The Use of Surface Observations in Four-Dimensional Data Assimilation Using a Mesoscale Model

Abstract

A system for the frequent intermittent assimilation of surface observations into a mesoscale model is described. The assimilation begins by transforming the surface observations to model coordinates. Next, the lowest-level model fields of potential temperature, relative humidity, u and v component winds, and surface pressure are updated by an objective analysis using the successive correction approach. The deviations of the analysis from the first guess at the lowest model layer are then used to adjust the other model layers within the planetary boundary layer. The PBL adjustment is carried out by using the model's values of eddy diffusivity, which are nudged to reflect the updated conditions, to determine the influence of the lowest-layer deviations on the other model layers. Results from a case study indicate that the frequent intermittent assimilation of surface data can provide superior mososcale analyses and forecasts compared to assimilation of synoptic data only. The inclusion of the PBL adjustment procedure is an important part of generating the better forecasts. Extrapolation of the results here suggests that two-dimensional data can be successfully assimilated into a model provided there is a mechanism to smoothly blend the data into the third dimension.