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RSAS Surface Grids



Gridded fields of surface variables are an effective and fundamental tool for meteorological analysis and prediction within the NWS operational community. They provide direct measurements of surface conditions, permit inference of conditions aloft, and often give crucial indicators of the potential for severe weather. Surface analyses are particularly valuable at the mesoscale where the frequency, completeness, and density of the surface data are unmatched among in situ observations.

The Rapid Update Cycle (RUC) Surface Assimilation Systems (RSAS) exploit the resolution of surface data by providing timely and detailed surface analyses updated twice per hour. Multiple runs per hour allow RSAS to first run earlier in the hour to provide more timely analyses (i.e. 5 minutes past the hour), and then later (21 minutes past the hour), to incorporate late arriving observations.

Other unique aspects of RSAS include speed, minimal disk space requirements, and a close fit to the observations. As a surface analysis-only system, RSAS produces a one-level, analysis-only grid and, therefore, requires very few compute resources. Also, because the system does not initialize a forecast model, the analysis is performed on the actual surface terrain and not along a model topography. Hence no model surface-to-station elevation extrapolations are required, all surface observations may be used, and the fit to the observations is maximized.

Since rough terrain can complicate the surface analyses, the RSAS system attempts to obtain an analyses with improved spatial continuity through careful choice of analysis methods and variables. RSAS, for example, incorporates elevation and potential temperature differences in the correlation functions used to model the spatial correlation of the surface observations. The resulting functions help to take into account physical blocking by mountainous terrain, and improve the representation of surface gradients. In addition, the analysis variables were chosen, whenever possible, in such a way as to minimize the effects of varying terrain. Potential temperature, for instance, is analyzed instead of surface temperature because it varies more smoothly over mountainous terrain when the boundary layer is relatively deep and well mixed.

The major pressure variable is a reduced pressure computed at each station location from altimeter setting observations. Station pressures calculated from the altimeter settings are reduced by using the 700-mb Eta temperature to estimate an effective surface temperature. This reduction generally provides smoother regional, diurnal, and seasonal variation since it avoids the use of actual surface temperatures, which are often unrepresentative of the surrounding conditions. Also, more data are available for analysis of the RSAS reduction because more stations report altimeter setting than report sea level pressure (SLP).

RSAS also provides an "NWS MSL Pressure" analysis (calculated directly from reported SLP observations), and a pressure change analysis produced by first calculating pressure change observations by differencing altimeter setting observations.

The RSAS domain incorporates a 15-km grid stretching from Alaska in the north to Central America in the south, and also covers oceanic areas.

Persistence (the previous hourly analysis) serves as the default background for the analysis in areas where surface observations are dense, i.e. CONUS. One-hour persistence provides an accurate forecast and allows the incorporation of previous surface observations into the analysis. It also assures continuity between analyses, especially near stations that report less frequently than hourly. Persistence, however, cannot be used in data-void or data-sparse areas. In these regions, gridded data from NCEP's NAM model are used as a background to ensure that the analysis does not stray far from reality. The NAM grids are linearly combined with 1-h persistence, using weights calculated to produce a smooth transition between data-dense and data-sparse areas. Verification statistics computed for parallel cycles of RSAS, one using a pure-model background, and the other a persistence/model blend, show that the use of persistence significantly improves the ability of the analysis to fit the observations, particularly in the western U.S.

Observations ingested into RSAS

Most observations contained in the in the RSAS domain are utilized. These include standard METARs, Coastal Marine Automated Network (C-MAN) observations, surface reports from fixed and drifting buoys, ships, and the NOAA Profiler Network, as well as surface observations from MADIS mesonets. Sophisticated quality control (QC) checks are employed to help screen the surface observations. Observations failing the checks are not ingested or analyzed by RSAS.

Geographic Coverage

The domain is a 15-km grid stretching from Alaska in the north to Central America in the south, and also covers significant oceanic areas.

Data Schedule

RSAS grids are updated every 15 minutes.


Typical daily volume for all MADIS datasets can be seen here.


No restrictions. All observations are publicly accessible.

RSAS Grids

Analyzed Grids produced by RSAS

Derived Grids produced by RSAS

Last updated 11 May 2017