MULTISCALE 4-DIMENSIONAL DATA ASSIMILATION

Four-dimensional data assimilation (FDDA) schemes capable of effective ly analyzing asynoptic, near-continuous data streams are especially im portant on the mesobeta scale for both model initialization and dynami c analysis. A multiscale nudging approach that utilizes grid nesting i s investigated for the generation of complete, dynamically consistent datasets for the mesobeta scale. These datasets are suitable for input into air quality models, but can also be used for other diagnostic pu rposes including model initialization. A multiscale nudging strategy i s used here to simulate the wind flow for two cases over the Colorado Plateau and Grand Canyon region during the winter of 1990 when a speci al mesobeta-scale observing system was deployed in the region to study the canyon's visibility impairment problem. The special data included Doppler sodars, profilers, rawinsondes, and surface stations. Combina tions of these data and conventional mesoalpha-scale data were assimil ated into a nested version of the Pennsylvania State University-Nation al Center for Atmospheric Research Mesoscale Model to investigate the importance of scale interaction and scale separation during FDDA. Meso alpha-scale forcing was shown to be important for accurate simulation of the mesobeta-scale flow over the 48-h period of the simulations. Di rect assimilation of mesoalpha-scale analyses on a finescale grid was shown to be potentially harmful to the simulation of mesobeta-scale fe atures. Nudging to mesoalpha-scale analyses on the coarse grid enabled nudging to mesobeta-scale observations on the inner fine grid to be m ore effective. This grid-nesting, multiscale FDDA strategy produced th e most accurate simulation of the low-level wind fields. It is demonst rated that when designing an FDDA strategy, scale interactions of diff erent flow regimes cannot be ignored, particularly for simulation peri ods of several days on the mesobeta scale.