The MSAS/RSAS Surface Analysis

Overview

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 MSAS and RSAS systems exploit the resolution of surface data by providing timely and detailed surface analyses. MSAS runs operationally at NWS forecast offices as part of the Advanced Weather Interactive Processing System (AWIPS). RSAS runs operationally at the NWS National Centers for Environmental Prediction (NCEP). RSAS also runs quasi-operationally in the ESRL/GSD Central Facility. The ESRL/GSD RSAS is also used to quality control surface observations distributed through the Meteorological Assimiliation Data Ingest System (MADIS), and as the development system where new advances can be incorporated before being put into operations at NCEP. RSAS is currently the only data assimilation system at NCEP providing subhourly updates to its gridded output, 5 minutes past the hour for more timely analyses, and the 20 minutes past for late arriving observations. At ESRL/GSD, RSAS provides subhourly updates every 15 minutes. All three systems have the advantages of speed and closer fit to the observations. They produce a one-level, analysis-only grid and, therefore, require very few compute resources. On the IBM SP at NCEP, RSAS requires only about 3 minutes (wall) on one processor to complete its grid generation. On AWIPS, MSAS takes between 1-3 minutes depending on the varying load on the system. Also, because the systems do 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 MSAS/RSAS systems attempt to obtain analyses with improved spatial continuity through careful choice of analysis methods and variables. MSAS/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. For an example of the effect of the MSAS correlation functions, and the influence of terrain, on a potential temperature analysis, click here. For an example of the ability of the correlation functions to influence the representation of surface gradients, click here.

The major pressure variable is a reduced pressure computed (by MSAS/RSAS) 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 MSAS reduction because more stations report altimeter setting than report sea level pressure (SLP).

MSAS/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. AWIPS users at each forecast office can choose the level of the MSAS pressure reduction (e.g. 1500m or sea level), and the time interval used in the MSAS pressure change analysis (e.g. 1-h or 3-h pressure change). By default, the "MSAS MSL Pressure" and "3hr Pressure Change" are the variables of choice, but each office can change that default by simply editing a file and relocalizing.

Although the domain and resolution configurations of the MSAS/RSAS systems are flexible, in default mode, the AWIPS system provides hourly analyses on a 60-km grid covering the 48 contiguous states (CONUS) and neighboring areas of Canada and Mexico. However, each forecast office can modify the location, size, and resolution of its local MSAS domain, and also the model background utilized in the MSAS analyses. Changes in the domain size are linked to changes in the grid resolution in such a way as to minimize AWIPS impacts and guarantee that overall MSAS computational demands remain the same. For example, forecast offices can choose a 15-km, regional-scale domain, or a 60-km CONUS domain, but not a 15-km CONUS domain. MSAS also supports domains outside the Continental U.S., i.e. Alaska and Puerto Rico. The RSAS systems at NCEP and ESRL/GSD feature a larger domain, which incorporates a 15-km grid stretching from Alaska in the north to Central America in the south, and also covers significant oceanic areas.

In all three systems, 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 Eta model are used as a background to ensure that the analysis does not stray far from reality. The Eta 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. For the MSAS system on AWIPS, the default background over CONUS is a linear combination of persistence and Eta-211, but forecast offices can specify that the GFS-213 be incorporated in the background grid, either in combination with persistence, or alone as a pure-model background. Off CONUS, the default background is a pure-model grid, using Eta-207 in Alaska and GFS-213 in Puerto Rico. Alaska sites can also choose GFS-213 to be used in the background grid.

For more information on the AWIPS customization options see the MSAS localization documentation. For more information on RSAS, see the RSAS Technical Procedures Bulletin.

Observations ingested into MSAS/RSAS

Most observations contained in their domains are utilized by MSAS and RSAS. 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 any local mesonets ingested through the AWIPS LDAD system at NWS forecast offices. The ESRL/GSD RSAS system also ingests the highly-dense MADIS integrated mesonet data. Sophisticated quality control (QC) checks are employed to help screen the surface observations. Observations failing the checks are not ingested or analyzed by MSAS.

On AWIPS, the results of the MSAS QC checks are passed to the Quality Control and Monitoring System (QCMS) for AWIPS display and QC database production. In addition, MSAS ingests and respects the QCMS subjective intervention lists maintained at each WFO. Observations listed in the QCMS subjective reject list, are not analyzed by MSAS, while observations listed in the QCMS subjective accept list are always analyzed, regardless of the outcome of the automated QC checks.

Analyzed Grids produced by MSAS/RSAS

Derived Grids produced by NCEP RSAS

Derived Grids produced by ESRL/GSD RSAS (and available via MADIS)

Derived Grids produced by MSAS


Last updated 10 December 2007.
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