Multiscale Four-Dimensional Data Assimilation

Abstract

Four-dimensional data assimilation (FDDA) schemes capable of effectively analyzing asynoptic, near-continuous data streams art especially important on the mesobeta scale for both model initialization and dynamic analysis. A multiscale nudging approach that utilizes grid nesting is 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 purposes including model initialization. A multiscale nudging strategy is 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 special 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. Combinations of these data and conventional mesoalpha-scale data were assimilated into a nested version of the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model to investigate the importance of wale interaction and scale separation during FDDA. Mesoalpha-scale forcing was shown to be important for accurate simulation of the mesobeta-scale flow over the 48-h period of the simulators. Direct assimilation of mesoalpha-scale analyses on a finescale grid was shown to be potentially harmful to the simulation of mesobeta-scale features. Nudging to mesoalpha-scale analyses on the coarse grid enabled nudging to mesobeta-scale observations on the inner fine grid to be more effective. This grid-nesting multiscale FDDA strategy produced the most accurate simulation of the low-level wind fields. It is demonstrated that when designing an FDDA strategy, scale interactions of different flow regimes cannot be ignored, particularly for simulation periods of several days on the mesobeta scale.