The CIMSS Regional Assimilation System (CRAS)

Real Time North American Run (CRAS45)

Updated 14 November 2008

Overview
CRAS is a regional numerical weather prediction system used to assess the impact of space-based observations on numerical forecast accuracy. The CRAS is unique from other mesoscale models in that its development was guided by validating forecasts using information from satellites.

Implementation date: 12 UTC 14 Apr 2008

Configuration: AWIPS grid number: Superset of NCEP 104 grid, double resolution

Domain: 280 columns x 180 rows x 38 levels

Grid length at 60N is 45.377 km.

Grid type is Arakawa C.

Levels: CRAS45 has 38 sigma levels in the vertical with a floating top at sigma = .036. The lowest sigma level is at .994.

Map projection parameters: North Pole at (140.5,218.5), rotation = 105W, grid length = 45.337, true at 60N

Topography calculated from 2-minute USGS global database.

Fixed surface fields are determined from 1-degree monthly climatology from GWEX and 2-minute USGS 25 vegetation category dataset. This includes albedo, roughness length, deep soil temperature, soil moisture, permanent sea ice, greenness, and leaf area index.

Forecast duration
84-hour forecast generated at 00z and 12z with output interval of 3-hours.

Boundary conditions: NCEP GFS forecast at 6-hour intervals.

Analysis:
A 12-hour spin-up forecast is used with the CRAS45 to adjust water vapor and clouds. Satellite information is inserted at the mid-time of each individual satellite scan. Water vapor, cloud and precipitation mixing ratio from the 12-hour CRAS45 spin-up are merged with 6-hour forecast GFS winds and temperatures from the previous GFS run, i.e. the 6-hour forecast grids from the 06z GFS run are used in the 12z CRAS45. The merge is conducted on 25 hPa pressure surfaces up to 10 hPa.

Observation types used in by the CRAS45

3-layer precipitable water from the GOES sounders (hourly) or MODIS (10-14 passes per day) are used to adjust water vapor columns in clear fields of view.
Cloud-top pressure and cloud effective amount from the GOES sounders (hourly) or MODIS (10-14 passes per day) are used to clear clouds or build 3D cloud fields.
METAR (surface temperature, dewpoint, winds)
Daily RTG SSTs from NCEP
Daily Integrated Multi-Sensor (IMS) snow cover from NESDIS
Deferred until later:
Hourly precipitation from NCEP analysis
Cloud-track and water vapor winds from GOES
Layer thicknesses from GOES sounder retrievals

Surface data
Surface observations are objectively analyzed to the model grid and assigned to those pressure levels that lie below the model topography. This is done to eliminate spurious values when the predicted variables are interpolated from the analysis pressure levels to the model sigma levels.

Cloud assimilation (Bayler et al., 2000)
GOES cloud-top pressure (CTP) and effective cloud amount (ECA) treatment. GOES CTP and ECA are first binned to the model grid. The distribution of CTP is examined to identify high, low, or high and low cloud types. High and/or low clouds are constructed using the CRAS cloud physics as an upper constraint. ECA is used to determine the cloud mass to be added to the model grid. Model background clouds remain untouched when the averaged GOES CTP matches the background. For clear fields of view model clouds are cleared down to surface and the column humidity is checked and adjusted to insure that clouds will not instantly reappear on the next model time step. Model clouds lying above the GOES CTP are cleared.

Water vapor assimilation
Variational moisture adjustment using 3-layer precipitable water (PW) retrievals from GOES. Model mixing ratio columns are adjusted to reflect the three retrieved layers of PW. A 1-D variational approach is used that preserves model generated lapse rates while adjusting the mixing ratio. RH of 95% is not exceeded since the retrievals exist in clear fields of view only.

Other observations used by CRAS45

Soil moisture is held constant at the climatological value except where precipitation has fallen within the forecast run. Deep soil temperature is held constant at the value prescribed by the monthly climatology.
Daily SSTs are objectively analyzed to the model grid.
Daily snow cover and permanent sea ice are objectively analyzed to the model grid. Water grid points that are identified as ice covered are changed to snow-covered land points.

Initialization
Vertical normal mode (Bourke and McGregor, 1983)

Model Dynamics

Semi-implicit time scheme (McGregor et al., 1978), 3rd order time filter (Raymond, 1991)
The advective form of the equations of motion are used (Leslie, et.al., 1985). Horizontal advection of cloud and precipitation are included.
Pseudo-non-hydrostatic, parameterized rain drag (Raymond and Aune, 1998)
6th order tangent filter replaces horizontal diffusion (Raymond, 1988). Cloud and precipitation are not filtered.

Model Physics

Convective parameterization

Modified Kuo type (Raymond and Aune, 2003). Convective cloud and precipitation are mapped onto grid. Static stability is used to define cloud base and top. Cloud is distributed from test layer to layer designated as convective cloud top.
Moistening of water vapor in the grid column due to convection has been eliminated. Cloud evaporation compensates for this on subsequent time steps.
Model layers below test layer specified as convective moisture source.

Bulk mixed-phase cloud microphysics

Explicit cloud and precipitation microphysics (Raymond, 1995), with diagnosed liquid/ice phase (Dudhia, 1989).
Precipitation fall velocity using sub-timestep loop (Liu and Orville, 1969).
Water/ice cloud sedimentation (Lee, 1992).
Collision-coalescence, precipitation evaporation and autoconversion micro-physics follows Sundquist, 1989. RH limits for cloud evaporation vary with temperature. RH for cloud condensation is less than 100% in the boundary layer.
Shallow convection scheme is turned off. The non-local turbulence scheme drives the formation of single layer cloud fields.

Shortwave and longwave radiation

Surface shortwave (visible and near IR) and longwave fluxes computed using the two-stream model of Ackerman and Stephens, 1987.

Land-surface processes

Vertical turbulent exchange is estimated using a K-theory scheme modified by a turbulent kinetic energy parameter calculated using a non-local approach (Raymond, 1999).
Skin temperature is predicted using a viscous layer approach. Surface sensible and latent-heat fluxes are parameterized.
5-layer soil model with the bottom layer held constant (Kondo et al., 1990 and Lee and Pielke, 1992).

Post-processing

Forecast products are output as binary files, McIDAS gridfiles, Vis5D format, and grib2. Unix utilities are used to generate gif files for viewing on the web. Output products in grib2 format are transferred to AWIPS.

New products

11 micron (window channel) images are generated using a transmissivity calculation starting with the surface skin temperature and integrating upward. A correction for water vapor is not included at this time.
A 6.7 micron water vapor image is generated using a GOES sounder forward radiative transfer model. An ozone climatology is used at this time.
Instantaneous rain rate is output directly from the model. They can be displayed using radar intensity color scale.
Sky cover (0-100%) is computed using an experimental algorithm that computes effective cloud amount and area coverage from the CRAS predicted cloud mixing ratio.

References

Ackerman, S. A. and G. L. Stephens, 1987: The absorption of solar radiation by cloud droplets: An application of anomalous diffraction theory. J. Atmos. Sci., 44, 1574-1588.

Bayler, G., R. M. Aune and W. H. Raymond, 2000: NWP cloud initialization using GOES sounder data and improved modeling of nonprecipitating clouds. Mon. Wea. Rev. 128, 3911-3920.

Bourke,W.P. and J.L. McGregor, 1983: A non-linear vertical mode initialization scheme for a limited area prediction model. Mon. Wea. Rev., 111, 1749-1771.

Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077-3107.

Kondo, J., N. Saigusa, and S. Takeshi, 1990: A parameterization of evaporation from bare soil surfaces. J. Appl. Meteor., 29, 385-389.

Lee, I. Y., 1992: Comparison of cloud microphysics parameterizations for simulation of mesoscale clouds and precipitation. Atmos. Enviro., 26A, 2699-2712.

Lee, T. L. and R. A. Pielke, 1992: Estimating the soil surface specific humidity. J. Appl. Meteor., 31, 480-484.

Leslie, L. M., L. W. Logan, D. J. Gauntlett, G. A. Kelly, J. L. McGreggor, and M. J. Manton, 1985: A high resolution primitive equation NWP model for operations and research. Aust. Meteo. Mag., 33, 11-35.

McGregor, J. L., L. M. Leslie and D. J. Gauntlett, 1978: The ANMRC limited area model: Consolidated formulation and operational results. Mon. Wea. Rev., 106, 427-438.

Raymond, W. H., 1988: High-order low-pass implicit tangent filters for use in finite area calculations. Mon. Wea. Rev., 116, 2132-2141.

Raymond, W. H., W. S. Olsen, and G. Callan, 1995: Diabatic forcing and initialization with assimilation of cloud and rainwater in a forecast model. Mon. Wea. Rev., 123, 366-382.

Raymond, W. H., and R. M. Aune, 1998: Improved precipitation forecasts using parameterized feedbacks in a hydrostatic forecast model. Mon. Wea. Rev., 126, 693-710.

Raymond, W. H., 1999: Non-local turbulent mixing based on convective adjustment concepts (NTAC). Bound-layer Meteor, 92, 263-291.

Raymond, W. H., 2000: Moisture advection using relative humidity. J. Appl. Meteor., 39, 2397-2408.

Raymond, W. H., and R. M. Aune, 2003: Conservation of moisture in a hybrid Kuo-type cumulus parameterization. Mon. Wea. Rev., 131, 771-779.

Schreiner, A. J., T. J. Schmit, R. M. Aune, 2002: Maritime inversions and the GOES sounder cloud product. Nat. Wea. Dig., 26, 27-38.

Stephens, G. L., D. L. Jackson, and J. J. Bates, 1994: A comparison of SSM/I and TOVS column water vapor data over the global oceans. Meteor and Atmos. Phys., 54, 183-201.

Sundqvist, H., E. Berge, and J. E. Kristjansson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117, 1641-1659.

For more information about the CRAS, contact the CRASmaster at cras@ssec.wisc.edu.