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The Impact of Lightning Data Assimilation on Deterministic and Ensemble Forecasts of Convective Events



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  • Journal Title:
    Journal of Atmospheric and Oceanic Technology
  • Personal Author:
  • NOAA Program & Office:
    OAR (Oceanic and Atmospheric Research)
  • Description:
    A general lightning data assimilation technique is developed and tested with observations from the World Wide Lightning Location Network (WWLLN). The technique nudges the water vapor mixing ratio toward saturation within 10 km of a lightning observation and is more general than other approaches that require specific model microphysics or flash rates. This approach is applied to both deterministic and ensemble forecasts of the 29 June 2012 derecho event over the eastern United States and a deterministic forecast of the 17 November 2013 convective event over the Midwest using the Weather Research and Forecasting (WRF) Model run at a convection-permitting resolution. Lightning data are assimilated over the first three hours of the forecasts, and the subsequent impact on forecast quality is evaluated. For both events, the deterministic simulations with lightning-based nudging produce more realistic predicted composite reflectivity fields. For the forecasts of the 29 June 2012 event using ensemble data assimilation, forecast improvements from lightning assimilation were more modest than for the deterministic forecasts, suggesting that lightning assimilation may produce greater improvements in convective forecasts where conventional observations (e.g., aircraft, surface, radiosonde, satellite) are less dense or unavailable.
  • Source:
    Journal of Atmospheric and Oceanic Technology, 33(9), 1801-1823
  • DOI:
  • ISSN:
    0739-0572 ; 1520-0426
  • Format:
    PDF
  • Publisher:
    American Meteorological Society
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA10OAR4320148
  • Rights Information:
    Other
  • Compliance:
    Library
  • Main Document Checksum:
    urn:sha-512:ca373150f66ef7a7faeabc36d53a598d86ca324ee632a8c35f8490a4d540fed958b1be758072fb41389c3894241930a34d8f9c35a66df23d578e15e2d35c9df5
  • Download URL:
  • File Type:
    Filetype[PDF - 20.93 MB ]
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