solweig-gpu 1.2.9

GPU-accelerated SOLWEIG model for urban thermal comfort simulation

SOLWEIG-GPU: GPU-Accelerated Thermal Comfort Modeling Framework

SOLWEIG-GPU is a Python package and command-line interface for running standalone SOLWEIG (Solar and LongWave Environmental Irradiance Geometry) model on CPU or GPU (if available). It enables high-resolution urban microclimate modeling by computing key variables such as Sky View Factor (SVF), Mean Radiant Temperature (Tmrt), and the Universal Thermal Climate Index (UTCI).

SOLWEIG was originally developed by Dr. Fredrik Lindberg's group. Journal reference: Lindberg, F., Holmer, B. & Thorsson, S. SOLWEIG 1.0 – Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52, 697–713 (2008). https://doi.org/10.1007/s00484-008-0162-7

SOLWEIG GPU code is an extension of the original SOLWEIG Python model that is part of the Urban Multi-scale Environmental Predictor (UMEP) (GitHub code reference: https://github.com/UMEP-dev/UMEP). UMEP journal reference: Lindberg, F., Grimmond, C.S.B., Gabey, A., Huang, B., Kent, C.W., Sun, T., Theeuwes, N.E., Järvi, L., Ward, H.C., Capel-Timms, I. and Chang, Y., 2018. Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. Environmental modelling & software, 99, pp.70-87. https://doi.org/10.1016/j.envsoft.2017.09.020

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Features

• CPU and GPU support (automatically uses GPU if available)
• Divides larger areas into tiles based on the selected tile size
• CPU-based computations of wall height and aspect are parallelized across multiple CPUs
• GPU-based computation of SVF, shortwave/longwave radiation, shadows, Tmrt, and UTCI
• Compatible with meteorological data from UMEP, ERA5, and WRF (wrfout)

Flowchart of the SOLWEIG-GPU modeling framework

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Required Input Data

• Building DSM: Includes both buildings and terrain elevation (e.g., Building_DSM.tif)
• DEM: Digital Elevation Model excluding buildings (e.g., DEM.tif)
• Tree DSM: Vegetation height data only (e.g., Trees.tif)
• Meteorological forcing:
  • Custom .txt file (from UMEP)
  • ERA5 (both instantaneous and accumulated)
  • WRF output NetCDF (wrfout)

Please refer to the sample dataset to familiarize yourself with the expected inputs. Sample data can be found at https://utexas.box.com/s/8fctqicidr5cup8kj3tk53jd444pow6z

ERA5 Variables Required

• 2-meter air temperature
• 2-meter dew point temperature
• Surface pressure
• 10-meter U and V wind components
• Downwelling shortwave radiation (accumulated)
• Downwelling longwave radiation (accumulated)

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Output Details

• Output directory: Outputs/
• Structure: One folder per tile (e.g., tile_0_0/, tile_0_600/)
• SVF: Single-band raster
• Other outputs: Multi-band raster (e.g., 24 bands for hourly results)

UTCI for New Delhi, India, generated using SOLWEIG-GPU and visualized with ArcGIS Online.

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Installation

conda create -n solweig python=3.10
conda activate solweig
conda install -c conda-forge gdal pytorch
pip install solweig-gpu
#Or from source 
git clone https://github.com/nvnsudharsan/solweig-gpu.git
cd solweig-gpu
pip install .

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Python Usage

from solweig_gpu import thermal_comfort

thermal_comfort(
    base_path='/path/to/input',
    selected_date_str='2020-08-13',
    building_dsm_filename='Building_DSM.tif',
    dem_filename='DEM.tif',
    trees_filename='Trees.tif',
    landcover_filename = None,
    tile_size =3600,
    overlap = 100,
    use_own_met=True,
    own_met_file='/path/to/met.txt',
    start_time='2020-08-13 00:00:00',
    end_time='2020-08-13 23:00:00',
    data_source_type='ERA5',  # or 'WRFOUT'
    data_folder='/path/to/era5_or_wrfout',
    save_tmrt=False, #True if you want to save TMRT, likewise below
    save_svf=False,
    save_kup=False,
    save_kdown=False,
    save_lup=False,
    save_ldown=False,
    save_shadow=False
)

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Command-Line Interface (CLI)

conda activate solweig
thermal_comfort --base_path /path/to/input \
                --selected_date_str 2020-08-13 \
                --building_dsm_filename Building_DSM.tif \
                --dem_filename DEM.tif \
                --trees_filename Trees.tif \
                --landcover_filename None \
                --tile_size 3600 \
                --use_own_met True \
                --own_met_file /path/to/met.txt \
                --start_time "2020-08-13 00:00:00" \
                --end_time "2020-08-13 23:00:00" \
                --data_source_type ERA5 \
                --data_folder /path/to/era5_or_wrfout \
                --save_tmrt True \
                --save_svf False

Tip: Use --help to list all CLI options.

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GUI Usage

To launch the GUI:

conda activate solweig
solweig_gpu_gui

GUI Workflow

1. Select the base path containing input datasets.
2. Choose the Building DSM, DEM, Tree DSM, and Land cover (optional) raster files.
3. Set the tile size (e.g., 600 or 1200 pixels).
4. Select a meteorological source (metfile, ERA5, or wrfout):
   • If metfile: Provide a .txt file.
   • If ERA5: Provide a folder with both instantaneous and accumulated files.
   • If wrfout: Provide a folder with WRF output NetCDF files.
5. Set the start and end times in UTC (YYYY-MM-DD HH:MM:SS).
6. Choose which outputs to generate (e.g., Tmrt, UTCI, radiation fluxes).
7. Output will be saved in Outputs/, with subfolders for each tile.

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Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

Please keep your pull requests small and focused. This will make it easier to review and merge.