Service Storm Research Articles

Easing Into Norman: Ed Kessler on the Origins and Growth of the National Severe Storms Laboratory

The following essay is excerpted from conversations with Edwin Kessler, founding director of NOAA's National Severe Storms Laboratory in Norman, Oklahoma, conducted by Ariel Cohen and Stephen and Sarah Corfidi of the National Weather Service's Storm Prediction Center. Cohen and the Corfidis based the discussion in part on Kessler's speech at the 50th anniversary of the School of Meteorology at the University of Oklahoma in October 2010, and Cohen converted this discussion to a Q&A format. The conversations range over many subjects, including accomplishments of NSSL, Kessler's role as its manager, and the many people who were crucial to the lab's growth. The entire conversation is published by BAMS online, but here we've selected Kessler's words regarding the move to Norman, the community that evolved there, and how NSSL thrived there because of—and despite—the bureaucratic changes that federal agencies underwent at that time.—The Editors

When Do Losses Count?

Current global and national databases that monitor losses from natural hazards suffer from a number of limitations, which in turn lead to misinterpretation and fallacies concerning the “truthfulness” of hazard loss data. These biases often go undetected by end users and are generally a product of the type of information stored in loss databases and how they are constructed. This paper highlights some common shortcomings and root causes for data misinterpretation by asking what biases are present in existing databases and how these then manifest themselves in actual loss figures. For illustrative purposes, four widely used, nonproprietary, Web-based hazard databases are examined: the international Emergency Events Database (EM-DAT), the international Natural Hazards Assessment Network (NATHAN), the Spatial Hazard Events and Losses Database for the United States (SHELDUS), and the National Weather Service's Storm Events. We identify six general biases: hazard bias, temporal bias, threshold bias, accounting bias, geographic bias, and systemic bias. To achieve resilient and sustainable communities, we need systematic and comprehensive inventories at the national as well as international level, and data that are temporally and geographically comparable.