**3. Analysis between AMSR-E brightness temperature and ground snow depth over Tibet Plateau, China**

#### **3.1 Introduction**

Over the Tibet Plateau (Western China), snow cover is presented only for a few months per year, except mountainous areas. However, it highly influences the energy flux, atmosphere dynamics and surface water reservoirs. Recently, much effort has been put into developing region-specific retrieval algorithms for snow parameter retrieval from passive microwave measurements. Automatic station observations of snow cover are essential factors in the development of these retrieval algorithms, but they cannot provide comprehensive information on the snow cover distribution. The recent study has improved the snow depth accuracy for some extent, but the method highly depend on the method training for the

The products of SCA and SWE could provide a long time series data and derived snow climatological analysis, when compared the optical and microwave remote sensing products of snow, IMS SCA and NISE SCA show difference each other, the blank area in Tibet and northwestern China could not enough to provide analytical result, though these are some clues on it. The snow product of IMS seems provide more reliable results over China area, and it is recommended that a new snow algorithm from satellite is needed for the accuracy

In the traditional view, the satellite data could provide more reliable large-scale snow parameters than the local observational station, the trend from several snow products provides the same continental regime over Northern American, it looks like snow cover gets a negative response to the global warming, while, a near local look over the Tibet Plateau, the result shows that the snow cover area appears a positive trend with snow equivalent

From the monthly snow water equivalent (mm) which is recorded from the AMSR-E/Aqua, two snow parameters are derived, one is the averaged SWE monthly and another is the snow cover area (squa. km). The result reveals the positive trend of the averaged SWE (mm) and snow cover area (squa. km) over the Tibet Plateau area, which is the same situation with the result of western China (Qin, 2006 and Xu, 2007). While the monthly trend for more than ten years, we can find some interest results (see b part in 2.2.5). The averaged SWE and Snow cover area experience slightly increasing trend in the summer and autumn time (June, July, August and September), while in the winter and spring time (from October to next

From the MODIS SCF time series analysis according to the different percentage pixels, we can find that the SCF less than 20% are quite variable with more pixels in summer time than that in the winter time, while all of the pixels that contain more snow indicate a similar positive trend, and less pixels in summer time than that in winter time. The higher of the SCF, the higher trend value for the line. The data from the MODIS/Aqua show very similar

It is hoped that China mainland area whose cryosphere is a major element in the climate now undertake national programs designed to address questions of global environment

**3. Analysis between AMSR-E brightness temperature and ground snow depth** 

Over the Tibet Plateau (Western China), snow cover is presented only for a few months per year, except mountainous areas. However, it highly influences the energy flux, atmosphere dynamics and surface water reservoirs. Recently, much effort has been put into developing region-specific retrieval algorithms for snow parameter retrieval from passive microwave measurements. Automatic station observations of snow cover are essential factors in the development of these retrieval algorithms, but they cannot provide comprehensive information on the snow cover distribution. The recent study has improved the snow depth accuracy for some extent, but the method highly depend on the method training for the

water from PSW dataset, and the situation is also same over the China West Area.

May), these parameters shows its negative trend.

result as that of MODIS/Terra, but larger area than Terra's.

assessment.

change.

**over Tibet Plateau, China** 

**3.1 Introduction** 

artificial neural network methodology (Yungang Cao, 2008) without more physical explanation. From the Fig.14, the distribution of the meteorological stations in the Tibet Plateau can be seen to be very sparse, especially over the main part of the Plateau. Furthermore, many of them are near areas of human activity, and provide few measurements for a long time span with very shallow snow depth values (see Fig.15 example for DanXung Station) (Che, 2004). Armstrong (2001) notes that passive microwave remote sensing tends to underestimate the snow in the fall and early winter due to the weak signal of thin snow with the 36.5GHz and 18.7GHz (Armstrong, 2001), while the situation is the opposite over Tibet Plateau. Matthew H. Savoie (2009) improved the accuracy of the snow measurement by considering the atmospheric influence to some extent; Qiu etc. (2009) paid attention to the atmosphere influence via the experiment and model simulation.

Due to the thin snow (snow occurrence) is often seen over western China, especially over the Tibet Plateau, more comprehensive analysis is urgent with the station observation data and microwave Tbs. In this work, we consider the shallow snow situation, and try to explain the discrepancy between the in situ time series measurement of snow (snow depth, SD) and the values retrieved from passive microwave remote sensing with the traditional difference between the brightness temperature at 36.5GHz and 18.7GHz, and that from 89.0GHz-18.7GHz and 18.7GHz-10.7GHz. Then, we analyze the ability of the higher frequencies in snow parameter retrieval over the Tibet Plateau (e.g. 89.0GHz at AMSR-E) using the time series data comparison.
