Anomalies: Calculate anomalies relative to climatological means
• Standardized Anomalies: Compute z-scores for statistical analysis

Quick Start

Monthly Mean Computation

from tcpyVPI import run_vpigpiv

# Compute GPIv for September 2022
results = run_vpigpiv(2022, 9)

Hourly Computation

from tcpyVPI import run_vpigpiv_hourly

# Compute GPIv for August 15, 2020 at 12Z
results = run_vpigpiv_hourly(2020, 8, 15, hour=12)

With Anomalies

from tcpyVPI import run_vpigpiv_hourly

# First, compute or load a climatology
results = run_vpigpiv_hourly(
    2020, 8, 15, hour=12,
    compute_anomalies=True,
    climatology_path='gpiv_climatology.nc'
)

Data Loading

The package provides flexible data loading from NCAR RDA THREDDS servers:

from tcpyVPI import load_era5_data, load_era5_hourly

# Load monthly mean data
ds_monthly = load_era5_data(2022, 9, data_source='monthly')

# Load hourly data for a specific time
ds_hourly = load_era5_data(2020, 8, day=15, hour=12, data_source='hourly')

# Load all hours of a day
ds_day = load_era5_hourly(2020, 8, 15)

ERA5 Dataset Structure

The package accesses ERA5 data via THREDDS with the following structure:

Monthly Mean (d633001):

• All 12 months in a single file per variable per year
• Both surface and pressure level variables

Hourly (d633000):

• Surface variables: Monthly files containing all hours
  • Example: e5.oper.an.sfc.128_165_10u.ll025sc.2020080100_2020083123.nc
• Pressure level variables: Daily files containing 24 hours
  • Example: e5.oper.an.pl.128_131_u.ll025uv.2020081500_2020081523.nc

Climatology Computation

from tcpyVPI import compute_monthly_climatology, compute_gpiv_from_dataset

# Compute 40-year climatology (1980-2020)
climatology = compute_monthly_climatology(
    compute_gpiv_from_dataset,
    years=range(1980, 2020),
    output_path='gpiv_climatology.nc'
)

Computing Components Individually

from tcpyVPI import (
    load_era5_data,
    calculate_potential_intensity,
    calculate_vws,
    calculate_entropy_deficit,
    calculate_etac,
)

# Load data
ds = load_era5_data(2022, 9, data_source='monthly')

# Calculate individual components
PI, asdeq = calculate_potential_intensity(ds)
VWS = calculate_vws(ds)
Chi = calculate_entropy_deficit(ds, asdeq)
eta_c = calculate_etac(ds)

Output Variables

The main computation returns a dataset with:

When computing anomalies, additional fields are added:

• *_anom: Anomaly fields
• *_clim: Climatological values

Dependencies

• numpy
• xarray
• tcpyPI
• matplotlib (for plotting)
• cartopy (for plotting)

License

MIT License - see LICENSE file for details.

Citation

If you use this package, please cite:

Chavas, D. R., Camargo, S. J., & Tippett, M. K. (2025). "Tropical cyclone genesis potential using a ventilated potential intensity". Journal of Climate.