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Abstract. Incorporating observations of shallow soil moisture content into land models is an important step in assimilating satellite observations of soil moisture content. In this study, several modifications of an ensemble Kalman filter (EnKF) are proposed for improving this assimilation. It was found that a forecast error inflation-based approach improves the soil moisture content in shallow layers, but it can increase the analysis error in deep layers. To mitigate the problem in deep layers while maintaining the improvement in shallow layers, a vertical localization-based approach was introduced in this study. During the data assimilation process, although updating the forecast state using observations can reduce the analysis error, the water balance based on the physics in the model could be destroyed. To alleviate the imbalance in the water budget, a weak water balance constrain filter is adopted. The proposed weakly constrained EnKF that includes forecast error inflation and vertical localization was applied to a synthetic experiment and two real data experiments. The results of the assimilation process suggest that the inflation approach effectively reduce both the short-lived analysis error and the analysis bias in shallow layers, while the vertical localization approach avoids increase in analysis error in deep layers. The weak constraint on the water balance reduces the degree of the water budget imbalance at the price of a small increase in the analysis error.

This paper used four cities -Meishan, Ziyang, Suining and Guang'an -as its research objects. All four cities are located in the middle of Sichuan Basin, closed by each other and distributed along a stripe. The band6 of the Landsat ETM+ Image in two time periods were used to recover the urban brightness temperature so as to disclose the features of their heat island effect and analyze its evolution. As shown by this research, the heat island effect in the cities in the middle of Sichuan Basin is getting stronger along with the cities' development. The features of the heat island effect have close relation to changes in the process of urbanization. The expansion of urban space and the distribution of industrial zones have evident impacts on the intensity of the heat island. The scale and water body of the cities are the important factors that affect the cities' heat island effect. So properly increasing water area is an effective way to alleviate urban heat island.

in this paper, NOAA/AVHRR data were used, which were obtained in winter of 2004 and 2006 respectively. And using NOAA/AVHRR data in spring of 2004 as reference, day-night characteristics and spatial distribution characteristics were received. The following are research results. In the daytime, urban heat island intensity was stronger than that at night. The maximum of urban heat island intensity was about 7.5 and 9.0 respectively.At night, urban heat island demonstrated a singlecenter or double-centers, and urban heat island center lied within the second circuit road. In the daytime, urban heat island demonstrated multi-centers. Urban heat island centers occurred in industrial and commercial districts, in transport hub, which locate in the north, the east, or the southwest of the city. Little higher temperature zone distributed like a non-closed ring. High temperature ring occurred nearby the second and third circuit road. The ring characteristic of urban heat island had close relationship with urban structure and urban activities, which was consist with some research results in summer.