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| Titel |
Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model |
| VerfasserIn |
K. Apodaca, M. Zupanski, M. Demaria, J. A. Knaff, L. D. Grasso |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 21, no. 5 ; Nr. 21, no. 5 (2014-10-10), S.1027-1041 |
| Datensatznummer |
250120945
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| Publikation (Nr.) |
 copernicus.org/npg-21-1027-2014.pdf |
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| Zusammenfassung |
| 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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