The National Weather Service (NWS) Damage Assessment Toolkit (DAT) has been utilized experimentally since 2009 to assess damage following tornadoes and convective wind events. The DAT is a GIS-based framework for collecting, storing, and analyzing damage survey data, utilizing the Enhanced Fujita (EF) scale for the classification of damage. Data collected from individual locations via mobile device are transmitted to a central geospatial database where they are quality controlled and analyzed to assign the official EF rating. In addition to the individual point, the data are analyzed to generate track centerlines and damage swaths. High resolution satellite imagery and radar data, through partnership with the NASA Short-term Prediction Research and Transition Center, are also available to aid in the analysis. The subsequent dataset is then made available through a web-based graphical interface and GIS services.

Through 2016, experimental use of the DAT has spread across most of the Southern and Central Regions of the NWS, with more gradual implementation across the Eastern and Western Regions. This usage has resulted in over 38,000 quality controlled damage points being collected. Data from the DAT have been used by a wide variety of end users, including emergency management agencies (Federal and State), the research community, as well as the reinsurance industry. Data from a sample of damage surveys will be presented, along with example end user cases. In addition, the current status of the DAT will be addressed, along with an outline of future plans as the DAT is transitioned to operational status.

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Parks Camp


While the data has been quality controlled, it is still considered preliminary. Official statistics for severe weather events can be found in the Storm Data publication, available from the National Climate Data Center (NCDC) at:


J. Parks Camp, NWSFO, Tallahassee, FL; and P. Kirkwood, J. G. LaDue, L. A. Schultz, and N. Parikh., National Weather Service Damage Assessment Toolkit: Transitioning to Operations, Abstract 9.1 presented at 2017 Annual Meeting, AMS, Seattle, Washington, 26 Jan.

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