wrf-rust 0.2.4

Rust-powered WRF post-processing — fixes CAPE, SRH, adds 65+ variables, 5-30x faster than wrf-python

wrf-rust

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

Install

pip install wrf-rust

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

Usage

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))

# 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")

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

Tested on a 199x199x79 WRF grid. All values match exactly (rel_err=0.00).

Variable	wrf-python	wrf-rust	Speedup
Temperature	0.268s	0.004s	76x
Potential temp	0.088s	0.003s	26x
Abs. vorticity	0.173s	0.015s	11x
Relative humidity	0.353s	0.097s	4x
Precipitable water	0.224s	0.077s	3x
Reflectivity	0.494s	0.234s	2x
SLP	0.517s	0.497s	1x
Wind destagger	0.089s	0.081s	1x

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

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).

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. All accept storm_motion=(u,v).

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.

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

Parameter	Description
units	Output unit conversion (every variable)
parcel_type	"sb", "ml", "mu" for CAPE
parcel_pressure/temperature/dewpoint	Custom parcel (hPa, degC, degC)
top_m / bottom_m	Layer bounds in m AGL
top_p / bottom_p	Layer bounds in hPa
depth_m	SRH/EHI depth (m AGL)
storm_motion	Custom (u, v) in m/s
layer_type	"fixed" or "effective" for STP
use_virtual	Virtual temperature for lapse rates
lake_interp	Interpolate 2m fields over lakes < N km2

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