Wind Gust Modeling¶

The wind gust model in erad an be imported using the command below. The gust model is defined by a max gust area with speed decay over surface distance beyond that area.

Williams et al. (2020) Bennett et al. (2021) Roueche & Prevatt (2013)

from IPython.display import display, HTML
import plotly.graph_objects as go
import plotly.io as pio

pio.renderers.default = "plotly_mimetype"


from erad.models.hazard import WindGustModel, GustModelArea

An instance of WindGustModel requires the following pieces of information,

timestamp: The timestamp for the wind event
max_wind_areas: list of areas affected, represented by GustModelArea

Each max wind area is defined by a polygon with maximum wind speed, surrounding high-wind distance, and optional decay factor representing surface roughness.

from datetime import datetime

from gdm.quantities import Distance
from shapely.geometry import Polygon

from erad.quantities import Speed

wind_area_0 = GustModelArea(
    
    max_wind_area=Polygon(
        [
            (-97.275, 28.297),
            (-97.281, 28.297),
            (-97.280, 28.305),
            (-97.276, 28.305),  
        ]
    ),
    max_wind_speed=Speed(110, "miles/hour"),
    wind_aff_distance=Distance(5, "miles"),
    decay_rate=0.5,
)

tstorm_model = WindGustModel(
    name="tstorm_1",
    timestamp=datetime.now(),
    max_wind_areas=[wind_area_0],
)
tstorm_model.pprint()

An example of the WindGustModel can be built using the example() methods for testing purposes.

gust_model = WindGustModel.example()
gust_model.pprint()

Plotting the Wind Gust Model¶



fig = go.Figure()
gust_model.plot(figure=fig)
fig.show()

References¶

Williams, J. H., Paulik, R., Wilson, T. M., Wotherspoon, L., Rusdin, A., & Pratama, G. M. (2020). Tsunami fragility functions for road and utility pole assets using field survey and remotely sensed data from the 2018 Sulawesi tsunami, Palu, Indonesia. Pure and Applied Geophysics, 177, 3545–3562.
Bennett, J. A., Trevisan, C. N., DeCarolis, J. F., Ortiz-Garcı́a, C., Pérez-Lugo, M., Etienne, B. T., & Clarens, A. F. (2021). Extending energy system modelling to include extreme weather risks and application to hurricane events in Puerto Rico. Nature Energy, 6(3), 240–249.
Roueche, D. B., & Prevatt, D. O. (2013). Residential Damage Patterns Following the 2011 Tuscaloosa, AL and Joplin, MO Tornadoes. Journal of Disaster Research, 8, 1061.