Assessing advances in the assimilation of radar data and other mesoscale observations within a collaborative forecasting-research environment

John S. Kain, Ming Xue, Michael C. Coniglio, Steven J. Weiss, Fanyou Kong, Tara L. Jensen, Barbara G. Brown, Jidong Gao, Keith Brewster, Kevin W. Thomas, Yunheng Wang, Craig S. Schwartz, Jason J. Levit

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

The impacts of assimilating radar data and other mesoscale observations in real-time, convection-allowing model forecasts were evaluated during the spring seasons of 2008 and 2009 as part of the Hazardous Weather Test Bed Spring Experiment activities. In tests of a prototype continental U.S.-scale forecast system, focusing primarily on regions with active deep convection at the initial time, assimilation of these observations had a positive impact. Daily interrogation of output by teams of modelers, forecasters, and verification experts provided additional insights into the value-added characteristics of the unique assimilation forecasts. This evaluation revealed that the positive effects of the assimilation were greatest during the first 3-6 h of each forecast, appeared to be most pronounced with larger convective systems, and may have been related to a phase lag that sometimes developed when the convective-scale information was not assimilated. These preliminary results are currently being evaluated further using advanced objective verification techniques.

Original languageEnglish
Pages (from-to)1510-1521
Number of pages12
JournalWeather and Forecasting
Volume25
Issue number5
DOIs
StatePublished - Oct 2010

Keywords

  • Convection
  • Data assimilation
  • Field experiments
  • Forecast verification
  • Radars/radar observations

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