The Impact of Topography on the Initial Error Growth Associated with Moist Convection
Author:
Affiliation:
1. Disaster Prevention Research Institute, Kyoto University
Publisher
Meteorological Society of Japan
Subject
Atmospheric Science
Link
https://www.jstage.jst.go.jp/article/sola/17/0/17_2021-024/_pdf
Reference22 articles.
1. Bachmann, K., C. Keil, and M. Weissmann, 2019: Impact of radar data assimilation and orography on predictability of deep convection. Quart. J. Roy. Meteor. Soc., 145, 117-130, doi:10.1002/qj.3412.
2. Bachmann, K., C. Keil, G. C. Craig, M. Weissmann, and C. A. Welzbacher, 2020: Predictability of deep convection in idealized and operational forecasts: Effects of radar data assimilation, orography, and synoptic weather regime. Mon. Wea. Rev., 148, 63-81, doi:10.1175/MWR-D-19-0045.1.
3. Bierdel, L., T. Selz, and G. C. Craig, 2018: Theoretical aspects of upscale error growth on the mesoscales: Idealized numerical simulations. Quart. J. Roy. Meteor. Soc., 144, 682-694, doi:10.1002/qj.3236.
4. Carbone, R. E., J. D. Tuttle, D. A. Ahijevych, and S. B. Trier, 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci., 59, 2033-2056.
5. Dowell, D. C., L. J. Wicker, and C. Snyder, 2011: Ensemble Kalman filter assimilation of radar observations of the 8 May 2003 Oklahoma City supercell: Influences of reflectivity observations on storm-scale analyses. Mon. Wea. Rev., 139, 272-294.
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