Accession Number:

AD1064159

Title:

Identification and Selection of Representative Storm Events from a Probabilistic Storm Data Base

Descriptive Note:

Technical Report

Corporate Author:

ERDC VICKSBURG United States

Personal Author(s):

Report Date:

2018-01-01

Pagination or Media Count:

10.0

Abstract:

PURPOSE This Coastal and Hydraulics Engineering Technical Note CHETN provides guidance on the use of a probabilistic storm data base for the development of environmental forcing information for use in probabilistic life-cycle analysis PLCA tools such as Beach-fx Gravens et al. 2007 or Generation Two Coastal Risk Model G2CRM currently under development at the U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory ERDC-CHL, and Institute for Water Resources IWR. In recent years large probabilistic storm data sets including storm surge hydrographs and coincident wind wave information have been generated using high-fidelity numerical models e.g., ADCIRC, STWAVE for purposes of characterizing the storm climatology in support of coastal storm risk assessments including updates of the Federal Emergency Management Agency Flood Hazard Mapping Program and the U.S. Army Corps of Engineers North Atlantic Comprehensive Coastal Study NACCS. The high-fidelity numerical hydrodynamic and wind wave modeling results from these studies are archived for analysis and project use purposes in the Coastal Hazards System CHS httpschs.erdc.dren.mildefault.aspx. The CHS is a national, coastal, storm-hazard data storage and mining system. It stores comprehensive, high-fidelity, storm-response computer modeling results including climatology, surge, total water levels, and waves as well as measurements. Extremal statistics and epistemic uncertainties on the processes are also stored, and the data are easily accessed, mined, plotted, and downloaded through a user-friendly web interface. The purpose of this CHETN is to provide a methodology to identify and select a small number 12 to 36 of representative storm events from the considerably larger probabilistic storm data base.

Subject Categories:

  • Meteorology
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE