• Atmosphere
• /
• v.22 no.1
• /
• pp.117-128
• /
• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• Korean Journal of Remote Sensing
• /
• v.28 no.5
• /
• pp.501-508
• /
• 2012

Albedo is known as a factor that directly impacts on the surface energy balance one of the elements of earth radiation balance. The change of albedo includes the change of soil moisture, vegetation, solar zenith angle, snow, and so on. In addition, it operates as a crucial path to understanding feedback mechanisms between radiation balance and its influence on climate and vegetation dynamics and therefore, observing the variation of albedo is a one of the essential procedures for anticipating climate change. In this study, we used MODIS 16-Day composited Albedo data from 2001 to 2011 years with the purpose of observing the change of albedo over Northeast Asia. According to the tendency of albedo for 11 years, albedo in the area of an active vegetation has increased in near-infrared (NIR) domain and decreased in visible (VIS) domain. On the basis of local changes in vegetation in 2002, the both area of the Gobi Desert and the Manchuria was enormously changed and chosen the research area and furthermore, the vegetation of both regions had deteriorated due to the change of the minimum value since 2010.