Meteorological model errors caused by imperfect parameterizations generally cannot be overcome simply by optimizing initial and boundary conditions. However, advanced data assimilation methods are capable of extracting significant information about parameterization behavior from the observations, and thus can be used to estimate model parameters while they adjust the model state. Such parameters should be identifiable, meaning that they must have a detectible impact on observable aspects of the model behavior, their individual impacts should be a monotonic function of the parameter values, and the various impacts should be clearly distinguishable from each other. A sensitivity analysis is conducted for the parameters within the Asymmetrical Convective Model, version 2 (ACM2) planetary boundary layer (PBL) scheme in the Weather Research and Forecasting model in order to determine the parameters most suited for estimation. A total of 10 candidate parameters are selected from what is, in general, an infinite number of parameters, most being implicit or hidden. Multiple sets of model simulations are performed to test the sensitivity of the simulations to these 10 particular ACM2 parameters within their plausible physical bounds. The most identifiable parameters are found to govern the vertical profile of local mixing within the unstable PBL, the minimum allowable diffusivity, the definition of the height of the unstable PBL, and the Richardson number criterion used to determine the onset of turbulent mixing in stable stratification. Differences in observability imply that the specific choice of parameters to be estimated should depend upon the characteristics of the observations being assimilated.
All Science Journal Classification (ASJC) codes
- Atmospheric Science