A rapid urbanization process is undergoing in China, exerting significant impacts on local weather and climate. However, rare study pays attention to the underdeveloped regions. An underdeveloped city of Nanning, in southwestern China, is experiencing considerable urbanization. The present urban area of Nanning is 2.8 times larger than that in 1980. Using the Weather Research and Forecasting model coupled with an urban canopy model, two experiments with different urban land use are designed to investigate impacts of Nanning urbanization on a rainstorm occurred in July 2009. Results show simulated precipitation can be significantly improved by the experiment with urbanization effects included. Urbanization leads to a warmer and drier surface, which is favored for the formation of near-surface low pressure and wind convergence over the city. But the warm-dry air near urban surface cannot lead to deep convective activity, resulting in urban-enhanced precipitation is dominated by the large-scale precipitation. Such enhanced large-scale precipitation has a positive contribution to atmospheric latent heat release, inducing a heating source in middle troposphere over the urban area. Then the cyclonic circulation around Nanning is intensified according to thermal adaption theory. The enhanced cyclonic circulation is beneficial for stronger moisture convergence and warm-moist southwesterly flow transports into Guangxi from tropical oceans. Vertical motion is amplified by the coastal orographic uplift effect, and wind convergence over the Nanning downstream is also enhanced. Therefore, the convective precipitation dominantly enhances over the coastal and city's downstream regions, which is different from the urban-increased large-scale precipitation in the Nanning City area.

Random rotation is one of the common perturbation approaches for privacy preserving data classification, in which the data matrix is multiplied by a random rotation matrix before publishing in order to preserve data privacy. One distinct advantage of this approach is that it can maintain the geometric properties of the data matrix, so several categories of classifiers that are based on the geometric properties of the data can achieve similar accuracy on the transformed data as that on the original data. In this paper, we generalize this idea to the situation where the data matrix is assumed to be vertically partitioned into several sub-matrices and held by different owners. Each data holder can choose a rotation matrix randomly and independently to perturb their individual data. Then they all send the transformed data to a third party, who collects all of them and forms a whole data set for data mining or other analysis purposes. We show that under such a scheme the geometric properties of the data set is also preserved and thus it can maintain the accuracy of many classifiers and clustering techniques applied on the transformed data as on the original data. This method enables us to develop efficient centralized data mining algorithms instead of distributed algorithms to preserve privacy. Experiments on real data sets show that such generalization is effective for vertically partitioned data sets.

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Spectral clustering is one of the most popular modern clustering techniques that often outperforms other clustering techniques. When data owned by different parties are used for analysis, the cooperating parties may need to perform spectral clustering jointly, even if the parties may not be willing to disclose their private data to each other. In this paper we develop privacy preserving spectral clustering protocols over vertically partitioned data sets. Such protocols allow various parties to analyze their data jointly while protecting their privacy.

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