A MULTISCALE 4-DIMENSIONAL DATA ASSIMILATION SYSTEM APPLIED IN THE SAN-JOAQUIN VALLEY DURING SARMAP .1. MODELING DESIGN AND BASIC PERFORMANCE-CHARACTERISTICS

This paper presents results of numerical simulations made with a high- resolution multiscale four-dimensional data assimilation system applie d over California during two episodes associated with high ozone conce ntrations in the San Joaquin Valley. The model used here is the nonhyd rostatic Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The focus of the paper is the objecti ve validation of the regional (mesoalpha scale) meteorological results . The multiscale data assimilation approach produces highly reliable s imulations of the wind, temperature, mixed-layer depth, and moisture, each of which is vital to air quality modeling and a host of other mes oscale applications. The significance of this research is threefold. F irst, it is the first evaluation of this multiscale assimilation syste m in strongly heated summertime conditions and with comparatively fine grid resolution (4-km inner mesh). Second, the assimilation system ha s been extended so that temperature soundings can be used to effective ly reduce model errors for the simulated mixed-layer depth (which is c rucial for correctly simulating boundary layer mixing and air chemistr y processes). Third, by withholding half of the special data for use i n model verification, it is shown that assimilation of observations at the mesoscale is, indeed, effective. Numerical errors are reduced ove r the intervening regions between the sites where data are assimilated . By establishing interobservation accuracy, we demonstrate that the d ata-assimilating model produces spatially consistent solutions without serious distortion of the active dynamical processes. In other words, the model and the observations are each able to contribute to the fin al numerical solution in a way that reduces error growth and does not disrupt the intervariable consistency among the primitive variable fie lds.