Articles | Volume 21, issue 5
Nonlin. Processes Geophys., 21, 1027–1041, 2014
https://doi.org/10.5194/npg-21-1027-2014
Nonlin. Processes Geophys., 21, 1027–1041, 2014
https://doi.org/10.5194/npg-21-1027-2014

Research article 10 Oct 2014

Research article | 10 Oct 2014

Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

K. Apodaca1, M. Zupanski1, M. DeMaria2,*, J. A. Knaff2, and L. D. Grasso1 K. Apodaca et al.
  • 1Colorado State University/Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado, USA
  • 2NOAA Center for Satellite Research and Applications, Fort Collins, Colorado, USA
  • *now at: Technology and Science Branch, National Hurricane Center, Miami, Florida, USA

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

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