In spring 2006, an initial version of the 5-km downscaling of RUC data for the CONUS Real-Time Mesoscale Analysis (RTMA) was implemented at the National Centers for Environmental Prediction. Since that time, the RUC downscaling techniques within the RUC post-processing have been refined several times, based on daily reviews by forecasters within the National Weather Service (NWS). Upgrades have been made to the RUC-RTMA downscaling as recently as in April 2007, and here we summarize its current status. 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 operational 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/landsurface model to ensure some measure of physical consistency with land-surface conditions, land-water contrasts, terrain elevation, and even includes 3-d effects with realistic thermal stability, boundary-layer structure, and local circulations. Therefore, the RTMA relies on a background field fully consistent Figure 2. Flowchart for RTMA processing. 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 3-d variational assimilation of METAR and mesonet data, is appropriate for providing the RTMA background field for a subsequent GSI-2dVAR enhancement (see Pondeca et al. 2007 at this same conference). As part of the hourly postprocessing in the NCEPoperational 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 (e.g., Fig. 1). The RUC-RTMA 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 with variabledependent treatment for vertical extrapolation vs. interpolation. In the horizontal, for example, coastline definition is enhanced as part of this RUC-RTMA downscaling using a 5-km land/water mask to sharpen land-water boundaries on the 5-km RTMA grid. The fields downscaled to the 5-km grid include • 2-m temperature • 2-m dewpoint • surface pressure • 10-m wind components • 2-m specific humidity • gust wind speed • cloud base height (ceiling) • 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 (?v), the related prognostic/analysis variable in the RUC model/assimilation systems. This is advantageous 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.
This publication was presented at the following: