Model predicted low-level cloud parameters: Part II: Comparison with satellite remote sensing observations during the BALTEX Bridge Campaigns

Abstract

A pressing task in numerical weather prediction and climate modelling is the evaluation of modelled cloud fields. Recent progress in spatial and temporal resolution of satellite remote sensing increases the potential of such evaluation efforts. This paper presents new methodologies to compare satellite remote sensing observations of clouds and output of atmospheric models and demonstrates their usefulness for evaluation. The comparison is carried out for two MODerate resolution Imaging Spectrometer (MODIS) scenes from the BALTEX Bridge Campaigns. Both scenes are characterised by low-level clouds with a substantial amount of liquid water. Cloud cover and cloud optical thickness of five different models, LM, Méso-NH, MM5 (non-hydrostatic models), RACMO2, and RCA (regional climate models) as well as corresponding retrievals from high resolution remote sensing observations of MODIS onboard the Terra satellite form the basis of a statistical analysis to compare the data sets. With the newly introduced patchiness parameters it is possible to separate differences between the two scenes on the one hand and between the models and the satellite on the other hand. We further introduce a new approach to spatially aggregate cloud optical thickness. Generally the models overestimate cloud optical thickness which can in part be ascribed to the lack of subgrid-scale variability. However, LM underestimates the frequency of occurrence of cloud optical thickness at values around 25. Furthermore, we compare the standard operational output of the non-hydrostatic models to simulations of the same models including parameterised shallow convection. However, clear improvements in the representation of low-level clouds are not found for these models. A change of the coefficients for autoconversion in RCA shows that LWP and precipitation strongly depend on this parameter. Refined vertical resolution, implemented in RACMO2, leads to a better agreement between model and satellite but still leaves room for further improvements. In general, this study reveals deficiencies of the models in representing low-level clouds, in particular for a stratiform cloud.