Abstract
This paper presents results from a series of observing system simulation experiments (OSSEs), designed to test a dynamic initialization procedure for continuous assimilation of observations from a hypothetical network of profilers. A 40-km adiabatic mesoscale model was used to generate a set of simulated observations from a network of 77 wind profilers over the continental United States with a station separation of 360 km. These observations were then assimilated into an 80-km model during a 12-h preforecast integration through a Newtonian nudging technique.
We found that dynamic initialization by nudging can successfully assimilate the time-continuous wind profiler observations into the model. The profiler data assimilation is effective in recovering mesoscale circulations which are not properly resolved by the observing network (due to inadequate horizontal resolution), while at the same time controlling the error growth for large-scale circulations. The impact is particularly significant in the divergence field, which is crucial for an accurate precipitation forecast. The improved initial state leads to further improvement in the subsequent forecast, demonstrating the value of the time-continuous profiler observations on short-range numerical weather prediction.
Assimilation of the wind field is found to be considerably more effective than assimilation of the temperature field. Specifically, wind assimilation leads to improvement in both the temperature and the wind fields, while temperature assimilation produces little improvement in the wind field. The best results are obtained when both temperature and wind fields are assimilated.
The proposed demonstration network of 31 profiles is likely to have a positive impact on short-range numerical weather prediction, though mainly confined over the localized region covered by the profiler network. Further expansion of the profiler network to increase its spatial resolution and its areal coverage is needed if improved prediction is expected over a larger area.
