{"id":2663452,"date":"2025-01-29T11:57:10","date_gmt":"2025-01-29T19:57:10","guid":{"rendered":"https:\/\/www.esri.com\/arcgis-blog\/?post_type=blog&p=2663452"},"modified":"2025-01-30T13:22:00","modified_gmt":"2025-01-30T21:22:00","slug":"daily-risk-of-severe-weather","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather","title":{"rendered":"Daily Risk of Severe Weather"},"author":5141,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[],"industry":[],"product":[36561],"class_list":["post-2663452","blog","type-blog","status-publish","format-standard","hentry","category-analytics","product-arcgis-pro"],"acf":{"authors":[{"ID":315222,"user_firstname":"Mark","user_lastname":"Gilbert","nickname":"Mark Gilbert","user_nicename":"mgilbert","display_name":"Mark Gilbert","user_email":"MGilbert@esri.com","user_url":"","user_registered":"2022-07-18 21:29:07","user_description":"Mark is a Principle GIS Engineer on the Living Atlas Environment team in Redlands, CA. He currently supports projects related to global climate projections and local climate resilience planning and mitigation using online data. He relies heavily on Python and Jupyter Notebooks to process raster datasets in his daily work. Previous experience in aerospace engineering and information technology helps him improve and automate global data processing workflows. Feel free to contact Mark at mgilbert@esri.com with questions or comments.","user_avatar":""},{"ID":5141,"user_firstname":"Kevin","user_lastname":"Butler","nickname":"Kevin Butler","user_nicename":"kevi6890","display_name":"Kevin Butler","user_email":"KButler@esri.com","user_url":"","user_registered":"2018-03-02 00:16:49","user_description":"Kevin Butler is a Product Engineer on Esri\u2019s Analysis and Geoprocessing Team working as a liaison to the science community. He holds a Ph.D. in Geography from Kent State University. Over the past decade he has worked on strategic projects, partnering with customers and other members of the science community to assist in the development of large ecological information products such as the ecological land units, ecological marine units and ecological coastal units. His research interests include a thematic focus on spatial statistical analytical workflows, a methodological focus on spatial clustering techniques and a geographic focus on Puerto Rico and midwestern cities.","user_avatar":""}],"short_description":"Explore severe weather patterns using the Space Time Kernel Density tool in ArcGIS Pro's Spatial Analyst extension.","flexible_content":[{"acf_fc_layout":"content","content":"

Imagine waking up to sunny skies only to see them transform into a dangerous storm by the afternoon. Tornadoes tearing across the plains, hail pelting down on rooftops, or powerful winds uprooting trees\u2014these severe weather events are a reality for many communities. Understanding when and where these events occur is essential for preparation and mitigation. This blog will guide you through a workflow to explore severe weather patterns using the Space Time Kernel Density<\/a> tool in ArcGIS Pro’s Spatial Analyst extension.<\/p>\n"},{"acf_fc_layout":"content","content":"

Severe Weather Climatologies<\/strong><\/p>\n

Severe weather climatologies, like the ones produced in this blog, provide a long-term statistical representation of weather patterns and risks. They are essential for understanding the spatial and temporal distribution of phenomena like tornadoes, hail, and damaging winds. These climatologies inform emergency management, logistics, urban planning, agriculture, and public awareness. By mapping severe weather patterns, we can identify trends that help protect lives and property.<\/p>\n"},{"acf_fc_layout":"content","content":"

The Results<\/strong><\/p>\n

Using Space Time Kernel Density, we visualized the patterns of severe weather events through space and time (this process is described later in this blog). We summarized hail, tornadoes, severe thunderstorm wind, and all these events combined. Each map shows the daily probability of severe weather occurring in each cell, with warmer colors representing a higher risk of severe weather events. These maps help reveal recurring seasonal patterns and regional hotspots.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2663732,"id":2663732,"title":"hail_compact","filename":"hail_compact.gif","filesize":8134923,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather\/hail_compact","alt":"","author":"5141","description":"","caption":"","name":"hail_compact","status":"inherit","uploaded_to":2663452,"date":"2025-01-29 04:43:47","modified":"2025-01-29 04:43:47","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":640,"height":479,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","medium-width":349,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","medium_large-width":640,"medium_large-height":479,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","large-width":640,"large-height":479,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","1536x1536-width":640,"1536x1536-height":479,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","2048x2048-width":640,"2048x2048-height":479,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact-621x465.gif","card_image-width":621,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/hail_compact.gif","wide_image-width":640,"wide_image-height":479}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Daily risk of hail.\u00a0The risk value can roughly be interpreted as the probability that an extreme weather event will occur on that day within 80 km of the location.\u00a0A hosted imagery layer for use in visualization and analysis workflows is available here<\/a>.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2663772,"id":2663772,"title":"thunderstorm_compact","filename":"thunderstorm_compact.gif","filesize":9483922,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather\/thunderstorm_compact","alt":"","author":"5141","description":"","caption":"","name":"thunderstorm_compact","status":"inherit","uploaded_to":2663452,"date":"2025-01-29 04:50:46","modified":"2025-01-29 04:50:46","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":640,"height":480,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","medium-width":348,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","medium_large-width":640,"medium_large-height":480,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","large-width":640,"large-height":480,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","1536x1536-width":640,"1536x1536-height":480,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","2048x2048-width":640,"2048x2048-height":480,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact-620x465.gif","card_image-width":620,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/thunderstorm_compact.gif","wide_image-width":640,"wide_image-height":480}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Daily risk of thunderstorm wind.\u00a0The risk value can roughly be interpreted as the probability that an extreme weather event will occur on that day within 80 km of the location.\u00a0A hosted imagery layer for use in visualization and analysis workflows is available here<\/a>.<\/p>\n"},{"acf_fc_layout":"image","image":{"ID":2663792,"id":2663792,"title":"tornado_compact","filename":"tornado_compact.gif","filesize":9167812,"url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather\/tornado_compact","alt":"","author":"5141","description":"","caption":"","name":"tornado_compact","status":"inherit","uploaded_to":2663452,"date":"2025-01-29 04:54:22","modified":"2025-01-29 04:54:22","menu_order":0,"mime_type":"image\/gif","type":"image","subtype":"gif","icon":"https:\/\/www.esri.com\/arcgis-blog\/wp-includes\/images\/media\/default.png","width":640,"height":480,"sizes":{"thumbnail":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact-213x200.gif","thumbnail-width":213,"thumbnail-height":200,"medium":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","medium-width":348,"medium-height":261,"medium_large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","medium_large-width":640,"medium_large-height":480,"large":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","large-width":640,"large-height":480,"1536x1536":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","1536x1536-width":640,"1536x1536-height":480,"2048x2048":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","2048x2048-width":640,"2048x2048-height":480,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact-620x465.gif","card_image-width":620,"card_image-height":465,"wide_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/01\/tornado_compact.gif","wide_image-width":640,"wide_image-height":480}},"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"

Daily risk of tornado.\u00a0The risk value can roughly be interpreted as the probability that an extreme weather event will occur on that day within 80 km of the location.\u00a0A hosted imagery layer for use in visualization and analysis workflows is available here<\/a>.<\/p>\n"},{"acf_fc_layout":"content","content":"

Potential Applications<\/strong><\/p>\n

Emergency managers can use these patterns to prioritize resources in high-risk areas during times that severe weather events are most likely to happen. City planners can incorporate these maps into infrastructure design, ensuring buildings and utilities can withstand extreme weather. Researchers can also identify trends tied to climate variability and change, highlighting areas for further study. The animations also help communicate weather risks to the public in an accessible format.<\/p>\n

Analysis details<\/strong><\/p>\n

This analysis builds on NOAA’s well-established techniques for severe weather climatologies<\/a> but uses Space Time Kernel Density to calculate probabilities of severe weather events across space and time. The input data comes from NOAA’s Storm Events Database<\/a>, which documents significant weather events with sufficient intensity to cause loss of life, injuries, or substantial property damage. The database contains point locations and includes date ranges for each event type: hail (1955\u20132023), tornadoes (1950\u20132023), and severe wind (1955\u20132023). In total, the dataset used includes 74,299 tornadoes, 494,107 severe wind events, and 379,295 hail events.<\/p>\n

These event points were processed using the Space Time Kernel Density<\/a> tool. For this analysis, a spatial bandwidth of 240 kilometers, a temporal bandwidth of 30 days, and a cell size of 80 kilometers were specified (roughly equivalent to the values used by NOAA). The tool calculates smoothed density values and converts them into expected counts, representing the probability of severe weather occurrence at each location over time. Additionally, the kernel smoothing helps address locational and temporal uncertainty in the dataset, mitigating inaccuracies in the reported event coordinates.<\/p>\n

Once the expected counts were generated by the kernel density tool, they were aggregated into recurring daily values (one map for each Julian day of the year) using the Aggregate Multidimensional Raster<\/a> tool. This step created daily averages for each location, representing the likelihood of severe weather on any given day of the year. The resulting data allows you to explore spatial and temporal patterns of severe weather risk while accounting for data limitations and seasonal variability.<\/p>\n"},{"acf_fc_layout":"content","content":"

Where to go from here<\/strong><\/p>\n

    \n
  1. Refine the study by adjusting spatial and temporal bandwidths to see how the patterns change.<\/li>\n
  2. Investigate additional types of severe weather events, such as flash floods or extreme heat, to expand the scope of the climatology.<\/li>\n
  3. Explore event severity, such as hailstone size or wind speeds, to better understand their spatiotemporal patterns.<\/li>\n<\/ol>\n

     <\/p>\n

    Severe weather is a persistent challenge, but with tools like Space Time Kernel Density and the power of GIS, we can improve our understanding of its patterns and prepare for its impacts. This workflow offers a practical starting point for analyzing severe weather.<\/p>\n"}],"related_articles":"","show_article_image":true,"card_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2020\/12\/storm-c.jpg","wide_image":false},"yoast_head":"\nDaily Risk of Severe Weather<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Daily Risk of Severe Weather\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.esri.com\/arcgis-blog\/products\/arcgis-pro\/analytics\/daily-risk-of-severe-weather\" \/>\n<meta property=\"og:site_name\" content=\"ArcGIS Blog\" \/>\n<meta property=\"article:publisher\" 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