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Method for Recording Broadband High Resolution Emission Spectra of Laboratory Lightning Arcs
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A simple lightning assimilation technique for improving retrospective WRF simulations.

Nicholas K Heath1, Jonathan E Pleim1, Robert C Gilliam1

  • 1Computational Exposure Division, National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.

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Lightning assimilation significantly improves weather model rainfall predictions, reducing summer biases and enhancing accuracy. This technique forces convective parameterizations where lightning occurs, leading to better retrospective modeling results.

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Area of Science:

  • Atmospheric Science
  • Meteorological Modeling
  • Weather Forecasting

Background:

  • Convective rainfall simulations often contain errors, particularly positive rainfall biases during summer months.
  • Overactive convective parameterizations in models like the Weather Research and Forecasting (WRF) model contribute to these biases.

Purpose of the Study:

  • To improve retrospective rainfall simulations using the WRF model by applying lightning assimilation.
  • To assess the impact of assimilating lightning data on the Kain-Fritsch (KF) convective scheme.

Main Methods:

  • Lightning assimilation was implemented in the KF scheme, forcing deep convection with observed lightning and optionally suppressing it where lightning was absent.
  • WRF model simulations were conducted over the continental United States for July 2012, July 2013, and January 2013, with and without lightning assimilation.
  • Simulations were evaluated against NCEP stage-IV precipitation data and MADIS near-surface meteorological observations.

Main Results:

  • Lightning assimilation considerably improved summertime rainfall simulations, reducing the 6-hour accumulated rainfall bias from 0.54 to 0.07 mm in July 2012.
  • Spatial correlation of rainfall simulation increased from 0.21 to 0.43 with lightning assimilation.
  • Statistical measures for near-surface meteorological variables also showed improvements, with consistent positive results for July 2013.

Conclusions:

  • Lightning assimilation is a promising technique for substantially improving warm-season rainfall simulations in retrospective WRF applications.
  • The straightforward approach of forcing convection based on lightning observations enhances model accuracy.
  • This method offers a pathway to reduce common biases in convective rainfall modeling.