Advanced search

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

Previous Articles     Next Articles

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 E-mail:zhuoga2013@yahoo.com
  • 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).

RichHTML

16

PDF (PC)

116

Abstract:

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
78.00°E–99.00°E 		Tibet Meteorological Bureau
APHRO Daily 0.5°×0.5° Gridded 5,000–12,000 1980–2007 15.00°N–55.00°N
60.00°E–150.00°E 		APHRODITE’s water resources http://www.chikyu.ac.jp/precip/
GPCC Monthly 0.5°×0.5° Gridded 67,200 1980–2013 90.00°N–90.00°S
0.00°E–360.00°E 		GPCC http://www.esrl.noaa.gov/psd/data/gridded/data.gpcc.html
UDel Monthly 0.5°×0.5° Gridded 4,100–22,000 1980–2013 89.75°N–89.75°S
0.25°E–359.75°E 		https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html
CMA Monthly 0.5°×0.5° Gridded 2,472 1980–2013 18.25°N–53.75°N
72.25°E–135.75°E 		CMA http://cdc.cma.gov.cn

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

Figure 8

Spatial distribution of mean precipitation trend (unit: mm/10a) in the rainy season from observations and gridded products during the period 1980–2013 (unit: mm/10a). (a) observations, (b) APHRO, (c) GPCC, (d) UDel, and (e) CMA"

Table 3

Temporal variation of the linear trend for gridded products during the period 1980–2013 (unit: mm/10a)"

Data APHRO GPCC UDel CMA Observations
Rainy season 7.08 9.89 2.90 12.70 8.55

Figure 9

Correlation coefficients of the (a) spatial and (b–e) temporal variations between products and observations at meteorological stations in Tibet during the period 1980–2013. (b) APHRO, (c) GPCC, (d) UDel, and (e) CMA. The value 0.312/0.329 represents time/area that exceeds a significance level of 95% respectively"

1 Bao XH, Zhang FQ, Sun JH Diurnal variations of warm-season precipitation east of the Tibetan Plateau over China. Monthly Weather Review 2011; 139: 9 2790- 2810.
doi: 10.1175/MWR-D-11-00006.1
2 Chen YL, Chen DH, Li ZC, et al. Preliminary studies on the dynamic prediction method of rainfall-triggered landslide. Journal of Mountain Science 2016; 13: 10 1735- 1745.
doi: 10.1007/s11629-014-3110-5
3 Dai AG, Fung IY, Del Genio AD Surface observed global land precipitation variations during 1900–88. Journal of Climate 1997; 10: 11 2943- 2962.
doi: 10.1175/1520-0442(1997)010<2943:SOGLPV>2.0.CO;2
4 Duan AM, Wu GX Role of the Tibetan Plateau thermal forcing in the summer climate patterns over subtropical Asia. Climate Dynamics 2005; 24: 7–8 793- 807.
doi: 10.1007/s00382-004-0488-8
5 Ensor LA, Robeson SM Statistical characteristics of daily precipitation: comparisons of gridded and point datasets. Journal of Applied Meteorology and Climatology 2008; 47: 9 2468- 2476.
doi: 10.1175/2008JAMC1757.1
6 Groisman PY, Koknaeva VV, Belokrylova TA, et al. Overcoming biases of precipitation measurement: a history of the USSR experience. Bulletin of the American Meteorological Society 1991; 72: 11 1725- 1733.
doi: 10.1175/1520-0477(1991)072<1725:OBOPMA>2.0.CO;2
7 Hutchinson MF Interpolation of rainfall data with thin plate smoothing splines — part I: two dimensional smoothing of data with short range correlation. Journal of Geographic Information and Decision Analysis 1998a; 2: 2 139- 151.
8 Hutchinson MF Interpolation of rainfall data with thin plate smoothing splines — part II: analysis of Topographic dependence. Journal of Geographic Information and Decision Analysis 1998b; 2: 2 152- 167.
9 Juárez RIN, Li WH, Fu R, et al. Comparison of precipitation datasets over the tropical south American and African continents. Journal of Hydrometeorology 2009; 10: 1 289- 299.
doi: 10.1175/2008JHM1023.1
10 Karl TR, Quayle RG, Groisman PY Detecting climate variations and change: new challenges for observing and data management systems. Journal of Climate 1993; 6: 8 1481- 1494.
doi: 10.1175/1520-0442(1993)006<1481:DCVACN>2.0.CO;2
11 Kidd C, Dawkins E, Huffman G Comparison of precipitation derived from the ECMWF operational forecast model and satellite precipitation datasets. Journal of Hydrometeorology 2013; 14: 5 1463- 1482.
doi: 10.1175/JHM-D-12-0182.1
12 Klein Tank AMG, Wijngaard JB, Können GP, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology 2002; 22: 12 1441- 1453.
doi: 10.1002/joc.773
13 Legates DR, Willmott CJ Mean seasonal and spatial variability in gauge-corrected, global precipitation. International Journal of Climatology 1990; 10: 2 111- 127.
doi: 10.1002/joc.3370100202
14 Li YQ, Li DJ, Yang S, et al. Characteristics of the precipitation over the eastern edge of the Tibetan Plateau. Meteorology and Atmospheric Physics 2010; 106: 1–2 49- 56.
doi: 10.1007/s00703-009-0048-1
15 Liu Z Evaluation of precipitation climatology derived from TRMM multi-satellite precipitation analysis (TMPA) monthly product over land with two gauge-based products. Climate 2015; 3: 4 964- 982.
doi: 10.3390/cli3040964
16 Long QC, Chen QL, Gui K, et al. A case study of a heavy rain over the southeastern Tibetan Plateau. Atmosphere 2016; 7: 9 118.
doi: 10.3390/atmos7090118
17 Ma YZ, Tang GQ, Long D, et al. Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau. Remote Sensing 2016; 8: 7 569.
doi: 10.3390/rs8070569
18 Mastyło M Bilinear interpolation theorems and applications. Journal of Functional Analysis 2013; 265: 2 185- 207.
doi: 10.1016/j.jfa.2013.05.001
19 Maussion F, Scherer D, Mölg T, et al. Precipitation seasonality and variability over the Tibetan Plateau as resolved by the high Asia reanalysis. Journal of Climate 2014; 27: 5 1910- 1927.
doi: 10.1175/JCLI-D-13-00282.1
20 Schneider U, Becker A, Finger P, et al., 2011. GPCC full data reanalysis version 6.0 at (0.5°, 1.0°, 2.5°): monthly land-surface precipitation from rain-gauges built on GTS-based and historic data. Main, Germany: GPCC, DOI: 10.5676/DWD_GPCC/FD_M_V6_050.
21 Şen Z, Habib Z Spatial analysis of monthly precipitation in Turkey. Theoretical and Applied Climatology 2000; 67: 1–2 81- 96.
doi: 10.1007/s007040070017
22 Song SY, Wang PX, 2013. Tibet Climate. Beijing: Meteorological Press, pp. 35–73.
23 Tong K, Su FG, Yang DQ, et al. Tibetan Plateau precipitation as depicted by gauge observations, reanalyses and satellite retrievals. International Journal of Climatology 2014; 34: 2 265- 285.
doi: 10.1002/joc.3682
24 Wu L, Zhai PM Validation of daily precipitation from two high-resolution satellite precipitation datasets over the Tibetan Plateau and the regions to its east. Acta Meteorologica Sinica 2012; 26: 6 735- 745.
doi: 10.1007/s13351-012-0605-2
25 Xiao H Bilinear interpolation parallel algorithm based on GPU computing. Journal of Chinese Computer Systems 2011; 32: 11 2241- 2245.
26 Xu Y, Gao XJ, Shen Y, et al. A daily temperature dataset over China and its application in validating a RCM simulation. Advances in Atmospheric Sciences 2009; 26: 4 763- 772.
doi: 10.1007/s00376-009-9029-z
27 Yanai M, Li CF, Song ZS Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon. Journal of the Meteorological Society of Japan 1992; 70: 1 319- 351.
doi: 10.2151/jmsj1965.70.1B_319
28 Yatagai A, Arakawa O, Kamiguchi K, et al. A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Scientific Online Letters on the Atmosphere 2009; 5: 1 137- 140.
doi: 10.2151/sola.2009-035
29 Yatagai A, Kamiguchi K, Arakawa O, et al. APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bulletin of the American Meteorological Society 2012; 93: 9 1401- 1415.
doi: 10.1175/BAMS-D-11-00122.1
30 Ye DZ, Gao YX, 1979. The Meteorology of the Qinghai-Xizang (Tibet) Plateau. Beijing: Science Press, pp. 1–278.
31 You QL, Kang SC, Pepin N, et al. Relationship between temperature trend magnitude, elevation and mean temperature in the Tibetan Plateau from homogenized surface stations and reanalysis data. Global and Planetary Change 2010; 71: 1–2 124- 133.
doi: 10.1016/j.gloplacha.2010.01.020
32 You QL, Fraedrich K, Ren GY, et al. Inconsistencies of precipitation in the eastern and central Tibetan Plateau between surface adjusted data and reanalysis. Theoretical and Applied Climatology 2012; 109: 3–4 485- 496.
doi: 10.1007/s00704-012-0594-1
33 You QL, Min JZ, Zhang W, et al. Comparison of multiple datasets with gridded precipitation observations over the Tibetan Plateau. Climate Dynamics 2015; 45: 3–4 791- 806.
doi: 10.1007/s00382-014-2310-6
34 Zhang XL, Wang SJ, Zhang JM, et al. Temporal and spatial variability in precipitation trends in the southeast Tibetan Plateau during 1961–2012. Climate of the Past Discussions 2015; 11: 1 447- 487.
doi: 10.5194/cpd-11-447-2015
35 Zhao TB, Fu CB Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China. Advances in Atmospheric Sciences 2006; 23: 4 593- 604.
doi: 10.1007/s00376-006-0593-1
[1] Xia Zhao,JunFeng Wang,Yun Wang,Xiang Lu,ShaoFang Liu,YuBao Zhang,ZhiHong Guo,ZhongKui Xie,RuoYu Wang. Influence of proximity to the Qinghai-Tibet highway and railway on variations of soil heavy metal concentrations and bacterial community diversity on the Tibetan Plateau [J]. Sciences in Cold and Arid Regions, 2019, 11(6): 407-418.
[2] ZeYong Hu,ZhiPeng Xie. Origin and advances in implementing blowing-snow effects in the Community Land Model [J]. Sciences in Cold and Arid Regions, 2019, 11(5): 335-339.
[3] Rui Chen,MeiXue Yang,XueJia Wang,GuoNing Wan. Review on simulation of land-surface processes on the Tibetan Plateau [J]. Sciences in Cold and Arid Regions, 2019, 11(2): 93-115.
[4] HongWei Wang,Yuan Qi,ChunLin Huang,XiaoYing Li,XiaoHong Deng,JinLong Zhang. Analysis of vegetation changes and dominant factors on the Qinghai-Tibet Plateau, China [J]. Sciences in Cold and Arid Regions, 2019, 11(2): 150-158.
[5] YanLi Xie, QiHao Yu, YanHui You, ZhongQiu Zhang, TingTao Gou. The changing process and trend of ground temperature around tower foundations of Qinghai-Tibet Power Transmission line [J]. Sciences in Cold and Arid Regions, 2019, 11(1): 13-20.
[6] Wei Cao,Yu Sheng,Ji Chen,JiChun Wu. Applying the AHP-FUZZY method to evaluate the measure effect of rubble roadbed engineering in permafrost regions of Qinghai-Tibet Plateau: a case study of Chaidaer-Muli Railway [J]. Sciences in Cold and Arid Regions, 2018, 10(6): 447-457.
[7] RuiQing Li,YanHong Gao,DeLiang Chen,YongXin Zhang,SuoSuo Li. Contrasting vegetation changes in dry and humid regions of the Tibetan Plateau over recent decades [J]. Sciences in Cold and Arid Regions, 2018, 10(6): 482-492.
[8] YinHuan Ao, ShiHua Lyu, ZhaoGuo Li, LiJuan Wen, Lin Zhao. Numerical simulation of the climate effect of high-altitude lakes on the Tibetan Plateau [J]. Sciences in Cold and Arid Regions, 2018, 10(5): 379-391.
[9] CaiXia Zhang,XunMing Wang,YongZhong Su,ZhiWen Han,ZhengCai Zhang,Ting Hua. Change in summer daily precipitation and its relation with air temperature in Northwest China during 1957–2016 [J]. Sciences in Cold and Arid Regions, 2018, 10(4): 317-325.
[10] Stuart A. Harris, HuiJun Jin, RuiXia He, SiZhong Yang. Tessellons, topography, and glaciations on the Qinghai-Tibet Plateau [J]. Sciences in Cold and Arid Regions, 2018, 10(3): 187-206.
[11] HeWen Niu, XiaoFei Shi, Gang Li, JunHua Yang, ShiJin Wang. Characteristics of total suspended particulates in the atmosphere of Yulong Snow Mountain, southwestern China [J]. Sciences in Cold and Arid Regions, 2018, 10(3): 207-218.
[12] ZhenMing Wu, Lin Zhao, Lin Liu, Rui Zhu, ZeShen Gao, YongPing Qiao, LiMing Tian, HuaYun Zhou, MeiZhen Xie. Surface-deformation monitoring in the permafrost regions over the Tibetan Plateau, using Sentinel-1 data [J]. Sciences in Cold and Arid Regions, 2018, 10(2): 114-125.
[13] BenLi Liu, JianJun Qu, ShiChang Kang, Bing Liu. Climate change inferred from aeolian sediments in a lake shore environment in the central Tibetan Plateau during recent centuries [J]. Sciences in Cold and Arid Regions, 2018, 10(2): 134-144.
[14] SiQiong Luo, BoLi Chen, ShiHua Lyu, XueWei Fang, JingYuan Wang, XianHong Meng, LunYu Shang, ShaoYing Wang, Di Ma. An improvement of soil temperature simulations on the Tibetan Plateau [J]. Sciences in Cold and Arid Regions, 2018, 10(1): 80-94.
[15] MingJun Zhang, ShengJie Wang. Precipitation isotopes in the Tianshan Mountains as a key to water cycle in arid central Asia [J]. Sciences in Cold and Arid Regions, 2018, 10(1): 27-37.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] Mohan Bahadur Chand,Rijan Bhakta Kayastha. Study of thermal properties of supraglacial debris and degree-day factors on Lirung Glacier, Nepal[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 357 -368 .
[2] AiHong Xie, ShiMeng Wang, YiCheng Wang, ChuanJin Li. Comparison of temperature extremes between Zhongshan Station and Great Wall Station in Antarctica[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 369 -378 .
[3] YanZai Wang, YongQiu Wu, MeiHui Pan, RuiJie Lu. Comparison of two classification methods to identify grain size fractions of aeolian sediment[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 413 -420 .
[4] YinHuan Ao, ShiHua Lyu, ZhaoGuo Li, LiJuan Wen, Lin Zhao. Numerical simulation of the climate effect of high-altitude lakes on the Tibetan Plateau[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 379 -391 .
[5] Rong Yang, JunQia Kong, ZeYu Du, YongZhong Su. Altitude pattern of carbon stocks in desert grasslands of an arid land region[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 404 -412 .
[6] Yang Qiu, ZhongKui Xie, XinPing Wang, YaJun Wang, YuBao Zhang, YuHui He, WenMei Li, WenCong Lv. Effect of slow-release iron fertilizer on iron-deficiency chlorosis, yield and quality of Lilium davidii var. unicolor in a two-year field experiment[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 421 -427 .
[7] Ololade A. Oyedapo,Joseph M. Agbedahunsi,H. C Illoh,Akinwumi J. Akinloye. Comparative foliar anatomy of three Khaya species (Meliaceae) used in Nigeria as antisickling agent[J]. Sciences in Cold and Arid Regions, 2018, 10(4): 279 -285 .
[8] YuMing Wei, XiaoFei Ma, PengShan Zhao. Transcriptomic comparison to identify rapidly evolving genes in Braya humilis[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 428 -435 .
[9] FangLei Zhong, AiJun Guo, XiaoJuan Yin, JinFeng Cui, Xiao Yang, YanQiong Zhang. Sociodemographic characteristics, cultural biases, and environmental attitudes: An empirical application of grid-group cultural theory in Northwestern China[J]. Sciences in Cold and Arid Regions, 2018, 10(5): 436 -446 .
[10] Yong Chen,Tao Wang,LiHua Zhou,Rui Wang. Industrialization model of enterprises participating in ecological management and suggestions: A case study of the Hobq Model in Inner Mongolia[J]. Sciences in Cold and Arid Regions, 2018, 10(4): 286 -292 .