Description
Extreme wind events, such as tornadoes and severe storms, have profound economic impacts, causing widespread damage to infrastructure, homes, and businesses. They lead to significant repair and reconstruction costs, disrupt utilities and transportation networks, and can halt production and services in affected areas, impacting local and national economies. The historical extreme wind risk product captures the near-term risks of extreme wind events across the globe using wind speed data. This extreme wind product seeks to identify areas across the globe where historically observed extreme wind events have posed risks to the built environment and human life.
Technical Description
The Extreme Wind Historical Risk product captures the recent near-historical risk of extreme wind events across the globe using historical wind speed reanalysis data. This extreme wind product seeks to identify areas across the globe where historically observed extreme wind events have posed risks to the built environment and human life. This product can be used to assess the frequency of extreme wind at different locations, or to identify relative risk across different regions at a single time horizon.
This dataset contains the following fields:
- extreme_wind_frequency: Frequencies of extreme wind occurrence at pixel locations over the historical baseline period used for calculation. This looks at the frequencies of extreme wind occurrence at pixel locations over the historical baseline period used for calculation. For instance, a value of 0.5 would indicate that half of the days were under extreme wind conditions for that location. This is a value between 0.0 and 1.0.
- extreme_wind_risk_class: The extreme wind risk classification for each value. Values are integers from 1 to 5 representing the following classes: Very Low Risk (1), Low Risk (2), Moderate Risk (3), High Risk (4), and Very High Risk (5). Values are categorized using the 44th, 67th, 84th, and 94th percentiles of the extreme_wind_frequency field, exclusive of '0' values.