Action at a Distance: Snowfall in the Sierra Nevada provides a large fraction of the water that California receives as precipitation. Knowing what factors influence the amount of snow that falls is thus critical for projecting how water availability may change in the future. Aerosols have an important effect on cloud processes and precipitation. Creamean et al. (p. 1572 , published online 28 February) found that dust and biological aerosols originating from as far away as the Sahara facilitate ice nuclei formation and ice-induced precipitation in the Sierra Nevada and show how dust and biological articles from places as distant as Africa and Asia can influence precipitation over the western United States.

Estimating the aerosol contribution to the global or regional radiative forcing can take advantage of the relationship between the spectral aerosol optical properties and the size and chemical composition of aerosol. Long term global optical measurements from observational networks or satellites can be used in such studies. Using in-situ chemical mixing state measurements can help us to constrain the limitations of such estimates. In this study, the Absorption Ångström Exponent (AAE) and the Scattering Ångström Exponent (SAE) derived from 10 operational AERONET sites in California are combined for deducing chemical speciation based on wavelength dependence of the optical properties. In addition, in-situ optical properties and single particle chemical composition measured during three aircraft field campaigns in California between 2010 and 2011 are combined in order to validate the methodology used for the estimates of aerosol chemistry using spectral optical properties. Results from this study indicate a dominance of mixed types in the classification leading to an underestimation of the primary sources, however secondary sources are better classified. The distinction between carbonaceous aerosols from fossil fuel and biomass burning origins is not clear, since their optical properties are similar. On the other hand, knowledge of the aerosol sources in California from chemical studies help to identify other misclassification such as the dust contribution.

Biomass burning (BB) is a significant source of atmospheric particles in many parts of the world. Whereas many studies have demonstrated the importance of BB emissions in central and northern Europe, especially in rural areas, its impact in urban air quality of southern European countries has been sparsely investigated. In this study, highly time resolved multi-wavelength absorption coefficients together with levoglucosan (BB tracer) mass concentrations were combined to apportion carbonaceous aerosol sources. The Aethalometer model takes advantage of the different spectral behavior of BB and fossil fuel (FF) combustion aerosols. The model was found to be more sensitive to the assumed value of the aerosol Ångström exponent (AAE) for FF (AAEff) than to the AAE for BB (AAEbb). As result of various sensitivity tests the model was optimized with AAEff=1.1 and AAEbb=2. The Aethalometer model and levoglucosan tracer estimates were in good agreement. The Aethalometer model was further applied to data from three sites in Granada urban area to evaluate the spatial variation of CMff and CMbb (carbonaceous matter from FF or BB origin, respectively) concentrations within the city. The results showed that CMbb was lower in the city centre while it has an unexpected profound impact on the CM levels measured in the suburbs (about 40%). Analysis of BB tracers with respect to wind speed suggested that BB was dominated by sources outside the city, to the west in a rural area. Distinguishing whether it corresponds to agricultural waste burning or with biomass burning for domestic heating was not possible. This study also shows that although traffic restrictions measures contribute to reduce carbonaceous concentrations, the extent of the reduction is very local. Other sources such as BB, which can contribute to CM as much as traffic emissions, should be targeted to reduce air pollution.

Based on a CCD camera, we have developed an in-house sky imager system for the purpose of cloud cover estimation and characterization. The system captures a multispectral image every $5\min$, and the analysis is done with a method based on an optimized neural network classification procedure and a genetic algorithm. The method discriminates between clear sky and two cloud classes: opaque and thin clouds. It also divides the image into sectors and finds the percentage of clouds in those different regions. We have validated the classification algorithm on two levels: image level, using the cloud observations included in the METAR register performed at the closest meteorological station, and pixel level, determining whether the final classification is correct.

Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.