Evaluation of a Numerical Weather Forecast Model Using Boundary Layer Cloud-Top Temperature Retrieved from AVHRR

Abstract

This study concerns the evaluation of the boundary layer (BL) subgrid parameterization of a numerical weather forecast model on the synoptic scale. The method presented aims at separating the two possible origins of model defficiencies in representing a cloud-topped boundary layer: (i) large-scale data assimilation issues, and (ii) BL parameterization. The method combines two sources of data: model analyzed fields from the SEMAPHORE field experiment, and the Advanced Very High Resolution Radiometer (AVHRR) dataset from the NOAA-11 and -12 Satellite. The application focuses on an anticyclonic period during field experiment (10–17 November 1993), for which a special version of ARPEGE—The Météo-France Numerical Weather Prediction model—is used to analyze the data from the field experiment. In the proposed method, the boundary layer is globally characterized by its height which is converted to an inversion layer temperature in the model. Low-level cloud-top temperatures of optically thick clouds are inferred from the satellite radiometers. The model and satellite-retrieved temperature fields are two independent fields that are used together to select regions for which spatial variations of boundary layer cloud-top temperature are approximately in phase. These regions are assumed to have correctly assimilated the large-scale data fields. It is found that they cover a significant part of the analyzed synoptic situations. In the selected regions, the two temperature fields can be examined to evaluate BL schemes and obtain insight on the physical processes responsible for cloudiness.