Penn State University (PSU)

Domains

Horizontal

• 40.5 km (160 by 121) / 13.5 km (160 by 121)
• PSU1: 13.5 km grid
• 40.5 km (160 by 121) / 13.5 km (160 by 121) / 4.5 km (253 by 253)
• PSU2: 4.5 km grid
• PSU5: 13.5 km grid
• 40.5 km (160 by 121) / 13.5 km (160 by 121) / 4.5 km (253 by 253) / 1.5 km (253 by 253)
• PSU3: 1.5 km grid
• PSU4: 13.5 km grid

Vertical

35 full levels with model top at 10 mb

Atmosphere

Model: WRF ARW V2.2.1

Overview

The WRF model is designed to be a flexible, state-of-the-art, portable code that offers two dynamic solvers and numerous physics options. The Advanced Research WRF (ARW) solver primarily developed at NCAR, utilizes the Arakawa C grid on several different projections and a terrain-following mass coordinate. For this test, the ARW is configured using the Lambert projection with two, three or four moving, two-way interactive nested domains. For more detailed information on the ARW, please see Skamarock et al, 2005.

View a subset of the configuration

Initialization

The initial and boundary condition fields are created from GFS operational analysis from the initial time of each case listed in Table 1 using the WRF Pre-processing System (WPSv2). WRF-3DVar is used to perturb the initial and boundary conditions to generate a 30-member ensemble. The ensemble is then integrated for several hours to the time at which the airborne radar observation are available. Then the super-observation of NOAA P3 airborne radar Vr with EnKF are assimilated to the last time of the airborne observation. The model then starts to integrate using the EnKF analysis (mean) initial conditions with the GFS boundary conditions.

Lateral Boundary Conditions

6-h GFS forecast output on 1 deg grid

Physics

Cumulus	Grell-Devenyi
Microphysics	WSM6
PBL	YSU
Surface Layer	Monin-Obukov
Land Surface	thermal diffusion
Radiation	RRTM (longwave) / Dudhia (shortwave)

Ocean

None.

Archival

Entire model output archived.

References

Zhang, Fuiqing, Y. Weng, J. Sippel, Z. Meng, and C Bishop, 2009: Cloud-resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter. Mon. Wea. Rev. In Press.

Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang and J. G. Powers, 2005: A Description of the Advanced Research WRF Version 2. NCAR Technical Note TN-468+STR. 88 pp.