 |
| Titel |
Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model |
| VerfasserIn |
K. Apodaca, M. Zupanski, M. Demaria, J. A. Knaff, L. D. Grasso |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
2198-5634
|
| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 1, no. 1 ; Nr. 1, no. 1 (2014-05-13), S.917-952 |
| Datensatznummer |
250115097
|
| Publikation (Nr.) |
 copernicus.org/npgd-1-917-2014.pdf |
|
|
|
|
|
| Zusammenfassung |
| Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the
Goestationary 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), improving initial conditions, and partially improving
WRF-NMM forecasts during several data assimilation cycles. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|