GLObal Building heights for Urban Studies (UT-GLOBUS)

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Abstract 

We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Data

We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions

Data coverage

The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.

How to find your city in the UT-GLOBUS dataset

Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.

How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters

Step 0: Before compiling WRF, go to 'dyn_em' folder and open 'module_initialize_real.F'.

Change line 3121 (in version 4.5.2): 

From 

grid%HI_URB2D(i,k,j)  = grid%URB_PARAM(i,k+117,j) 

To

grid%HI_URB2D(i,k,j)  = grid%URB_PARAM(i,k+117,j)*100.

1. Change the name of the binary files 'ufrac' and 'urb_param' inside 'urb_fra' and 'GLOBUS_morph' folders, respectively to 00001-tile_x.00001-tile_y.

Values for tile_x and tile_y can be found in the index file inside the 'urb_fra' and 'GLOBUS_morph' folders. Make sure to append zeros before tile_x and tile_y values to make 5 digits. 

Ex: tile_x = 260 and tile_y = 219; Then the binary files should be renamed as 00001-00260.00001-00209 

2. Copy the 'urb_fra' and 'GLOBUS_morph' folders to WRF static data directory.

3. Change the paths to 'URB_PARAM' and 'FRC_URB2D' variables inside GEOGRID.TBL file as follows:

===============================

name=URB_PARAM

        priority=1

        optional=yes

        dest_type=continuous

        fill_missing = 0.

        z_dim_name=num_urb_params

        interp_option=default:nearest_neighbor

        abs_path= Your_WPS_static_data_folder/GLOBUS_morph/

        flag_in_output=FLAG_URB_PARAM

===============================

name=FRC_URB2D

        priority=1

        optional=yes

        dest_type=continuous

        fill_missing = 0.

        interp_option=default:nearest_neighbor

        abs_path= Your_WPS_static_data_folder/urb_fra/

        flag_in_output=FLAG_FRC_URB2D

===============================

4. Run geogrid.exe. If the domain covers the chosen city:

 -- 'FRC_URB2D' variable will show the urban fraction.

 -- 'URB_PARAM[91,:,:]' will show the plan area fraction.

 -- 'URB_PARAM[94,:,:]' will show the area averaged building heights.

 -- 'URB_PARAM[95,:,:]' will show the building surface to total area fraction.

 -- 'URB_PARAM[118-132,:,:]' will show the building height histograms with 5-meter bin size.

5. If you see the data in 'FRC_URB2D' and 'URB_PARAM' variables after running the geogrid.exe, GLOBUS data is ingested in WPS and you can continue with ungrib and metgrid as usual.

6. For running the model over the domain area which covers more that one city, UT-GLOBUS UCPs can be stitched together. For instance, if two cities are covered in the domain, step number 3 should be modified as follows:

===============================

name=URB_PARAM

        priority=1

        dest_type=continuous

        fill_missing = 0.

        z_dim_name=num_urb_params

        interp_option=default:nearest_neighbor

        abs_path=Your_WPS_static_data_folder/GLOBUS_morph_for_city-1/             

flag_in_output=FLAG_URB_PARAM

===============================

name=FRC_URB2D

        priority=1

        dest_type=continuous

        fill_missing = 0.

        interp_option=default:nearest_neighbor

        abs_path= Your_WPS_static_data_folder/urb_fra_for_city-1/

        flag_in_output=FLAG_FRC_URB2D

===============================

name=URB_PARAM

        priority=2

        dest_type=continuous

        fill_missing = 0.

        z_dim_name=num_urb_params

        interp_option=default:nearest_neighbor

        abs_path= Your_WPS_static_data_folder/GLOBUS_morph_for_city-2/

===============================

name=FRC_URB2D

        priority=2

        dest_type=continuous

        fill_missing = 0.

        interp_option=default:nearest_neighbor

        abs_path= Your_WPS_static_data_folder/urb_fra_for_city-2/

===============================

References

Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M., Barker, D., Huang, X., 2021. A Description of the advanced research WRF model.

Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorol 104, 261–304. https://doi.org/10.1023/A:1016099921195

Sun, T., Grimmond, S., 2019. A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): Development, deployment and demonstration. Geosci Model Dev 12, 2781–2795. https://doi.org/10.5194/gmd-12-2781-2019

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. Int J Biometeorol 52, 697–713. https://doi.org/10.1007/s00484-008-0162-7

Software: QGIS (https://www.qgis.org/en/site/)