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James W. Wilson and Rita D. Roberts

realistic squall-line structures, and explain this in terms of the inability to realistically capture nonhydrostatic effects in convection at horizontal grid spacings greater than about 4 km. During IHOP the RUC10 ran and assimilated every hour a number of special datasets including mesonets (like the Oklahoma Mesonet), profilers (including Radio Acoustic Sounding System), integrated precipitable water from GPS sites, satellite cloud drift winds, VAD winds from Doppler radar, and winds and temperatures

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Jianping Li and Ruiqiang Ding

of variable X of the Lorenz63 model as a function of the number of data points. Because of large fluctuations in error growth when a small number of data points is used, the predictability limit here is defined as the time at which the error reaches 90% of its saturation level, in order to reduce the effects of sampling fluctuations. The dashed line represents the predictability limit obtained using 2 × 10 4 data points. In a real situation, the number of experimental or observational data

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Paul J. Neiman, F. Martin Ralph, M. A. Shapiro, B. F. Smull, and D. Johnson

–28 March 1991. Bull. Amer. Meteor. Soc., 73, 459–472. Wang, P.-Y., J. E. Martin, J. D. Locatelli, and P. V. Hobbs, 1995: Structure and evolution of winter cyclones in the central United States and their effects on the distribution of precipitation. Part II: Arctic fronts. Mon. Wea. Rev., 123, 1328–1344. Weber, B. L., and D. B. Wuertz, 1991: Quality control algorithm for profiler measurements of winds and temperatures. NOAA Tech. Memo. ERL WPL

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Da-Lin Zhang, Kun Gao, and David B. Parsons

and Fritsch (1986,1988b), to demonstrate that the meso-lg scale structureand evolution of MCSs can be numerically simulatedfrom conventional meteorological observations if compatible grid resolution, reasonable model physics andinitial conditions are utilized, and to provide muchneeded understanding of the dynamics of MCSs. In addition, the observational PRE-STORM network, whichprovided supplemental rawinsondes, surface reports,conventional and Doppler radars, acoustic sounders,profilers and

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Patrick S. Skinner, Christopher C. Weiss, Louis J. Wicker, Corey K. Potvin, and David C. Dowell

supercells is the presence of large errors in the pressure field of imperfect, compressible models ( Tong and Xue 2005 ), which are likely attributable to the generation of acoustic waves by dynamic imbalances in the analyses ( Potvin and Wicker 2013b ). Despite these large errors in the EnKF pressure analyses, if the pressure errors do not degrade the three-dimensional depictions of the wind and buoyancy fields provided by the EnKF ( Potvin and Wicker 2013b ), a diagnostic pressure equation can be

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Kevin J. Nelson, David B. Mechem, and Yefim L. Kogan

consistency through the precipitation and coalescence-scavenging scalings gives valuable insight into the resolution dependence of simulation results beyond the basic comparison between model and observations. 2. Methodology a. Model and domain configuration All simulations employ NRL COAMPS ( Hodur 1997 ), which is based on nonhydrostatic, compressible dynamics. COAMPS integrates acoustically active terms using a mode-splitting technique and uses a 1.5-order “level 2.5” turbulence closure ( Mellor and

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Brian A. Klimowski

through theexamination of vertical profiles of momentum flux. Asimilar convective circulation was also presented byNicholls ( 1987 ) through examination of the pressureresponse to the buoyancy of a rising thermal (see Nicholls 1987, his Fig. 21 ). The second formation mechanism suggested by Smull and Houze (1987) was thata midlevel mesoscale low in the stratiform region (generated from the combined effects of latent heat releasein the mesoscale updraft above the low and evaporativeCooling enhanced by

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Steven M. Cavallo, Judith Berner, and Chris Snyder

. 2004 ; Williamson et al. 2005 ; Williamson and Olson 2007 ; Hannay et al. 2009 ; Medeiros et al. 2012 ). Williamson et al. (2005) compared moisture tendencies averaged over many forecasts from CAM, version 2 (CAM2), with estimates from observations from two intensive observing periods (IOPs) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site. They found that the largest forecast errors formed from fast processes that occurred within the first 24 h, and that subsequent

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Alex Schueth, Christopher Weiss, and Johannes M. L. Dahl

(numerical) mixing as well as the effect of the subgrid-scale (SGS) turbulence closure. The parcels are forward integrated in the model using the RK2 scheme ( Wicker and Skamarock 2002 ) and averaged winds to mitigate the effect of acoustic waves. The vorticity tendencies are calculated for each grid point and then trilinearly interpolated to the parcel location at each model time step. 1 These five vorticity tendencies make up the entire vorticity budget and have been custom added into CM1 as output

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Brett Soderholm, Bryn Ronalds, and Daniel J. Kirshbaum

larger time step for the integration of meteorological processes and a smaller time step to maintain stability of acoustic modes. We use sixth-order centered horizontal advection along with fifth-order vertical advection with implicit diffusion. To diminish the spurious effects of poorly resolved waves, we apply sixth-order horizontal diffusion with a coefficient of 0.12. Unless otherwise specified, all simulations use the following subgrid parameterization schemes: a 1.5-order TKE subgrid mixing

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