wrf-rust 0.1.0

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. 80+ diagnostic variables, configurable CAPE/SRH/shear/lapse rates, built-in plotting, and parallel computation via rayon.

Built on top of wx-math and metrust for meteorological calculations.

Install

Requires Rust, maturin, and system libnetcdf/libhdf5 (easiest from conda).

conda install -c conda-forge libnetcdf hdf5
pip install maturin

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

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 -- select your parcel type
sbcape = getvar(f, "sbcape")                             # surface-based
mlcape = getvar(f, "mlcape")                             # 100 hPa mixed-layer
mucape = getvar(f, "mucape")                             # most unstable
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)  # pressure-based
lr_v  = getvar(f, "lapse_rate", bottom_m=0, top_m=3000, use_virtual=True)

# 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 map
plot_field(f, "slp", units="hPa")                # unit conversion
plot_wind(f)                                      # wind barbs over speed fill
plot_skewt(f, point=(35.0, -97.5))               # Skew-T with hodograph
panel(f, ["sbcape", "srh1", "stp", "shear_0_6km"])  # multi-panel

Multi-timestep rendering with consistent scales and GIF output:

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

Variables

80+ diagnostic variables. All support the units= parameter.

Thermodynamics

Variable	Aliases	Units	Description
temp	tk	K	Temperature
tc	temp_c	degC	Temperature (Celsius)
theta	th	K	Potential temperature
theta_e	eth	K	Equivalent potential temperature
theta_w		K	Wet-bulb potential temperature
tv		K	Virtual temperature
twb	wet_bulb	K	Wet-bulb temperature
td	dp, dewpoint	degC	Dewpoint
rh		%	Relative humidity

Pressure & height

Variable	Aliases	Units	Description
pressure	pres, p	Pa	Full model pressure
slp	mslp	hPa	Sea-level pressure
height	z	m	Height MSL
height_agl	z_agl	m	Height AGL
terrain	ter, hgt	m	Terrain height
geopt		m2/s2	Geopotential
omega		Pa/s	Vertical velocity (pressure coords)

Moisture

Variable	Aliases	Units	Description
pw	precipitable_water	mm	Precipitable water
rh2m	rh2	%	2-m relative humidity
dp2m	td2	degC	2-m dewpoint
mixing_ratio	qvapor	kg/kg	Water vapor mixing ratio
specific_humidity	q	kg/kg	Specific humidity

CAPE & convection

All CAPE variables support top_m for truncated integration (e.g. top_m=3000 for 3CAPE).

Variable	Units	Description
sbcape / sbcin	J/kg	Surface-based CAPE/CIN
mlcape / mlcin	J/kg	Mixed-layer CAPE/CIN (100 hPa)
mucape / mucin	J/kg	Most-unstable CAPE/CIN
cape / cin	J/kg	Generic (accepts parcel_type or custom parcel)
lcl / lfc / el	m	LCL / LFC / EL height AGL
effective_cape	J/kg	MUCAPE within effective inflow layer
effective_inflow	m	Effective inflow base/top heights

Wind

Variable	Aliases	Units	Description
ua / va / wa		m/s	Destaggered U/V/W wind
wspd / wdir		m/s, deg	Wind speed / direction
wspd10 / wdir10		m/s, deg	10-m wind speed / direction
uvmet / uvmet10		m/s	Earth-rotated wind components

SRH & shear

SRH uses Bunkers Internal Dynamics method. All accept storm_motion=(u,v) for custom motion.

Variable	Units	Description
srh1 / srh3	m2/s2	0-1 / 0-3 km SRH
srh	m2/s2	Configurable depth via depth_m
effective_srh	m2/s2	SRH over effective inflow layer
shear_0_1km / shear_0_6km	m/s	Fixed-layer bulk shear
bulk_shear	m/s	Configurable via bottom_m / top_m
mean_wind	m/s	Configurable via bottom_m / top_m
bunkers_rm / bunkers_lm	m/s	Bunkers right/left-mover motion

Severe composites

Variable	Description
stp	Significant Tornado Parameter (fixed or layer_type="effective")
scp	Supercell Composite Parameter
ehi	Energy-Helicity Index (configurable SRH depth via depth_m)
critical_angle	Critical angle
ship	Significant Hail Parameter
bri	Bulk Richardson Number

Radar & cloud

Variable	Units	Description
dbz / maxdbz	dBZ	Simulated / composite reflectivity
ctt	degC	Cloud-top temperature
cloudfrac	%	Cloud fraction (low/mid/high)
uhel	m2/s2	Updraft helicity (configurable bottom_m/top_m)

Vorticity

Variable	Units	Description
avo	s-1	Absolute vorticity
pvo	PVU	Potential vorticity

Lapse rates & levels

Variable	Units	Description
lapse_rate_700_500	degC/km	700-500 hPa lapse rate
lapse_rate_0_3km	degC/km	0-3 km lapse rate
lapse_rate	degC/km	Configurable (bottom_m/top_m or bottom_p/top_p, use_virtual)
freezing_level	m	Freezing level AGL
wet_bulb_0	m	Wet-bulb zero height AGL

Fire weather

Variable	Description
fosberg	Fosberg Fire Weather Index
haines	Haines Index
hdw	Hot-Dry-Windy Index

Configurable parameters

Many variables have both hardcoded convenience names and generic configurable versions.

Parameter	Type	Description
units	str	Output unit conversion (works on every variable)
parcel_type	str	"sb", "ml", "mu" for CAPE variables
parcel_pressure	float	Custom parcel pressure (hPa)
parcel_temperature	float	Custom parcel temperature (degC)
parcel_dewpoint	float	Custom parcel dewpoint (degC)
top_m	float	Top of layer in m AGL
bottom_m	float	Bottom of layer in m AGL
top_p	float	Top of layer in hPa (lapse rates)
bottom_p	float	Bottom of layer in hPa (lapse rates)
depth_m	float	SRH/EHI integration depth (m AGL)
storm_motion	(u, v)	Custom storm motion in m/s
layer_type	str	"fixed" or "effective" for STP
use_virtual	bool	Virtual temperature for lapse rates

Unit conversion

Every variable supports units=. Case-insensitive.

Category	Strings
Temperature	K, degC, degF, celsius, fahrenheit
Pressure	Pa, hPa, mb, inHg
Speed	m/s, knots, kt, mph, kph
Length	m, ft, km, mi
Moisture	kg/kg, g/kg
Depth	mm, in

Building from source

Prerequisites

1. Rust -- rustup.rs
2. libnetcdf + libhdf5 -- conda install -c conda-forge libnetcdf hdf5
3. maturin -- pip install maturin
4. wx-math + metrust -- the meteorological math libraries:

   git clone https://github.com/FahrenheitResearch/rustmet.git ~/rustmet
   git clone https://github.com/FahrenheitResearch/metrust.git ~/metrust
   

Build

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

Update crates/wrf-core/Cargo.toml if your wx-math/metrust paths differ. Update .cargo/config.toml for HDF5/NetCDF library paths.

Windows

The .cargo/config.toml points to conda's wrfplot environment for HDF5/NetCDF. The Python module auto-discovers DLL paths from common conda locations on import.

Architecture

wrf-rust/
  crates/wrf-core/         Pure Rust library
    src/
      file.rs              NetCDF reading + field caching
      grid.rs              Arakawa C-grid destaggering
      variables.rs         Variable registry (80+ entries)
      compute.rs           getvar dispatch + unit conversion
      diag/                12 diagnostic modules
        cape.rs            SB/ML/MU CAPE, custom parcels, effective inflow
        srh.rs             Bunkers SRH, effective SRH, shear, mean wind
        severe.rs          STP (fixed + effective), SCP, EHI, critical angle
        thermo.rs          Temperature variants, theta-e, dewpoint, RH
        pressure.rs        SLP, height, omega, geopotential
        wind.rs            Destaggering, rotation, speed/direction
        moisture.rs        PW, mixing ratio, 2m fields
        radar.rs           Simulated reflectivity
        cloud.rs           Cloud-top temp, cloud fraction
        vorticity.rs       Absolute and potential vorticity
        helicity.rs        Updraft helicity
        extra.rs           Lapse rates, freezing level, fire indices
  src/                     PyO3 bindings
  python/wrf/              Python API
    __init__.py            getvar, WrfFile, ALL_TIMES
    plot.py                Plotting (matplotlib + cartopy)
    cli.py                 CLI (python -m wrf)

License

MIT