A real-time weather-adaptive 3DVAR analysis system for severe weather detections and warnings

Jidong Gao, Travis M. Smith, David J. Stensrud, Chenghao Fu, Kristin Calhoun, Kevin L. Manross, Jeffrey Brogden, Valliappa Lakshmanan, Yunheng Wang, Kevin W. Thomas, Keith Brewster, Ming Xue

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

A real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAA's Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the assessment of this type of 3DVAR analysis for use in severe weather warnings. The eventual goal of this real-time 3DVAR system is to help meteorologists better track severe weather events and eventually provide better warning information to the public, ultimately saving lives and reducing property damage.

Original languageEnglish (US)
Pages (from-to)727-745
Number of pages19
JournalWeather and Forecasting
Volume28
Issue number3
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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