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[1] Mineral dust particles loaded into the atmosphere from the Sahara desert represent one major factor affecting the Earth’s radiative budget. Regular model-based forecasts of 3-D dust fields can be used in order to determine the dust radiative effect in climate models, in spite of the large gaps in observations of dust vertical profiles. In this study, dust forecasts by the Tel Aviv University ( TAU) dust prediction system were compared to lidar observations to better evaluate the model’s capabilities. The TAU dust model was initially developed at the University of Athens and later modified at Tel Aviv University. Dust forecasts are initialized with the aid of the Total Ozone Mapping Spectrometer aerosol index ( TOMS AI) measurements. The lidar soundings employed were collected at the outskirts of Rome, Italy (41.84 degrees N, 12.64 degrees E) during the high - dust activity season from March to June of the years 2001, 2002, and 2003. The lidar vertical profiles collected in the presence of dust were used for obtaining statistically significant reference parameters of dust layers over Rome and for model versus lidar comparison. The Barnaba and Gobbi ( 2001) approach was used in the current study to derive height-resolved dust volumes from lidar measurements of backscatter. Close inspection of the juxtaposed vertical profiles, obtained from lidar and model data near Rome, indicates that the majority (67%) of the cases under investigation can be classified as good or acceptable forecasts of the dust vertical distribution. A more quantitative comparison shows that the model predictions are mainly accurate in the middle part of dust layers. This is supported by high correlation (0.85) between lidar and model data for forecast dust volumes greater than the threshold of 1 x 10(-12) cm(3)/cm(3). In general, however, the model tends to underestimate the lidar-derived dust volume profiles. The effect of clouds in the TOMS detection of AI is supposed to be the main factor responsible for this effect. Moreover, some model assumptions on dust sources and particle size and the accuracy of model-simulated meteorological parameters are also likely to affect the dust forecast quality.