2004
DOI: 10.1175/1520-0493(2004)132<0368:nsotfi>2.0.co;2
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Numerical Simulations of the Foehn in the Rhine Valley on 24 October 1999 (MAP IOP 10)

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Cited by 61 publications
(63 citation statements)
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“…In the majority of mesoscale models, numerical diffusion is specified as a fourth-order smoothing operator acting quasi-horizontally on the terrain-following coordinate surfaces. As was first recognized in high-resolution simulations investigating the interaction between föhn and cold-air pools, computing numerical diffusion in this way induces pronounced systematic errors over steep topography (Zängl, 2002;Zängl et al, 2004). The errors are particularly evident for temperature and moisture because these variables tend to have a strong vertical stratification.…”
Section: Model Improvements Triggered By Mapmentioning
confidence: 94%
“…In the majority of mesoscale models, numerical diffusion is specified as a fourth-order smoothing operator acting quasi-horizontally on the terrain-following coordinate surfaces. As was first recognized in high-resolution simulations investigating the interaction between föhn and cold-air pools, computing numerical diffusion in this way induces pronounced systematic errors over steep topography (Zängl, 2002;Zängl et al, 2004). The errors are particularly evident for temperature and moisture because these variables tend to have a strong vertical stratification.…”
Section: Model Improvements Triggered By Mapmentioning
confidence: 94%
“…The sub-kilometre resolution was found necessary in order to resolve fine-scale structures of the föhn flow and associated dynamical features such as hydraulic jumps (Jaubert and Stein, 2003). The föhn simulations of Zängl et al (2004a) indicate that mesoscale structures can successfully be simulated at moderate (1 km, in their case) horizontal resolution, but the details (e.g. nearsurface characteristics) exhibit considerable discrepancies between observation and simulation.…”
Section: Numerical Modellingmentioning
confidence: 99%
“…This was vital for the validation of the models. This network also contributed to the derivation of better initial conditions for research and NWP models (Zängl et al, 2004a;Jaubert et al, 2005) and the mesoscale analyzes extensively used for föhn investigations in the Rhine Valley (Drobinski et al, 2003a;Chimani et al, 2006;Flamant et al, 2006).…”
Section: Observation Networkmentioning
confidence: 99%