Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case
Author:
Publisher
Springer Science and Business Media LLC
Subject
Atmospheric Science
Link
https://link.springer.com/content/pdf/10.1007/s00376-023-2340-2.pdf
Reference57 articles.
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2. Anderson, J. L., and N. Collins, 2007: Scalable implementations of ensemble filter algorithms for data assimilation. J. Atmos. Oceanic Techol., 24, 1452–1463, https://doi.org/10.1175/JTECH2049.1.
3. Apodaca, K., M. Zupanski, M. DeMaria, J. A. Knaff, and L. D. Grasso, 2014: Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model. Nonlinear Processes in Geophysics, 21, 1027–1041, https://doi.org/10.5194/npg-21-1027-2014.
4. Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The rapid refresh. Mon. Wea. Rev., 144, 1669–1694, https://doi.org/10.1175/MWR-D-15-0242.1.
5. Bruning, E. C., and Coauthors, 2019: Meteorological imagery for the geostationary lightning mapper. J. Geophys. Res., 124, 14 285–14 309, https://doi.org/10.1029/2019JD030874.
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