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We describe and evaluate the NMMB/BSC-Dust, a new dust aerosol cycle model embedded online within the NCEP Non-hydrostatic Multiscale Model (NMMB). NMMB is a further evolution of the operational Nonhydrostatic Mesoscale Model (WRF-NMM), which together with other upgrades has been extended from meso to global scales. Its unified non-hydrostatic dynamical core is prepared for regional and global simulation domains. The new NMMB/BSC-Dust is intended to provide short to medium-range weather and dust forecasts from regional to global scales and represents a first step towards the development of a unified chemical-weather model. This paper describes the parameterizations used in the model to simulate the dust cycle including sources, transport, deposition and interaction with radiation. We evaluate monthly and annual means of the global configuration of the model against the AEROCOM dust benchmark dataset for year 2000 including surface concentration, deposition and aerosol optical depth (AOD), and we evaluate the daily AOD variability in a regional domain at high resolution covering Northern Africa, Middle East and Europe against AERONET AOD for year 2006. The NMMB/BSC-Dust provides a good description of the horizontal distribution and temporal variability of the dust. Daily AOD correlations at the regional scale are around 0.6-0.7 on average without dust data assimilation. At the global scale the model lies within the top range of AEROCOM dust models in terms of performance statistics for surface concentration, deposition and AOD. This paper discusses the current strengths and limitations of the modeling system and points towards future improvements. [ABSTRACT FROM AUTHOR]

Aerosol levels at Mediterranean Basin are significantly affected by desert dust that is eroded in North Africa and is transported northwards. This study aims to assess the performance of the Dust REgional Atmospheric Model (BSC-DREAM8b) in the prediction of dust outbreaks near the surface in Eastern Mediterranean. For this purpose, model PM10 predictions covering a 7-year period and PM10 observations at five surface monitoring sites in Greece are used. A quantitative criterion is set to select the significant dust outbreaks defined as those when the predicted PM10 surface concentration exceeds 12 μg/m3. The analysis reveals that significant dust transport is usually observed for 1-3 consecutive days. Dust outbreak seasons are spring and summer, while some events are also forecasted in autumn. The seasonal variability of dust transport events is different at Finokalia, where the majority of events are observed in spring and winter. Dust contributes by 19-25% to the near surface observed PM10 levels, which can be increased to more than 50 μg/m3 during dust outbreaks, inducing violations of the air quality standards. Dust regional modeling can be regarded as a useful tool for air quality managers when assessing compliance with air quality limit values. [ABSTRACT FROM AUTHOR]

Abstract: Currently three air quality modelling systems operate routinely with high-resolution over mainland Portugal for forecasting purposes, namely MM5-CHIMERE, MM5-EURAD, and CALIOPE. They each operate daily using different horizontal resolutions (10 km × 10 km, 5 km × 5 km, and 4 km × 4 km, respectively), specific physical and chemical parameterizations, and their own emission pre-processors (with a common EMEP emission database source but different spatial disaggregation methodologies). The operational BSC-DREAM8b model is coupled offline within the aforementioned air quality systems to provide the Saharan dust contribution to particulate matter. Bias-correction studies have demonstrated the benefit of using past observational data to reduce systematic model forecast errors. The present contribution aims to evaluate the application of two bias-correction techniques, the multiplicative ratio and the Kalman filter, in order to improve air quality forecasts for Portugal. Both techniques are applied to the three modelling systems over the full year of 2010. Raw and unbiased model results for the main atmospheric pollutants (O3, NO2, SO2, PM10, and PM2.5) are analysed and compared with data from 18 monitoring stations distributed within inland Portugal on an hourly basis. Statistical analysis shows that both bias-correction techniques improve the raw forecast skills (for all the modelling systems and pollutants). In the case of O3 max-8 h, correlation coefficients improve by 19–45%, from 0.56–0.81 (raw models) to 0.78–0.86 (corrected models). PM2.5 also presents significant improvements, for example correlation coefficients increase by more than 50% (with both techniques), reaching values between 0.50 and 0.64. The corrected primary pollutants NO2 and SO2 demonstrate significant relative improvements compared to O3, mostly because the original modelling system skills are lower for those species. Although the applied techniques have different mathematical formulations and complexity levels, there are comparable answers for all of the forecasting systems. Analysis performed over specific situations such as air quality episodes and cases of unvalidated or missing data reveals different behaviours of the bias-correction techniques under study. The results confirm the advantage of the application of bias-correction techniques for air quality forecasts. Both techniques can be applied routinely in operational forecast systems and they will be useful to provide accurate alerts about exceedances to the population. [Copyright &y& Elsevier]