Evaluation of Planetary Boundary Layer Scheme Sensitivities for the Purpose of Parameter Estimation

Abstract

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.

Coupling of Important Physical Processes in the Planetary Boundary Layer between Meteorological and Chemistry Models for Regional to Continental Scale Air Quality Forecasting: An Overview

“…The key to model performance in capturing the characteristics of turbulent mixing within the PBL is often attributed to systematic efforts in parameter tuning and optimization of the model or sub-models used. This study quoted a rather thorough parameterization optimization study by Nielsen-Gammon et al [58] performed for the local convective mixing parameterization scheme within ACM2 [62]. It illustrates the uncertainties involved and the well-invested optimization study utilizing variational methods, which can significantly improve model performance.…”

“…Among these efforts, Nielsen-Gammon et al [58] performed a rather vigorous parameter optimization study of the new local convection scheme of ACM2 [62]. Equation 1 states the PBL scaling form of K z , the vertical eddy diffusivity for heat, within the layer.…”

“…The optimal value of p was found to be 2, dictating a parabolic characteristic of K z over height. The smaller the value of p, the stronger is the turbulent mixing; and vice versa [58]. Deep soil moisture and temperature govern the slowly varying evaporative component of the soil surface.…”

Evaluation of ABL parametrization schemes in the COSMO, a regional non-hydrostatic atmospheric model over an inhomogeneous environment

“…The basic role of an ABL parametrization scheme is to provide the coupling with the surface through the determination of turbulent fluxes and their vertical diffusion, to give realistic forecasts of near surface parameters such as temperature, specific humidity and wind and to provide input to the cloud and convection schemes (Pielke 2002;Teixeira et al 2003). With the development of highresolution atmospheric models used for the NWP, requirements for accurate ABL parametrization schemes have dramatically increased, making them a key element in the weather and climate predictions, as well as in several other environmental applications, such as pollutant dispersion and biometeorology (Yamartino and Wiegand 1986;Nielsen-Gammon et al 2010;Hu et al 2010). Despite a considerable progress in the prediction of synoptic weather conditions through atmospheric models, the errors and uncertainties associated with the ABL parametrization schemes in the NWP model simulations remain one of the primary sources of inaccuracies, especially for the tropics where small-scale cumulus convection dominate (Basu et al 1998(Basu et al , 1999Lee and Hong 2005;Pleim 2007a, b;Shah et al 2010).…”

Linear and non-linear response to parameter variations in a mesoscale model

“…Nielsen-Gammon et al (2010) refers to a parameter that results in a reasonably strong and unique relationship between parameters and state variables as distinguishable, and we adopt the same terminology.…”