ABSTRACT. In this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snowcover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai-Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

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Due to the rapid economic development in China, the conflict between the increasing traditional energy consumption and the severe environmental threats is more and more serious. To ease the situation, greater use of wind energy in China could be the solution for energy conservation and sustainable environment in the long run.

Abstract-A modified differential evolution algorithm (MDE) for pattern synthesis of antenna arrays is proposed in this paper. By employing the novel strategies of best of random mutation and randomized local search, the convergence of standard differential evolution algorithm (SDE) is significantly accelerated. Five standard benchmark functions are optimized to testify the proposed algorithm by comparison with several other optimization algorithms. The numerical results verify the superior performance of the proposed MDE. Furthermore, the MDE is applied to two pattern synthesis examples, including a linear array and a cylindrical conformal array. Experiment results demonstrate that the proposed MDE has better performance than the other optimization methods in both of these two examples, which indicate the proposed algorithm is a competitive optimization algorithm in pattern synthesis.