The Real-Time Mesoscale Analysis is designed to provide the best 5-km gridded estimate of current surface and near-surface conditions on an hourly basis in support of National Weather Service activities and the NWS National Digital Forecast Database (NDFD). Even with availability of increasingly dense mesonet observations, the RTMA must incorporate a 3-d atmospheric/land-surface model to include physical consistency with land-surface conditions, land-water contrasts, terrain elevation, and even with 3-d effects with realistic thermal stability, boundary-layer structure, and local circulations. Therefore, the RTMA relies on a background field fully consistent with these 3-d model-based effects by using the previous 1-h forecast from the Rapid Update Cycle (RUC). The RUC, with its detailed hourly assimilation of 3-d atmospheric observations and special emphasis on 3dVAR assimilation of METAR and mesonet data, is appropriate for providing the RTMA background field for a subsequent GSI-2dVAR enhancement (see Pondeca et al. at this same conference).

As part of the hourly post-processing in the NCEP-operational 13-km RUC, a downscaling technique was developed to produce 5-km gridded fields from the full-resolution native (hybrid sigma-isentropic) RUC coordinate data to calculate values consistent with the higher-resolution 5-km RTMA terrain elevation field. This downscaling technique includes both horizontal and vertical components. The vertical component uses near-surface stability from the RUC native data to adjust to the RTMA 5-km terrain. In the horizontal, for example, coastline definition is enhanced as part of this RUC-RTMA downscaling using a 5-km roughness length field used to distinguish land-water boundaries on the 5-km RTMA grid. The fields downscaled to the 5-km grid include 2-m temperature and dewpoint, surface pressure, 10-m wind components, 2-m specific humidity, gust wind speed, cloud base height (ceiling), and visibility. Ceiling and visibility (not yet required for RTMA) are defined with some accuracy due to RUC hourly assimilation of METAR cloud and visibility observations. RTMA downscaling for temperature uses virtual potential temperature, the related prognostic/analysis variable in the RUC model/assimilation systems, an advantage for interpolation in irregular terrain in mixed layer conditions. Different techniques were developed for these different variables, including special approaches for vertical extrapolation vs. interpolation dependent on whether RTMA terrain elevation is higher or lower than RUC terrain. The accuracy of the RTMA fields is dependent on this RUC-RTMA downscaling, and therefore, of considerable interest to NWS RTMA users. These RUC-RTMA downscaling techniques will be described in more detail in this paper.