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Fidelity of the APHRODITE Dataset in Representation of Extreme Precipitation over Central Asia

Fund Project:

National Key Research and Development Program of China (2018YFC1507101) National Natural Science Foundation of China (41861144014, 41875078 and 41630424)

  • Using rain-gauge-observation daily precipitation data from the Global Historical Climatology Network (V3.25) and the Chinese surface daily climate dataset (V3.0), this study investigates the fidelity of the AHPRODITE dataset in representing extreme precipitation, in terms of the extreme precipitation threshold value, occurrence number, probability of detection and extremal dependence index during the cool (October to April) and warm (May to September) seasons in Central Asia during 1961–1990. The distribution of extreme precipitation is characterized by large extreme precipitation threshold values and high occurrence numbers over the mountainous areas. The APHRODITE dataset is highly correlated with the gauge-observation precipitation data and can reproduce the spatial distributions of the extreme precipitation threshold value and total occurrence number. However, APHRODITE generally underestimates the extreme precipitation threshold values, while it overestimates the total numbers of extreme precipitation events, particularly over the mountainous areas. These biases can be attributed to the overestimation of light rainfall and the underestimation of heavy rainfall induced by the rainfall distribution-based interpolation. Such deficits are more evident for the warm season with respect to the cool season, and thus the biases are more pronounced in the warm season than in the cool season. The probability of detection and extremal dependence index reveal that APHRODITE has a good capability of detecting extreme precipitation, particularly in the cool season.
  • [1] Yongguang ZHENG, Yanduo GONG, Jiong CHEN, Fuyou TIAN, 2019: Warm-Season Diurnal Variations of Total, Stratiform, Convective, and Extreme Hourly Precipitation over Central and Eastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 143-159.  doi: 10.1007/s00376-018-7307-3
    [2] Gao Xuejie, Zhao Zongci, Filippo Giorgi, 2002: Changes of Extreme Events in Regional Climate Simulations over East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 927-942.  doi: 10.1007/s00376-002-0056-2
    [3] NING Liang, QIAN Yongfu, 2009: Interdecadal Change in Extreme Precipitation over South China and Its Mechanism, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 109-118.  doi: 10.1007/s00376-009-0109-x
    [4] Dorina CHYI, Zuowei XIE, Ning SHI, Pinwen GUO, Huijun WANG, 2020: Wave-Breaking Features of Blocking over Central Siberia and Its Impacts on the Precipitation Trend over Southeastern Lake Baikal, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 75-89.  doi: 10.1007/s00376-019-9048-3
    [5] GAO Yanhong, Yongkang XUE, PENG Wen, Hyun-Suk KANG, Duane WALISER, 2011: Assessment of Dynamic Downscaling of the Extreme Rainfall over East Asia Using a Regional Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1077-1098.  doi: 10.1007/s00376-010-0039-7
    [6] ZHOU Botao, ZHAO Ping, 2010: Influence of the Asian-Pacific Oscillation on Spring Precipitation over Central Eastern China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 575-582.  doi: 10.1007/s00376-009-9058-7
    [7] Zhiwei HE, Qinghong ZHANG, Jun SUN, 2016: The Contribution of Mesoscale Convective Systems to Intense Hourly Precipitation Events during the Warm Seasons over Central East China, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1233-1239.  doi: 10.1007/s00376-016-6034-x
    [8] ZHANG Huan, ZHAI Panmao, 2011: Temporal and Spatial Characteristics of Extreme Hourly Precipitation over Eastern China in the Warm Season, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1177-1183.  doi: 10.1007/s00376-011-0020-0
    [9] Yujie WANG, Botao ZHOU, Dahe QIN, Jia WU, Rong GAO, Lianchun SONG, 2017: Changes in Mean and Extreme Temperature and Precipitation over the Arid Region of Northwestern China: Observation and Projection, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 287-305.  doi: 10.1007/s00376-016-6160-5
    [10] XU Ying, GAO Xuejie, SHEN Yan, XU Chonghai, SHI Ying, F. GIORGI, 2009: A Daily Temperature Dataset over China and Its Application in Validating a RCM Simulation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 763-772.  doi: 10.1007/s00376-009-9029-z
    [11] LI Hongmei, FENG Lei, ZHOU Tianjun, 2011: Multi-model Projection of July--August Climate Extreme Changes over China under CO$_{2}$ Doubling. Part I: Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 433-447.  doi: 10.1007/s00376-010-0013-4
    [12] Juan AO, Jianqi SUN, 2016: The Impact of Boreal Autumn SST Anomalies over the South Pacific on Boreal Winter Precipitation over East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 644-655.  doi: 10.1007/s00376-015-5067-x
    [13] LIU Run, LIU Shaw Chen, Ralph J. CICERONE, SHIU Chein-Jung, LI Jun, WANG Jingli, ZHANG Yuanhang, 2015: Trends of Extreme Precipitation in Eastern China and Their Possible Causes, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1027-1037.  doi: 10.1007/s00376-015- 5002-1
    [14] ZHOU Ningfang, YU Yongqiang, QIAN Yongfu, 2006: Simulations of the 100-hPa South Asian High and Precipitation over East Asia with IPCC Coupled GCMs, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 375-390.  doi: 10.1007/s00376-006-0375-9
    [15] FU Yunfei, LIN Yihua, Guosheng LIU, WANG Qiang, 2003: Seasonal Characteristics of Precipitation in 1998 over East Asia as Derived from TRMM PR, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 511-529.  doi: 10.1007/BF02915495
    [16] Dong-Kyou LEE, William J. GUTOWSKI, Jr., Hyun-Suk KANG, Chun-Ji KIM, 2007: Intercomparison of Precipitation Simulated by Regional Climate Models over East Asia in 1997 and 1998, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 539-554.  doi: 10.1007/s00376-007-0539-2
    [17] Yun-Young LEE, Richard GROTJAHN, 2019: Evidence of Specific MJO Phase Occurrence with Summertime California Central Valley Extreme Hot Weather, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 589-602.  doi: 10.1007/s00376-019-8167-1
    [18] Ruidan CHEN, Zhiping WEN, Riyu LU, Chunzai WANG, 2019: Causes of the Extreme Hot Midsummer in Central and South China during 2017: Role of the Western Tropical Pacific Warming, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 465-478.  doi: 10.1007/s00376-018-8177-4
    [19] V. HAMZA, C. A. BABU, T. P. SABIN, 2007: Characteristic Study of the Boundary Layer Parameters over the Arabian Sea and the Bay of Bengal Using the QuikSCAT Dataset, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 631-643.  doi: 10.1007/s00376-007-0631-7
    [20] Yuan WANG, 2015: Air Pollution or Global Warming: Attribution of Extreme Precipitation Changes in Eastern China——Comments on "Trends of Extreme Precipitation in Eastern China and Their Possible Causes", ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1444-1446.  doi: 10.1007/s00376-015-5109-4

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Manuscript History

Manuscript received: 07 April 2020
Manuscript revised: 30 July 2020
Manuscript accepted: 30 July 2020
通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Fidelity of the APHRODITE Dataset in Representation of Extreme Precipitation over Central Asia

Abstract: Using rain-gauge-observation daily precipitation data from the Global Historical Climatology Network (V3.25) and the Chinese surface daily climate dataset (V3.0), this study investigates the fidelity of the AHPRODITE dataset in representing extreme precipitation, in terms of the extreme precipitation threshold value, occurrence number, probability of detection and extremal dependence index during the cool (October to April) and warm (May to September) seasons in Central Asia during 1961–1990. The distribution of extreme precipitation is characterized by large extreme precipitation threshold values and high occurrence numbers over the mountainous areas. The APHRODITE dataset is highly correlated with the gauge-observation precipitation data and can reproduce the spatial distributions of the extreme precipitation threshold value and total occurrence number. However, APHRODITE generally underestimates the extreme precipitation threshold values, while it overestimates the total numbers of extreme precipitation events, particularly over the mountainous areas. These biases can be attributed to the overestimation of light rainfall and the underestimation of heavy rainfall induced by the rainfall distribution-based interpolation. Such deficits are more evident for the warm season with respect to the cool season, and thus the biases are more pronounced in the warm season than in the cool season. The probability of detection and extremal dependence index reveal that APHRODITE has a good capability of detecting extreme precipitation, particularly in the cool season.

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