Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.
CONG Zhiyuan
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