wrf-rust

Rust-powered WRF post-processing with Python bindings. 83 diagnostic variables, built-in plotting, and parallel computation.

Install

pip install wrf-rust-next

Pre-built wheels for Python 3.10-3.13 on Linux, macOS, and Windows. No Rust toolchain, no system libraries, no conda required.

Community Guide

The repo now includes a full WRF community setup guide for Windows, WSL 2, real-data initialization, domain sizing, and troubleshooting:

• Manual-first Pages guide: docs/index.html
• Starter files: docs/starter-files/README.md
• Optional Codex skill: skills/wrf-community-onboarding/SKILL.md

Scientific Notes

Recent correctness work tightened the severe-weather diagnostics without changing the basic getvar() workflow:

• effective_inflow now returns the actual effective inflow base/top heights, not the MU parcel EL.
• Effective stp and scp now use effective SRH plus effective bulk wind difference (EBWD).
• bri now uses BRN shear instead of plain 0-6 km bulk shear.
• Raw staggered fields (U, V, W) keep their native WRF shapes, and ALL_TIMES stacking now runs in Rust.

Usage

import numpy as np
from wrf import WrfFile, getvar

f = WrfFile("wrfout_d01_2024-06-01_00:00:00")

# Basic fields
temp = getvar(f, "temp", units="degC")
slp  = getvar(f, "slp",  units="hPa")
wspd = getvar(f, "wspd", units="knots")

# CAPE with parcel selection
sbcape = getvar(f, "sbcape")
mlcape = getvar(f, "mlcape")
mucape = getvar(f, "mucape")
sb3cap = getvar(f, "sbcape", top_m=3000)           # 0-3 km CAPE

# Custom parcel
cape = getvar(f, "cape", parcel_pressure=850,
              parcel_temperature=20, parcel_dewpoint=15)

# SRH with Bunkers storm motion
srh1 = getvar(f, "srh1")                            # 0-1 km
srh3 = getvar(f, "srh3")                            # 0-3 km
srh  = getvar(f, "srh", depth_m=1500, storm_motion=(12, 8))

# Per-grid custom storm motion
sm_u = np.full((f.ny, f.nx), 12.0)
sm_v = np.full((f.ny, f.nx), 8.0)
srh_custom = getvar(f, "srh", depth_m=1500, storm_motion=(sm_u, sm_v))

# Effective inflow layer
eff_srh  = getvar(f, "effective_srh")
eff_cape = getvar(f, "effective_cape")

# Severe composites
stp     = getvar(f, "stp")                           # fixed-layer
stp_eff = getvar(f, "stp", layer_type="effective")   # effective-layer
scp     = getvar(f, "scp")
ehi     = getvar(f, "ehi", depth_m=3000)             # 0-3 km EHI

# Configurable layers
shear = getvar(f, "bulk_shear", bottom_m=1000, top_m=6000)
mw    = getvar(f, "mean_wind",  bottom_m=0, top_m=6000)
lr    = getvar(f, "lapse_rate", bottom_p=700, top_p=500)
lr_v  = getvar(f, "lapse_rate", bottom_m=0, top_m=3000, use_virtual=True)

# Lake interpolation (removes 2m artifacts over water bodies)
cape = getvar(f, "sbcape", lake_interp=1000)         # interp lakes < 1000 km2

# All timesteps
slp_all = getvar(f, "slp", timeidx=None)             # shape (nt, ny, nx)

Also accepts netCDF4.Dataset directly:

from netCDF4 import Dataset
slp = getvar(Dataset("wrfout_d01..."), "slp")

Note: dataset inputs are reopened by filepath under the hood. On Windows, do not keep a netCDF4.Dataset open while calling wrf-rust on that same file, especially in subprocesses. Close the dataset first or pass a file path / WrfFile instead.

Plotting

from wrf import plot_field, plot_wind, plot_skewt, panel

plot_field(f, "sbcape")                               # auto colormap + cartopy
plot_field(f, "sbcape", style="solar7")               # Solarpower07 colormaps
plot_wind(f)                                           # wind barbs
plot_skewt(f, point=(35.0, -97.5))                    # Skew-T with hodograph
panel(f, ["sbcape", "srh1", "stp", "shear_0_6km"])   # multi-panel

# Multi-timestep with consistent scale + GIF
from wrf.plot import render_timesteps
render_timesteps(f, "sbcape", timesteps=[0,1,2,3],
                 gif=True, fixed_scale=True)

CLI

python -m wrf info  wrfout_d01_2024-06-01_00:00:00
python -m wrf stats wrfout_d01_2024-06-01_00:00:00 sbcape slp temp
python -m wrf plot  wrfout_d01_2024-06-01_00:00:00 slp -o slp.png
python -m wrf panel wrfout_d01_2024-06-01_00:00:00 sbcape srh1 stp -o severe.png

Benchmark vs wrf-python

Benchmarked on a 199x199x79 WRF grid. The fields below matched wrf-python on that case, but broader scientific equivalence still depends on variable and workflow.

Plus 17 variables wrf-python doesn't have (STP, SCP, EHI, critical angle, shear, Bunkers, lapse rates, fire indices, effective inflow layer).

Variables

83 diagnostic variables. All support units= parameter.

Thermodynamics

temp tc theta theta_e theta_w tv twb td rh

Pressure & height

pressure slp height height_agl terrain geopt omega

pressure defaults to hPa for wrf-python compatibility. Use pres / p or units="Pa" when you want Pascals.

Moisture

pw rh2m dp2m mixing_ratio specific_humidity

CAPE & convection

sbcape sbcin mlcape mlcin mucape mucin cape cin lcl lfc el effective_cape effective_inflow cape2d cape3d

All CAPE variables support top_m for truncated integration (e.g. top_m=3000 for 3CAPE). Generic cape/cin accept parcel_type or custom parcel (parcel_pressure, parcel_temperature, parcel_dewpoint). effective_inflow returns a two-plane output: effective layer base followed by effective layer top, both in meters AGL.

Wind

ua va wa wspd wdir wspd10 wdir10 uvmet uvmet10

SRH & shear

srh1 srh3 srh effective_srh shear_0_1km shear_0_6km bulk_shear mean_wind bunkers_rm bunkers_lm mean_wind_0_6km

SRH uses Bunkers Internal Dynamics method. storm_motion=(u, v) accepts either scalar components or (ny, nx) component grids.

Severe composites

stp stp_fixed stp_effective scp ehi critical_angle ship bri

STP supports layer_type="effective" for the 5-term formula with MLCIN. Effective stp uses ESRH + EBWD, and scp uses MUCAPE + effective SRH + EBWD.

Radar & cloud

dbz maxdbz ctt cloudfrac uhel

Vorticity

avo pvo

Lapse rates & levels

lapse_rate_700_500 lapse_rate_0_3km lapse_rate freezing_level wet_bulb_0

Generic lapse_rate accepts bottom_m/top_m or bottom_p/top_p, plus use_virtual=True.

Fire weather

fosberg haines hdw

Parameters

Unit strings

Every registered variable supports units=. Raw WRF variables (RAINNC, T2, PSFC, etc.) also support conversion when their default unit is known. Case-insensitive.

Raw WRF variables

Any variable name not in the computed registry is read directly from the file. Common ones:

# These all work:
rain = getvar(f, "RAINNC", units="in")     # accumulated precip in inches
tsk  = getvar(f, "TSK", units="degF")      # skin temp in Fahrenheit
pblh = getvar(f, "PBLH", units="ft")       # PBL height in feet

Building from source

Only needed for development. Users should pip install wrf-rust.

git clone https://github.com/FahrenheitResearch/wrf-rust.git
cd wrf-rust
pip install maturin
maturin develop --release

License

MIT