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

K. Apodaca, M. Zupanski, M. Demaria, J. A. Knaff, L. D. Grasso

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1027-1041
Number of pages15
JournalNonlinear Processes in Geophysics
Volume21
Issue number5
DOIs
StatePublished - Oct 10 2014
Externally publishedYes

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Geophysics
  • Geochemistry and Petrology

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