solweig 0.1.0b38

High-performance SOLWEIG urban microclimate model (Rust + Python)

SOLWEIG

Map how hot it feels across a city — pixel by pixel.

SOLWEIG computes Mean Radiant Temperature (Tmrt) and thermal comfort indices (UTCI, PET) for urban environments. Give it a building height model and weather data, and it produces high-resolution maps showing where people experience heat stress — and where trees, shade, and cool surfaces make a difference.

Built on Rust for speed, with optional GPU acceleration. Handles everything from a single city block to an entire district.

Status: Beta (v0.1.0). The API is stabilising. Feedback and bug reports welcome — open an issue.

What can you do with it?

• Urban planning — Compare street canyon designs, tree planting scenarios, or cool-roof strategies by mapping thermal comfort before and after.
• Heat risk assessment — Identify the hottest spots in a neighbourhood during a heatwave, hour by hour.
• Research — Run controlled microclimate experiments at 1 m resolution with full radiation budgets.
• Climate services — Generate thermal comfort maps for public health warnings or outdoor event planning.

How it works (in brief)

SOLWEIG models the complete radiation budget experienced by a person standing in an urban environment:

1. Shadows — Which pixels are shaded by buildings and trees at a given sun angle?
2. Sky View Factor — How much sky can a person see from each point? (More sky = more incoming radiation.)
3. Surface temperatures — How hot are the ground and surrounding walls?
4. Radiation balance — Sum shortwave (sun) and longwave (heat) radiation from all directions.
5. Tmrt — Convert total absorbed radiation into a single "felt temperature" metric.
6. Thermal comfort — Optionally derive UTCI or PET, which combine Tmrt with air temperature, humidity, and wind.

Quick start

Install

pip install solweig

Minimal example (numpy arrays)

import numpy as np
import solweig
from datetime import datetime

# A flat surface with one 15 m building
dsm = np.full((200, 200), 2.0, dtype=np.float32)
dsm[80:120, 80:120] = 15.0

surface = solweig.SurfaceData(dsm=dsm, pixel_size=1.0)
# SVF is required before calculate(); compute once and reuse
surface.compute_svf()

location = solweig.Location(latitude=48.8, longitude=2.3, utc_offset=1)  # Paris
weather = solweig.Weather(
    datetime=datetime(2025, 7, 15, 14, 0),
    ta=32.0,          # Air temperature (°C)
    rh=40.0,          # Relative humidity (%)
    global_rad=850.0, # Solar radiation (W/m²)
)

result = solweig.calculate(surface, location, weather)

print(f"Sunlit Tmrt: {result.tmrt[result.shadow > 0.5].mean():.0f}°C")
print(f"Shaded Tmrt: {result.tmrt[result.shadow < 0.5].mean():.0f}°C")

Real-world workflow (GeoTIFFs + EPW weather)

import solweig

# 1. Load surface — prepare() computes and caches walls/SVF when missing
surface = solweig.SurfaceData.prepare(
    dsm="data/dsm.tif",
    cdsm="data/trees.tif",       # Optional: vegetation canopy heights
    working_dir="cache/",        # Expensive preprocessing cached here
)

# 2. Load weather from an EPW file (standard format from climate databases)
weather_list = solweig.Weather.from_epw(
    "data/weather.epw",
    start="2025-07-01",
    end="2025-07-03",
)
location = solweig.Location.from_epw("data/weather.epw")

# 3. Run — outputs saved as GeoTIFFs, thermal state carried between timesteps
results = solweig.calculate_timeseries(
    surface=surface,
    weather_series=weather_list,
    location=location,
    output_dir="output/",
    outputs=["tmrt", "shadow"],
)

# 4. Post-process thermal comfort (optional, runs on saved Tmrt files)
solweig.compute_utci(
    tmrt_dir="output/",
    weather_series=weather_list,
    output_dir="output_utci/",
)

What you need

Input	Required?	What it is
DSM	Yes	Digital Surface Model — a height grid (metres) including buildings. GeoTIFF or numpy array.
Location	Yes	Latitude, longitude, and UTC offset. Can be extracted from the DSM's CRS or an EPW file.
Weather	Yes	Air temperature, relative humidity, and global solar radiation. Load from an EPW file or create manually.
CDSM	No	Canopy heights (trees). Adds vegetation shading.
DEM	No	Ground elevation. Separates terrain from buildings.
Land cover	No	Surface type grid (paved, grass, water, etc.). Affects surface temperatures.

What you get

Output	Unit	Description
Tmrt	°C	Mean Radiant Temperature — the main output. How much radiation a person absorbs.
Shadow	0–1	Shadow fraction (1 = sunlit, 0 = fully shaded).
UTCI	°C	Universal Thermal Climate Index — "feels like" temperature combining all factors.
PET	°C	Physiological Equivalent Temperature — similar to UTCI but with customisable body parameters.
Kdown / Kup	W/m²	Shortwave radiation (down and reflected up).
Ldown / Lup	W/m²	Longwave radiation (thermal, down and emitted up).

Don't have an EPW file? Download one

# Download weather data for any location (no API key needed)
epw_path = solweig.download_epw(latitude=37.98, longitude=23.73, output_path="athens.epw")
weather_list = solweig.Weather.from_epw(epw_path)

Demos

Complete working scripts you can run directly:

• demos/athens-demo.py — Full workflow: rasterise tree vectors, load GeoTIFFs, run a multi-day timeseries, post-process UTCI.
• demos/solweig_gbg_test.py — Gothenburg: surface preparation with SVF caching, timeseries calculation.

Documentation

• Installation
• Quick Start Guide — Step-by-step first calculation
• User Guide — Common workflows, height conventions, and options
• API Reference — All classes and functions
• Physics — How the radiation model works

QGIS Plugin

SOLWEIG is also available as a QGIS plugin for point-and-click spatial analysis:

1. Plugins → Manage and Install Plugins
2. Settings tab → Check "Show also experimental plugins"
3. Search for "SOLWEIG" → Install Plugin

Citation

Adapted from UMEP by Fredrik Lindberg, Sue Grimmond, and contributors.

If you use SOLWEIG in your research, please cite the original model paper and the UMEP platform:

1. Lindberg F, Holmer B, Thorsson S (2008) SOLWEIG 1.0 – Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International Journal of Biometeorology 52, 697–713 doi:10.1007/s00484-008-0162-7

2. Lindberg F, Grimmond CSB, Gabey A, Huang B, Kent CW, Sun T, Theeuwes N, Järvi L, Ward H, Capel-Timms I, Chang YY, Jonsson P, Krave N, Liu D, Meyer D, Olofson F, Tan JG, Wästberg D, Xue L, Zhang Z (2018) Urban Multi-scale Environmental Predictor (UMEP) – An integrated tool for city-based climate services. Environmental Modelling and Software 99, 70-87 doi:10.1016/j.envsoft.2017.09.020

Demo data

The Athens demo dataset (demos/data/athens/) uses the following sources:

• DSM/DEM — Derived from LiDAR data available via the Hellenic Cadastre geoportal
• Tree vectors (trees.gpkg) — Derived from the Athens Urban Atlas and municipal open data at geodata.gov.gr
• EPW weather (athens_2023.epw) — Generated using Copernicus Climate Change Service information [2025] via PVGIS. Contains modified Copernicus Climate Change Service information; neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.

License

GNU Affero General Public License v3.0 — see LICENSE.