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The winter storm severity index (WSSI) is a tool designed to assist National Weather Service (NWS) operational forecasters, core partners and stakeholders, media, and the public in maintaining situational awareness of anticipated winter-weather-related impacts. The NWS Weather Prediction Center (WPC) produces the WSSI several times per day by combining the current official NWS gridded forecasts from the National Digital Forecast Database (NDFD) with nonmeteorological datasets (e.g., climatology, land use, population) to create a graphical depiction of anticipated impacts to society due to winter weather. Impact severity output from the WSSI is designed to communicate spatial and temporal impact information in an easily consumable graphic. These impact scales range from minor to extreme, with a winter-weather area shown for areas where the winter-weather conditions are not expected to impact daily life. This product emerged in response to user needs for information about societal impacts that can occur from winter storm hazards. Specific hazards include snow amount, snow load, ice accumulation, flash freeze, blowing snow, and ground blizzard. The maximum value from any of these components contributes to a final overall impact component. Information about the WSSI, including a detailed overview of the product, recent social-science-backed improvements, components, and use in an operational setting are presented. Preliminary verification efforts are also detailed. Significance Statement The winter storm severity index (WSSI) is the first nationwide tool used operationally within the National Weather Service (NWS) to forecast impacts from winter weather. This product allows the NWS to broaden its communication options for impact-based decision support services and directly speak to potential impact severity. It also provides a concise informational graphic with actionable information that highlights and summarizes multifaceted winter-weather events that can aid in decision-making. An overview of the WSSI is presented, including highlights for its utility in an operational forecast setting and verification.
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Kastman et al. (2025) studied this question.
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