Combining CHAMP and Swarm Satellite Data to Invert the Lithospheric Magnetic Field in the Tibetan Plateau

Abstract. The impact of aerosols above and around the Tibetan Plateau on the Asian Summer Monsoon during pre-monsoon seasons March-April-May 2007, 2008, and 2009 is investigated by means of remote sensing and radiative transfer modelling. Four source regions are found to be responsible for the high aerosol loading around the Tibetan Plateau: the Taklamakan Desert, the Ganges Plains, the Indus Plains, and the Arabian Sea. CALIPSO lidar satellite data, providing vertically resolved images of aerosols, shows aerosol concentrations to be highest in the lower 5 km of the atmosphere with only little amounts reaching the Tibetan Plateau altitude. Using a radiative transfer model we find that aerosol plumes reduce shortwave radiation throughout the Monsoon region in the seasonal average by between 20 and 30 W/m2. Peak shortwave heating in the lower troposphere reaches 0.2 K/day. In higher layers this shortwave heating is partly balanced by longwave cooling. Although high-albedo surfaces, such as deserts or the Tibetan Plateau, increase the shortwave heating by around 10%, the overall effect is strongest close to the aerosol sources. A strong elevated heating which could influence large-scale monsoonal circulations as suggested by previous studies is not found.

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Comparison of observed and modelled radiation budget over the Tibetan Plateau using satellite data

International Journal of Climatology ◽

10.1002/joc.3370150406 ◽

1995 ◽

Vol 15 (4) ◽

pp. 423-445 ◽

Cited By ~ 3

Author(s):

Zhian Sun

Keyword(s):

Tibetan Plateau ◽

Satellite Data ◽

Radiation Budget ◽

The Tibetan Plateau

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Monitoring snow cover on the Tibetan Plateau using passive microwave satellite data

Passive Microwave Remote Sensing of Land-Atmosphere Interactions ◽

10.1515/9783112319307-018 ◽

1995 ◽

pp. 285-294

Keyword(s):

Tibetan Plateau ◽

Snow Cover ◽

Satellite Data ◽

Passive Microwave ◽

The Tibetan Plateau

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Combining Snow Depth From FY-3C and In Situ Data Over the Tibetan Plateau Using a Nonlinear Analysis Method

Frontiers in Physics ◽

10.3389/fphy.2021.672288 ◽

2021 ◽

Vol 9 ◽

Author(s):

Aixia Feng ◽

Feng Gao ◽

Qiguang Wang ◽

Aiqing Feng ◽

Qiang Zhang ◽

...

Keyword(s):

Time Series ◽

Time Delay ◽

Phase Space ◽

Tibetan Plateau ◽

Satellite Data ◽

Snow Depth ◽

Hydrological Cycle ◽

The Tibetan Plateau ◽

Analysis Method

Snow cover over the Tibetan Plateau plays a vital role in the regional and global climate system because it affects not only the climate but also the hydrological cycle and ecosystem. However, high-quality snow data are hindered due to the sparsity of observation networks and complex terrain in the region. In this study, a nonlinear time series analysis method called phase space reconstruction was used to obtain the Tibetan Plateau snow depth by combining the FY-3C satellite data and in situ data for the period 2014–2017. This method features making a time delay reconstruction of a phase space to view the dynamics. Both of the grids and their nearby in situ snow depth time series were reconstructed with two appropriate parameters called time delay and embedding dimension. The values of the snow depth for grids were averaged over the in situ observations and retrieval of the satellite if their two parameters were the same. That implies that the two trajectories of the time series had the same evolution trend. Otherwise, the snow depth values for grids were averaged over the in situ observation. If there were no in situ sites within the grids, the retrieval of the satellite remained. The results show that the integrated Tibetan Plateau snow depth (ITPSD) had an average bias of –1.35 cm and 1.14 cm, standard deviation of the bias of 3.96 cm and 5.67 cm, and root mean square error of 4.18 cm and 5.79 cm compared with the in situ data and FY-3C satellite data, respectively. ITPSD expressed the issue that snow depth is usually overestimated in mountain regions by satellites. This is due to the introduction of more station observations using a dynamical statistical method to correct the biases in the satellite data.

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