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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°N, 12.64°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 Â 10 À12 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.

[1] In this study, forecast errors in dust vertical distributions were analyzed. This was carried out by using quantitative comparisons between dust vertical profiles retrieved from lidar measurements over Rome, Italy, performed from 2001 to 2003, and those predicted by models. Three models were used: the four-particle-size Dust Regional Atmospheric Model (DREAM), the older one-particle-size version of the SKIRON model from the University of Athens (UOA), and the pre-2006 one-particle-size Tel Aviv University (TAU) model. SKIRON and DREAM are initialized on a daily basis using the dust concentration from the previous forecast cycle, while the TAU model initialization is based on the Total Ozone Mapping Spectrometer aerosol index (TOMS AI). The quantitative comparison shows that (1) the use of four-particle-size bins in the dust modeling instead of only one-particle-size bins improves dust forecasts; (2) cloud presence could contribute to noticeable dust forecast errors in SKIRON and DREAM; and (3) as far as the TAU model is concerned, its forecast errors were mainly caused by technical problems with TOMS measurements from the Earth Probe satellite. As a result, dust forecast errors in the TAU model could be significant even under cloudless conditions. The DREAM versus lidar quantitative comparisons at different altitudes show that the model predictions are more accurate in the middle part of dust layers than in the top and bottom parts of dust layers.

In the absence of convection permitting numerical weather prediction (NWP) ensembles, the most recent deterministic NWP precipitation forecast is usually addressed. However, the exact intensity, location and timing of a deterministic precipitation forecast is not always reliable because of the chaotic nature and complexity of precipitation formation mechanisms. This study examines a way to optimize the use of precipitation forecasts for deterministic NWP models. More specifically, it suggests using a spatially smoothed time‐lagged ensemble (TLE) to obtain more reliable precipitation forecasts. A global NWP model (integrated forecast system—IFS) and a regional convection permitting NWP model (COSMO) over the Eastern Mediterranean during the period 2016–2018 were used for the analysis. First, the paper defines light, light–moderate and moderate intensities for 6 hr accumulated precipitation (6hAP) and investigates the corresponding definitions for 1 hr accumulated precipitation (1hAP). Next, fractional skill score (FSS) are used to estimate the optimal spatial smoothing scale of a deterministic precipitation forecast for the three intensity categories for the 6hAP and 1hAP. The FSS is also used to compare COSMO and IFS deterministic precipitation forecasts, and to analyse the skill degradation with the forecast range. It is quantitatively shown that the useful scale of precipitation forecasts is smaller for larger accumulation time intervals. Finally, precipitation forecasts for TLE are formed from successive smoothed deterministic forecasts and compared with the most recent deterministic forecast. It is found that, on average, TLEs have better skills for both 6hAP and 1hAP. The reason for this improvement in skill is illustrated using a case study as an example.

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