pepc-global-intensity 0.1.0

Predict tropical cyclone intensity using pre-trained Input-Output Hidden Markov Models

pepc-global-intensity

Predict tropical cyclone intensity using pre-trained Input-Output Hidden Markov Models.

Installation

pip install --upgrade pepc-global-intensity

Usage

import pandas as pd
from pepc_global_intensity import predict_intensity

# DataFrame for a single storm with required columns:
#   step, time, year, mode, ILD, grd, ut, pi, shr, rh600,
#   ocean, ocean_next, mean_stl1, mean_fsr, mean_swvl1
df = pd.DataFrame({
    "step":       [0, 1, 2],
    "time":       pd.to_datetime(["2000-07-01", "2000-07-01 06:00", "2000-07-01 12:00"]),
    "year":       [2000, 2000, 2000],
    "mode":       ["ocean", "ocean", "ocean"],
    "ILD":        [50.0, 50.0, 50.0],
    "grd":        [1e-4, 1e-4, 1e-4],
    "ut":         [5.0, 5.0, 5.0],
    "pi":         [70.0, 70.0, 70.0],
    "shr":        [10.0, 10.0, 10.0],
    "rh600":      [0.7, 0.7, 0.7],
    "ocean":      [True, True, True],
    "ocean_next": [True, True, True],
    "mean_stl1":  [0.0, 0.0, 0.0],
    "mean_fsr":   [0.0, 0.0, 0.0],
    "mean_swvl1": [0.0, 0.0, 0.0],
})

result = predict_intensity(
    basin="NA",
    df=df,
    experiment="historical",
    model="ACCESS-ESM1-5",
    variant="r1i1p1f1",
    seed=42,
)

Parameters

• basin: str — one of "AS", "BoB", "WNP", "ENP", "NA", "SI", "SP"
• df: pandas.DataFrame — pre-prepared data for a single storm with columns:
  • step — track point index within the storm (0 at genesis)
  • time — timestamp
  • year — calendar year
  • mode — "ocean", "land", or "not"
  • ILD — isothermal layer depth (m)
  • grd — ocean temperature gradient
  • ut — storm translation speed (m/s)
  • pi — potential intensity (kt)
  • shr — wind shear (m/s)
  • rh600 — relative humidity at 600 hPa
  • ocean — whether the current point is over ocean
  • ocean_next — whether the next point is over ocean
  • mean_stl1 — land-weighted soil temperature
  • mean_fsr — land-weighted fraction of sunshine radiation
  • mean_swvl1 — land-weighted soil water volume
• experiment: str — "historical" or "ssp585"
• model: str — CMIP6 model name (used for ssp585 upper-bound schedule lookup)
• variant: str — CMIP6 variant label (used for ssp585 upper-bound schedule lookup)
• seed: int or None — random seed for reproducibility

Returns

• pandas.DataFrame — copy of the input with added columns: pre_wind, pre_delta_wind_b, pre_delta_wind_f, pre_ocn, state

Model Weights

Model weights (HMM parameters and land-mode regression coefficients) are bundled with the package.