Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale Data

A four-dimensional data assimilation (FDDA) scheme based on Newtonian relaxation or “nudging” is tested using standard rawinsonde data in the Penn State/NCAR limited-area mesoscale model. It is imperative that we better understand these FDDA-generated datasets, which are widely used for model initialization and diagnostic analysis. The main hypothesis to be tested is that use of coarse-resolution rawinsonde observations throughout a model integration, rather than at only the initial time, can limit large-scale model error growth (amplitude and phase errors) while the model generates realistic mesoscale structures not resolved by the data. The main objective of this study is to determine what assimilation strategies and what meteorological fields (mass, wind or both) have the greatest positive impact via FDDA on the numerical simulators for two midlatitude, real-data cases using the full-physics version of a limited-area model. Seven experiments are performed for each case: one control experiment (no nudging), five experiments which nudge the model solution to analyses of observations, and a seventh experiment in which the actual rawinsonde observations are assimilated directly into the model. Subjective and statistical evaluation of the results include verification of the primitive variable fields, plus a detailed precipitation verification which is especially valuable since rainfall is the result of many complex physical processes and is usually characterized by small-scale variability, which makes it much more difficult to simulate accurately than the other variables. The results show that the assimilation of both wind and thermal data throughout the model atmosphere had a consistently positive impact on the synoptic-scale and mesoscale mass and wind fields for both cases and for the precipitation simulations in the case dominated by large-scale forcing. However, in the other case for which small-scale convection was the dominant precipitation mechanism, the FDDA system using only rawinsonde data showed only a minor improvement in the rainfall. This may be attributed to 1) the fact that time scales of small convective systems am less than 12 h, the temporal resolution of the data used for FDDA, and 2) assimilation of 12-hourly temperature data near the surface may adversely affect the model's diurnal cycle and low-level stability, which are very important for convection. Other results show that nudging vorticity or the rawinsonde-based mixing ratio analyses tended to seriously degrade the precipitation simulators for both cases and should be avoided. The transfer of information on the mesoscale from the wind (mass) fields to the mass (wind) fields was found to be significant: for shallow forcing (small equivalent depth), the winds were shown to adjust to the mass fields, while for large-scale forcing through the depth of the troposphere (large equivalent depth), wind data were generally more effective than mass data. The most accurate mass and wind fields in both cases, however, were produced by assimilating both wind and temperature information. Nudging the model' wind and temperature fields directly to the rawinsonde observations generally produced results comparable to nudging to the gridded analyses of these data.