IMPACT OF VERTICAL RESOLUTION, MODEL TOP AND DATA ASSIMILATION ON WEATHER FORECASTING-A CASE STUDY

Abstract

The impacts of stratospheric initial conditions and vertical resolution on the stratosphere by raising the model top, refining the vertical resolution, and the assimilation of operationally available observations, including conventional and satellite observations, on continental U. S. winter short-range weather forecasting, were investigated in this study. The initial and predicted wind and temperature profiles were analyzed against conventional observations. Generally, the initial wind and temperature bias profiles were better adjusted when a higher model top and refined vertical resolution were used. Negative impacts were also observed in both the initial wind and temperature profiles, over the lower troposphere. Different from the results by only raising the model top, the assimilation of operationally available observations led to significant improvements in both the troposphere and stratosphere initial conditions when a higher top was used. Predictions made with the adjusted stratospheric initial conditions and refined vertical resolutions showed generally better forecasting skill. The major improvements caused by raising the model top with refined vertical resolution, as well as those caused by data assimilation, were in both cases located in the tropopause and lower stratosphere. Negative impacts were also observed in the predicted near surface wind and lower-tropospheric temperature. These negative impacts were related to the uncertainties caused by more stratospheric information, as well as to some physical processes. A case study shows that when we raise the model top, put more vertical layers in stratosphere and apply data assimilation, the precipitation scores can be slightly improved. However, more analysis is needed due to uncertainties brought by data assimilation.