The CIMSS Regional Assimilation System


The CIMSS Regional Assimilation System (CRAS) is a collection of computer programs designed to predict the weather. The system is 'Regional' in that it predicts the weather over a limited horizontal area, which can be set up to forecast for anywhere on the planet. The term 'Assimilation' refers to the system's ability to incorporate information from many different types of atmospheric observing systems which are used as input to the prediction model. Special algorithms have been developed that take advantage of observations from weather satellites. The CRAS has been modified and updated over the past fifteen years by scientists at CIMSS. The majority of these upgrades were validated using observations from satellites.

The CRAS is composed of three sub-systems. The first is called the pre-analysis. It collects large amounts of weather observations (winds, temperatures, humidity, clouds), checks them for errors, and analyzes them onto the 3-dimensional model grid. It also gathers and prepares gridded fields from a large scale model for use as initial and lateral boundary conditions. The Global Forecast System (GFS) from the National Centers for Environmental Prediction (NCEP) is typically used.

The second component is the forecast model. For the past fifteen years numerous improvements have been made to the prediction model (see Mills and Hayden, 1983; Bourke and McGregor, 1983; Leslie et al., 1985; Diak, 1987; Raymond, 1988; Diak et al. 1992). Since 1992 the CRAS has been run each day at the synoptic times of 00 and 12 UTC to generate near real-time forecasts numerous geographic areas. Horizontal resolutions range from 127 km (Northern Hemishpere), to 20 km (Midwestern U.S.). It is routinely initialized with conventional surface and radiosonde (balloon) observations (temperature, humidity, wind) and integrated water vapor and cloud data from the atmospheric sounders onboard the GOES-11 and GOES-12 geostationary satellites. A 12-hr spinup forecast is used so that five satellite observation data sets at three hour intervals can be incorporated. These forecasts are routinely compared to those without GOES data to determine The impact of the satellite data on the forecast.

The third component is the post-processor. It takes output from the CRAS forecast model and generates numerous products for use by forecasters, researchers, and can be viewed on various web pages. It also creats 4D datasets for viewing with Vis5d, a powerful, interactive visualization tool that displays multivariable, four-dimensional gridded datasets. Some of Vis5D's capabilities are demonstrated in this Vis5D animation produced from a 48-hour CRAS simulation of a heavy precipitation event over Northern Wisconsin on July 7, 2000. Iso-surfaces of cloud (gray) and precipitation (blue) are shown. The animation is restarted four times to demonstrate the scroll and pan capabilities of Vis5D.


Here is a list of publications that refer to features in the CRAS or to studies performed with the CRAS.

Ackerman, S. A. and G. L. Stephens, 1987: The absorption of solar radiation by cloud droplets: An application of anomolous 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.


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


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.


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


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.


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.


Please refer questions or comments to the CRASmaster at