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Evaluation of precipitation from CMORPH, GPCP-2, TRMM 3B43, GPCC, and ITPCAS with ground-based measurements in the Qinling-Daba Mountains, China.

Gefei WangPeiyun ZhangLiwen LiangShiqiang Zhang
Published in: PloS one (2017)
The correspondence between five precipitation products, including CMORPH, GPCP-2, TRMM 3B43, GPCC, and ITPCAS, and ground-based measurements of precipitation were evaluated on annual, seasonal, and monthly scales during 2000-2014 in the Qinling-Daba Mountains over China, which is a significant area with vital value of climate and hydrology. Performances of the precipitation products in the relatively arid/humid years were also analyzed. In general, ITPCAS data displayed the highest accuracy, GPCP-2 and CMORPH data showed relatively poor performance, and GPCC and TRMM 3B43 data were average at different temporal scales among the five precipitation products. The Pearson correlation coefficient of each station had minor fluctuations for the five precipitation products. A larger deviation was found at Wudu station, most likely due to the undulating terrain. The performances of the precipitation products from highest to least accuracy are as follows: ITPCAS > TRMM 3B43 > GPCC > GPCP-2 > CMORPH. Except for CMORPH (-20.76%), the percentage precipitation differences (PPDs) of the other four precipitation products fluctuated in the range of 10% during the relatively arid (2001) and humid (2011) years. In addition, all precipitation products and ground gauge observed precipitation did not show an obvious gradient with altitude, which is different from that in other mountainous areas and is perhaps due to complex terrain, lack of observation in high altitudes, and precipitation undercatch. In consideration of the significance of Qinling-Daba Mountains as the geographic and ecological dividing lines, the present study may provide a new perspective for hydrological, climatic, and ecological researches and practices in local and other mountainous areas.
Keyphrases
  • climate change
  • healthcare
  • magnetic resonance imaging
  • big data
  • computed tomography
  • machine learning