tcpyVPI 0.1.2

Calculate the tropical cyclone ventilated Potential Intensity (vPI) and the Genesis Potential Index using vPI (GPIv) from gridded datafiles. Supports both monthly mean and hourly ERA5 data. See Chavas Camargo Tippett (2025, J. Clim.) for details.

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:

Variable	Description	Units
GPIv	Ventilated Genesis Potential Index	-
vPI	Ventilated Potential Intensity	m/s
PI	Potential Intensity	m/s
VWS	Vertical Wind Shear (200-850 hPa)	m/s
Chi	Entropy Deficit	-
eta_c	Capped Absolute Vorticity (850 hPa)	s⁻¹
ventilation_index	Ventilation Index	-

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.