Sciences in Cold and Arid Regions ›› 2018, Vol. 10 ›› Issue (5): 392-403.doi: 10.3724/SP.J.1226.2018.00392

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Comparison of precipitation products to observations in Tibet during the rainy season

Zhuo Ga1,2,*(),Za Dui3,Duodian Luozhu3,Jun Du2   

  1. 1 Lhasa Branch of Chengdu Institute of Plateau Meteorology, China Meteorological Administration, Lhasa, Tibet 850000, China
    2 Tibet Climate Center, Tibet Meteorological Bureau, Lhasa, Tibet 850000, China
    3 Lhasa Meteorological Bureau, Lhasa, Tibet 850000, China
  • Received:2017-07-13 Accepted:2018-06-28 Online:2018-11-19 Published:2018-11-21
  • Contact: Zhuo Ga
  • Supported by:
    This study was supported by the National Natural Science Foundation of China (Grant No. 41130960) and Key Science and Technology Plan of Tibet Autonomous Region (Grant No. XZ201703-GA-01).


Precipitation is an important component of global water and energy transport and a major aspect of climate change. Due to the scarcity of meteorological observations, the precipitation climate over Tibet has been insufficiently documented. In this study, the distribution of precipitation during the rainy season over Tibet from 1980 to 2013 is described on monthly to annual time scales with meteorological observations. Furthermore, four precipitation products are compared to observations over Tibet. These datasets include products derived from the Asian Precipitation-Highly-Resolved Observational Data (APHRO), the Global Precipitation Climatology Centre (GPCC), the University of Delaware (UDel), and the China Meteorological Administration (CMA). The error, relative error, standard deviation, root-mean-square error, correlations and trends between these products for the same period are analyzed with in situ precipitation during the rainy season from May to September. The results indicate that these datasets can broadly capture the temporal and spatial precipitation distribution over Tibet. The precipitation gradually increases from northwest to southeast. The spatial precipitation in GPCC and CMA are similar and positively correlated to observations. Areas with the largest deviations are located in southwestern Tibet along the Himalayas. The APHRO product underestimates, while the UDel, GPCC, and CMA datasets overestimates precipitation on the basis of monthly and inter-annual variation. The biases in GPCC and CMA are smaller than those in APHRO and UDel with a mean relative error lower than 10% during the same periods. The linear trend of precipitation indicates that the increase in precipitation has accelerated extensively during the last 30 years in most regions of Tibet. The CMA generally achieves the best performance of these four precipitation products. Data uncertainty in Tibet might be caused by the low density of stations, complex topography between the grid points and stations, and the interpolation methods, which can also produce an obvious difference between the gridded data and observations.

Key words: APHRO, GPCC, UDel, CMA, Tibet, precipitation

Figure 1

Elevation above sea level in Tibet with the locations of meteorological stations, red dots refer to the locations in the paper (unit: m)"

Table 1

Description of the precipitation products used in this study"

Dataset Time resolution
Spatial resolution
Data type Number of stations interpolate Data period Coverage Data sources
Rain gauge Monthly Irregular Site 38 1980–2013 26.00°N–37.00°N
Tibet Meteorological Bureau
APHRO Daily 0.5°×0.5° Gridded 5,000–12,000 1980–2007 15.00°N–55.00°N
APHRODITE’s water resources
GPCC Monthly 0.5°×0.5° Gridded 67,200 1980–2013 90.00°N–90.00°S
UDel Monthly 0.5°×0.5° Gridded 4,100–22,000 1980–2013 89.75°N–89.75°S
CMA Monthly 0.5°×0.5° Gridded 2,472 1980–2013 18.25°N–53.75°N

Figure 2

Spatial distribution of precipitation at meteorological stations in Tibet (unit: mm)"

Figure 3

Error of the gridded products with respect to meteorological observations (unit: mm): (a) APHRO minus observations, (b) GPCC minus observations, (c) UDel minus observations, and (d) CMA minus observations. The pink triangles indicate meteorological stations where the deviation in precipitation was larger than 300 mm"

Figure 4

Distribution of SD from different data sources (unit: mm). (a) observations, (b) APHRO, (c) GPCC, (d) UDel, and (e) CMA"

Figure 5

Distribution of RMSE between gridded products and observations (unit: mm): (a) APHRO, (b) GPCC, (c) UDel, and (d) CMA"

Figure 6

Monthly precipitation in (a) arid and humid sub-areas, in (b) western and (c) eastern Tibet from gridded products and observations during 1980–2013 (1980–2007 for APHRO) (unit: mm)"

Figure 7

Temporal variation in the RE between gridded products and observations during the period 1980–2013 (unit: %)"

Table 2

Inter-decadal variation of mean RE between gridded products and observations (unit: %)"

Data Period Rainy season
APHRO 1980s 16.70
1990s 14.11
2000s 19.31
GPCC 1980s 6.02
1990s 4.71
2000s 6.21
UDel 1980s 33.94
1990s 24.97
2000s 27.49
CMA 1980s 7.20
1990s 5.55
2000s 6.11