This presented study applies, for the first time, near synchronized in-situ, space-borne (A-Train) and ground-based observations to evaluate the boundary layer clouds (BLCs) over Tasmania and the adjacent SO simulated by the limited-area Numerical Weather Prediction (NWP) version of the Australian Community Climate and Earth System Simulator (ACCESS). This NWP forecast system is largely based on the UK Met Office Unified Model/Variational Assimilation (UM/VAR) system. In this study, two cases associated with postfrontal conditions and the leading side of high pressure ridge are investigated.
Results of the simulations suggest that the operational ACCESS NWP demonstrates a considerable level of skill in forecasting the macrophysical properties of the BLCs in the post-frontal and high pressure environments over the SO, generally consistent with the in-situ and remote sensing observations. However, some notable challenges remain. The fractional cloud cover of the widespread BLCs is under-predicted. There is a positive (negative) bias in the forecast cloud-base and cloud-top heights (cloud-top temperature). The forecast capping inversion is too high and strong compared to the measurements. The secondary inversion constantly present in the observation is not predicted. And the frequently observed large values of liquid water content are not well reproduced.
Sensitivity experimentations of testing the newly developed parameterisations are undertaken. Simulations with the shear-dominated planetary boundary layer (PBL) scheme (Lock et al. 2000) and the autoconversion microphysics scheme (Franklin 2008) show notable improvements in the forecast fractional cloud cover, cloud structure, and the distribution of liquid water content (better average and maximum values), although the lack of enough cloud liquid water is still a discernible shortcoming in the model.
The implication of this study to better understanding the nature of the SO clouds and the regional shortwave radiation bias in the climate model simulations will be discussed. Possible reasons that likely have contributed to the model deficiencies will be presented.