天气与气候

陆面同化及再分析降水资料在内蒙古地区的适用性

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  • 1.内蒙古农业大学水利与土木建筑工程学院,内蒙古 呼和浩特 010018
    2.内蒙古自治区生态与农业气象中心,内蒙古 呼和浩特 010051
宋海清(1988-),男,博士研究生,主要从事数据同化与气象水文模拟研究. E-mail: haiqingsong@emails.imau.edu.cn

收稿日期: 2021-01-05

  修回日期: 2021-03-09

  网络出版日期: 2021-11-29

基金资助

国家自然科学基金(51669018);国家自然科学基金(51779116);国家重点研发计划重大自然灾害监测预警与防范专项(2018YFC1506606)

Validation of land data assimilation and reanalysis precipitation datasets over Inner Mongolia

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  • 1. Water Conservancy and Civil Engineering College, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
    2. Ecology and Agrometeorology Center of Inner Mongolia, Hohhot 010051, Inner Mongolia, China

Received date: 2021-01-05

  Revised date: 2021-03-09

  Online published: 2021-11-29

摘要

利用1982—2018年内蒙古地区115个气象站点的月降水观测资料评估了FLDAS、ERA5、CRA40/Land和GLDAS 4种陆面同化及再分析降水资料在内蒙古地区的可靠性。结果表明:(1) 4种降水资料均能较好地表征降水量在内蒙古地区从东北向西部递减和冬季降水少、夏季降水多的时空变化特征。(2) 相关系数、绝对平均误差、均方根误差和纳什效率系数的计算结果表明,4种降水资料在夏季表现最优、冬季表现最差,在东部半湿润区和半干旱区好于西部干旱区和极干旱区。(3) ERA5资料在绝大多数时间对内蒙古的降水有高估现象,GLDAS却对降水存在低估,尤其是在冬季,GLDAS对固态降水几乎没有观测能力,而新发布的FLDAS资料表现较好。总体来看,相对于ERA5和GLDAS,FLDAS和CRA40/Land降水资料与观测值之间的差别最低,有着最优的统计特征。

本文引用格式

宋海清,朱仲元,李云鹏 . 陆面同化及再分析降水资料在内蒙古地区的适用性[J]. 干旱区研究, 2021 , 38(6) : 1624 -1636 . DOI: 10.13866/j.azr.2021.06.14

Abstract

Monthly precipitation data observed at 115 weather stations in Inner Mongolia during 1982-2018 was compared with land data assimilation and reanalysis precipitation datasets [Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), the Fifth Generation of ECMWF Reanalysis (ERA5), China Meteorological Administration, Global Land surface Reanalysis (CRA40/Land), and Global Land Data Assimilation System (GLDAS)]. The reliability of the four precipitation datasets was analyzed and accuracy of monthly variation was also evaluated. The results showed that the four precipitation datasets captured features of precipitation distribution. Precipitation levels decreased from the northeast to west and were lower in winter and higher in summer. By analyzing correlation coefficients, mean absolute errors, mean bias errors, root mean square errors, and Nash-Sutcliffe efficiency coefficients, the four precipitation datasets showed the best performance in summer and the worst performance in winter, and better values in eastern semi-humid and semiarid areas than western arid and extremely arid areas. Compared with ERA5 and GLDAS, FLDAS, and CRA40/Land showed the lowest differences from observed values and the best statistical characteristics. ERA5 data mostly overestimated precipitation in Inner Mongolia during most of the study period, while GLDAS underestimated it. Particularly in winter, GLDAS showed almost no observation capability for snow precipitation, while the newly released FLDAS data performed better. Overall, FLDAS and CRA40/Land precipitation datasets performed best with better statistical power.

参考文献

[1] Kidd C, Huffman G. Global precipitation measurement[J]. Meteorological Applications, 2011, 18(3):334-353.
[2] Chen Y, Sharma S, Zhou X, et al. Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya[J]. Atmospheric Research, 2020, 250:105365.
[3] 闫燕, 刘罡, 何军, 等. 重庆地区卫星及再分析降水资料评估[J]. 高原气象, 2020, 39(3):594-608.
[3] [ Yan Yan, Liu Gang, He Jun, et al. Assessment of satellite and reanalysis precipitation data in Chongqing[J]. Plateau Meteorology, 2020, 39(3):594-608. ]
[4] Tapiador F J, Turk F J, Petersen W, et al. Global precipitation measurement: Methods, datasets and applications[J]. Atmospheric Research, 2012,104-105:70-97.
[5] 汪君, 王会军, 洪阳. 一个新的高分辨率洪涝动力数值监测预报系统[J]. 科学通报, 2016, 61(增刊):518-528.
[5] [ Wang Jun, Wang Huijun, Hong Yang. A high-resolution flood forecasting and monitoring system for China using satellite remote sensing data[J]. Chinese Science Bulletin, 2016, 61(Suppl. ): 518-528. ]
[6] Jiang S, Ren L, Hong Y, et al. Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method[J]. Journal of Hydrology, 2012, 452-453:213-225.
[7] 王炳尧, 刘星辰, 刘立超. 1957—2017年腾格里沙漠地区降水量[J]. 中国沙漠, 2020, 40(4):163-170.
[7] [ Wang Bingyao, Liu Xingchen, Liu Lichao. Characteristics of precipitation in the surrounding area of Tengger Desert in 1957-2017[J]. Journal of Desert Research, 2020, 40(4):163-170. ]
[8] 王玉丹, 陈浩, 刘璨然, 等. ITPCAS和CMORPH两种遥感降水产品在陕西地区的适用性研究[J]. 干旱区研究, 2018, 35(3):579-588.
[8] [ Wang Yudan, Chen Hao, Liu Canran, et al. Applicability of ITPCAS and CMORPH precipitation datasets over Shaanxi province[J]. Arid Zone Research, 2018, 35(3):579-588. ]
[9] 刘田, 阳坤, 秦军, 等. 青藏高原中、东部气象站降水资料时间序列的构建与应用[J]. 高原气象, 2018, 37(6):1449-1457.
[9] [ Liu Tian, Yang Kun, Qin Jun, et al. Construction and applications of time series of monthly precipitation at weather stations in the central and eastern Qinghai-Tibetan plateau[J]. Plateau Meteorology, 2018, 37(6):1449-1457. ]
[10] 宇婧婧, 沈艳, 潘旸, 等. 概率密度匹配法对中国区域卫星降水资料的改进[J]. 应用气象学报, 2013, 24(5):544-553.
[10] [ Yu Jingjing, Shen Yan, Pan Yang, et al. Improvement of satellite-based precipitation estimates over China based on probability density function matching method[J]. Journal of Applied Meteorological Science, 2013, 24(5):544-553. ]
[11] 潘旸, 谷军霞, 宇婧婧, 等. 中国区域高分辨率多源降水观测产品的融合方法试验[J]. 气象学报, 2018, 76(5):755-766.
[11] [ Pan Yang, Gu Junxia, Yu Jingjing, et al. Test of merging methods for multi-source observed precipitation products at high resolution over China[J]. Acta Meteorologica Sinica, 2018, 76(5):755-766. ]
[12] Lu D, Yong B. Evaluation and hydrological utility of the latest GPM IMERG V5 and GSMaP V7 precipitation products over the Tibetan Plateau[J]. Remote Sensing, 2018, 10(12):2022.
[13] Sharma S, Chen Y, Zhou X, et al. Evaluation of GPM-Era satellite precipitation products on the southern slopes of the central Himalayas against rain gauge data[J]. Remote Sensing, 2020, 12(11):1836.
[14] Dinku T, Connor S J, Ceccato P. Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America: Satellite Rainfall Applications for Surface Hydrology[M]. Dordrecht: Springer Press, 2010.
[15] Funk C, Peterson P, Landsfeld M, et al. The climate hazards infrared precipitation with stations: A new environmental record for monitoring extremes[J]. Scientific Data, 2015, 2(1):1-21.
[16] Sahlu D, Moges S A, Nikolopoulos E I, et al. Evaluation of high-resolution multi-satellite and reanalysis rainfall products over East Africa[J]. Advances in Meteorology, 2017, 2017:1-14. https://doi.org/10.1155/2017/4957960.
[17] 王文, 汪小菊, 王鹏. GLDAS月降水数据在中国区的适用性评估[J]. 水科学进展, 2014, 25(6):769-778.
[17] [ Wang Wen, Wang Xiaoju, Wang Peng. Assessing the applicability of GLDAS monthly precipitation data in China[J]. Advances in Water Science, 2014, 25(6):769-778. ]
[18] Dee D P, Uppala S M, Simmons A J, et al. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[J]. Quarterly Journal of the Royal Meteorological Society, 2011, 137(656):553-597.
[19] Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis[J]. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730):1999-2049.
[20] Saha S, Moorthi S, Pan H L, et al. The NCEP climate forecast system reanalysis[J]. Bulletin of the American Meteorological Society, 2010, 91(8):1015-1058.
[21] 张蒙, 黄安宁, 计晓龙, 等. 卫星反演降水资料在青藏高原地区的适用性分析[J]. 高原气象, 2016, 35(1):34-42.
[21] [ Zhang Meng, Huang Anning, Ji Xiaolong, et al. Validation of satellite precipitation products over Qinghai-Xizang Plateau Region[J]. Plateau Meteorology, 2016, 35(1):34-42. ]
[22] Li Chunxiang, Zhao Tianbao, Shi Chunxiang, et al. Evaluation of daily precipitation product in China from the CMA global atmospheric interim reanalysis[J]. Journal of Meteorological Research, 2020, 34(1):117-136.
[23] Molod A, Takacs L, Suarez M, et al. Development of the GEOS-5 atmospheric general circulation model: Evolution from MERRA to MERRA2[J]. Geoscientific Model Development, 2015, 8(5):1339-1356.
[24] McNally A, Arsenault K, Kumar S, et al. A land data assimilation system for sub-Saharan Africa food and water security applications[J]. Scientific Data, 2017, 4(1):1-19.
[25] McNally A, Verdin K, Harrison L, et al. Acute Water-Scarcity monitoring for Africa[J]. Water, 2019, 11(10):1968.
[26] He J, Yang K, Tang W, et al. The first high-resolution meteorological forcing dataset for land process studies over China[J]. Scientific Data, 2020, 7(25):1-12.
[27] 刘川, 余晔, 解晋, 等. 多种土壤温湿度资料在青藏高原的适用性[J]. 高原气象, 2015, 34(3):653-665.
[27] [ Liu Chuan, Yu Ye, Xie Jin, et al. Applicability of soil temperature and moisture in several datasets over Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2015, 34(3):653-665. ]
[29] Smith R B, Barstad I. A linear theory of orographic precipitation[J]. Journal of Atmospheric Sciences, 2004, 61(12):1377-1391.
[29] Blocken B, Poesen J, Carmeliet J. Impact of wind on the spatial distribution of rain over micro-scale topography-numerical modelling and experimental verification[J]. Hydrological Processes, 2006, 20:345-368.
[30] 申露婷, 张方敏, 黄进, 等. 1961—2018 年内蒙古生长季昼夜降水气候特征[J]. 干旱区研究, 2020, 37(6):1519-1527.
[30] [ Shen Luting, Zhang Fangmin, Huang Jin, et al. Climate characteristics of day and night precipitation during the growing season in Inner Mongolia from 1961 to 2018[J]. Arid Zone Research, 2020, 37(6):1519-1527. ]
[31] Fallah A, Rakhshandehroo G R, Berg P, et al. Evaluation of precipitation datasets against local observations in southwestern Iran[J]. International Journal of Climatology, 2020, 40(9):4102-4116.
[32] Wang Y, Yang K, Zhou X, et al. Synergy of orographic drag parameterization and high resolution greatly reduces biases of WRF-simulated precipitation in central Himalaya[J]. Climate Dynamics, 2020, 54:1729-1740.
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