lmd_Seze1996_abstracts.html
1996 .
(2 publications)W. Yu, M. Doutriaux, G. Sèze, H. Le Treut, and M. Desbois. A methodology study of the validation of clouds in GCMs using ISCCP satellite observations. Climate Dynamics, 12:389-401, May 1996. [ bib | DOI | ADS link ]
The cloudiness fields simulated by a general circulation model and a validation using the International Satellite Cloud Climatology Project (ISCCP) satellite observations are presented. An adapted methodology is developed, in which the issue of the sub-grid scale variability of the cloud fields, and how it may affect the comparison exercise, is considered carefully. In particular different assumptions about the vertical overlap of cloud layers are made, allowing us to reconstruct the cloud distribution inside a model grid column. Carrying out an analysis directly comparable to that of ISCCP then becomes possible. The relevance of this method is demonstrated by its application to the evaluation of the cloud schemes used in Laboratoire de Météoroligie Dynamique (LMD) general circulation model. We compare cloud properties, such as cloud-top height and cloud optical thickness, analysed by ISCCP and simulated by the LMD GCM. The results show that a direct comparison of simulated low cloudiness and that shown from satellites is not possible. They also reveal some model deficiencies concerning the cloud vertical distribution. Some of these features depend little on the cloud overlap assumption and may reveal inadequate parameterisation of the boundary layer mixing or the cloud water precipitation rate. High convective clouds also appear to be too thick.
C. J. Stubenrauch, G. Seze, N. A. Scott, A. Chedin, M. Desbois, and R. S. Kandel. Cloud Field Identification for Earth Radiation Budget Studies. Part II: Cloud Field Classification for the ScaRaB Radiometer. Journal of Applied Meteorology, 35:428-443, March 1996. [ bib | DOI | ADS link ]
Gaining a better understanding of the influence of clouds on the earth's energy budget requires a cloud classification that takes into account cloud height, thickness, and cloud cover. The radiometer ScaRaB (scanner for radiation balance), which was launched in January 1994, has two narrowband channels (0.5 0.7 and 10.5 12.5 m) in addition to the two broadband channels (0.2 4 and 0.2 50 m) necessary for earth radiation budget (ERB) measurements in order to improve cloud detection. Most automatic cloud classifications were developed with measurements of very good spatial resolution (200 m to 5 km). Earth radiation budget experiments (ERBE), on the hand, work at a spatial resolution of about 50 km (at nadir), and therefore a cloud field classification adapted to this scale must be investigated. For this study, ScaRaB measurements are simulated by collocated Advanced Very High Resolution Radiometer (AVHRR) ERBE data. The best-suited variables for a global cloud classification are chosen using as a reference cloud types determined by an operationally working threshold algorithm applied to AVHRR measurements at a reduced spatial resolution of 4 km over the North Atlantic. Cloud field types are then classified by an algorithm based on the dynamic clustering method. More recently, the authors have carried out a global cloud field identification using cloud parameters extracted by the 3I (improved initialization inversion) algorithm, from High-Resolution Infrared Sounder (HIRS)-Microwave Sounding Unit (MSU) data. This enables the authors first to determine mean values of the variables best suited for cloud field classification and then to use a maximum-likelihood method for the classification. The authors find that a classification of cloud fields is still possible at a spatial resolution of ERB measurements. Roughly, one can distinguish three cloud heights and two effective cloud amounts (combination of cloud emissivity and cloud cover). However, only by combining flux measurements (ERBE) with cloud field classifications from sounding instruments (HIRS/MSU) can differences in radiative behavior of specific cloud fields be evaluated accurately.