Flash flooding is a high impact weather event that requires clear communication regarding severity and potential hazards among forecasters, researchers, emergency managers, and the general public. Current standards used to communicate these characteristics include return periods and the United States (U.S.) National Weather Service (NWS) 4-tiered river flooding severity scale. Return periods are largely misunderstood, and the NWS scale is limited to flooding on gauged streams and rivers, often leaving out heavily populated urban corridors. To address these shortcomings, a student-led group of interdisciplinary researchers came together in a collaborative effort to develop an impact-based Flash Flood Severity Index (FFSI). The index was proposed as a damage-based, post-event assessment tool, and preliminary work toward the creation of this index has been completed and presented here. Numerous case studies were analyzed to develop the preliminary outline for the FFSI, and three examples of such cases are included in this paper. The scale includes five impact-based categories ranging from Category 1 very minor flooding to Category 5 catastrophic flooding. Along with the numerous case studies used to develop the initial outline of the scale, empirical data in the form of semi-structured interviews were conducted with multiple NWS forecasters across the country and their responses were analyzed to gain more perspective on the complicated nature of flash flood definitions and which tools were found to be most useful. The feedback from these interviews suggests the potential for acceptance of such an index if it can account for specific challenges.
Flash flooding is one of the most costly and deadly natural hazards in the United States and across the globe. This study advances the use of high-resolution quantitative precipitation forecasts (QPFs) for flash flood forecasting. The QPFs are derived from a stormscale ensemble prediction system, and used within
Convection-allowing models offer forecasters unique insight into convective hazards relative 4 to numerical models using parameterized convection. However, methods to best characterize 5 the uncertainty of guidance derived from convection-allowing models are still unrefined. This 6 paper proposes a method of deriving calibrated probabilistic forecasts of rare events from 7 deterministic forecasts by fitting a parametric kernel density function to the model's histor-8 ical spatial error characteristics. This kernel density function is then applied to individual 9 forecast fields to produce probabilistic forecasts.
This article aims to update nurses who are experienced in obtaining cervical samples and stimulate interest in health professionals who wish to undertake the procedure in the future. It also provides information on the NHS cervical screening programme and its relevance in the reduction of cervical cancer. It should help the reader to understand the nature, importance and prevention of cervical cancer, and the techniques for detection and treatment of pre-cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.