天气与气候

基于FY-4A QPE的中亚五国降水时空分布特征

  • 陈爱军 ,
  • 张寅 ,
  • 楚志刚
展开
  • 1.南京信息工程大学,气象灾害教育部重点实验室,江苏 南京 210044
    2.南京信息工程大学,中国气象局气溶胶与云降水重点开放实验室,江苏 南京 210044
    3.南京信息工程大学,大气物理学院,江苏 南京 210044
陈爱军(1972-),男,副教授,主要从事气象卫星资料处理与应用研究. E-mail: chenaijun@nuist.edu.cn

收稿日期: 2023-02-28

  修回日期: 2023-04-18

  网络出版日期: 2023-09-28

基金资助

国家自然科学基金面上项目(41871238)

Spatiotemporal distribution of precipitation in five Central Asian countries based on FY-4A quantitative precipitation estimates

  • Aijun CHEN ,
  • Yin
Expand
  • 1. Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
    2. China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China
    3. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, Jiangsu, China

Received date: 2023-02-28

  Revised date: 2023-04-18

  Online published: 2023-09-28

摘要

FY-4A定量降水估计产品(Quantitative Precipitation Estimation, QPE)为深入研究中亚五国降水的时空分布特征提供了数据源。本文首先采用全球降水观测(Global Precipitation Measurement, GPM)多星集成降水终级产品IMERG-F(Integrated Multi-satellite Retrievals for GPM Final run)评估FY-4A QPE,然后利用FY-4A QPE分析中亚五国的降水特点及时空分布特征,结果表明:(1) FY-4A QPE能够精细地反映中亚五国降水的空间分布差异,降水估计结果比较合理且与IMERG-F的时序变化具有较好的一致性。(2) 中亚五国年平均降水量的空间分布差异大,且与海拔高度有关,高海拔地区的年平均降水量超过500 mm,但面积占比不足10%;低海拔地区的年平均降水量不足350 mm,但面积占比却超过90%。(3) 中亚五国降水的空间分布有明显的季节性,夏季降水范围最广,平均降水量超过50 mm;秋季平均降水量最小,绝大部分地区平均降水量不足40 mm。吉尔吉斯斯坦和塔吉克斯坦四季降水相对充足,部分区域季节平均降水量超过480 mm;哈萨克斯坦中西部、乌兹别克斯坦中西部和土库曼斯坦北部季节平均降水量不足40 mm。(4) 根据月平均降水量超过40 mm区域的聚集度,中亚五国月平均降水的空间分布可以大致分为点状离散分布型、干旱型、半干半湿型和三明治型四种分布形态。(5) 中亚五国夏季降水多发区的逐小时平均降水量具有“准三小时”周期性日变化特征,午后至前半夜是降水多发时段之一,降水类型以小雨为主,其次是少量的中雨。

本文引用格式

陈爱军 , 张寅 , 楚志刚 . 基于FY-4A QPE的中亚五国降水时空分布特征[J]. 干旱区研究, 2023 , 40(9) : 1369 -1381 . DOI: 10.13866/j.azr.2023.09.01

Abstract

The FY-4A Quantitative Precipitation Estimation (QPE) product is crucial for comprehensive research on precipitation patterns and spatiotemporal distribution across Central Asian (CA) countries. In this study, FY-4A QPE data quality was evaluated using the Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final run (IMERG-F), and the precipitation characteristics and spatiotemporal distribution over five CA countries were subsequently examined. The main findings were as follows. (1) FY-4A QPE accurately reflected precipitation spatial disparities across the CA countries, aligning well with the temporal changes of IMERG-F. (2) Annual average precipitation (AAP) exhibited substantial spatial variation over the CA countries in relation to altitude. High-altitude regions exceeded 500 mm AAP, encompassing <10% of the area, whereas low-altitude areas experienced <350 mm AAP, accounting for >90% of the region. (3) Precipitation distribution exhibited pronounced seasonality across the five CA countries. Summer exhibited the widest precipitation range, averaging >50 mm. Conversely, the autumn average, typically <40 mm, was the lowest. Kyrgyzstan and Tajikistan experienced sufficient precipitation year-round, with some areas showing an average >480 mm. However, central and western Kazakhstan, Uzbekistan, and northern Turkmenistan received <40 mm. (4) According to clustering of areas with a monthly average precipitation exceeding 40 mm, the five CA countries were classified into four spatial distribution types: point discrete, drought, semi-dry and semi-wet, and sandwich. (5) In summer across the five CA countries, areas with elevated precipitation density displayed a near 3-hour cyclic daily variation. Notably, one of these periods occurred from noon to the first half of the night. Furthermore, the predominant precipitation type was light rain, with a minor occurrence of moderate rain.

参考文献

[1] 吴玥葶, 郭利丹, 井沛然, 等. 中亚五国水-能源-粮食-生态耦合关系及时空分异[J]. 干旱区研究, 2023, 40(4): 573-582.
[1] [Wu Yueting, Guo Lidan, Jing Peiran, et al. Coupling relationship and spatiotemporal differentiation of the water-energy-food-ecology nexus in five Central Asian countries[J]. Arid Zone Research, 2023, 40(4): 573-582.]
[2] 谢志轩, 肖天贵, 叶如辉, 等. “一带一路”陆路地区自然灾害时空分布特征[J]. 成都信息工程大学学报, 2022, 37(1): 111-118.
[2] [Xie Zhixuan, Xiao Tiangui, Ye Ruhui, et al. Temporal and spatial distribution characteristics of natural disasters in the land area of the “Belt and Road”[J]. Journal of Chengdu University of Information Technology, 2022, 37(1): 111-118.]
[3] 王会军, 唐国利, 陈海山, 等. “一带一路” 区域气候变化事实、影响及可能风险[J]. 大气科学学报, 2020, 43(1): 1-9.
[3] [Wang Huijun, Tang Guoli, Chen Haishan, et al. The Belt and Road region climate change: Facts, impacts and possible risks[J]. Transactions of Atmospheric Sciences, 2020, 43(1): 1-9.]
[4] 周高洁, 赵勇, 姚俊强, 等. 气候变化背景下中亚干旱区大气水分循环要素时空演变[J]. 干旱区研究, 2022, 39(5): 1371-1384.
[4] [Zhou Gaojie, Zhao Yong, Yao Junqiang, et al. Spatiotemporal evolution of atmospheric water cycle factors in arid regions of Central Asia under climate change[J]. Arid Zone Research, 2022, 39(5): 1371-1384.]
[5] Wang J S, Chen F H, Jin L Y, et al. Characteristics of the dry/wet trend over arid Central Asia over the past 100 years[J]. Climate Research, 2010, 41(1): 51-59.
[6] 闫昕旸, 张强, 张文波, 等. 泛中亚干旱区气候变化特征分析[J]. 干旱区研究, 2021, 38(1): 1-11.
[6] [Yan Xinyang, Zhang Qiang, Zhang Wenbo, et al. Analysis of climate characteristics in the Pan-Central-Asia arid region[J]. Arid Zone Research, 2021, 38(1): 1-11.]
[7] 陈发虎, 黄伟, 靳立亚, 等. 全球变暖背景下中亚干旱区降水变化特征及其空间差异[J]. 中国科学:地球科学, 2011, 41(11): 1647-1657.
[7] [Chen Fahu, Huang Wei, Jin Liya, et al. Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming[J]. Science China Earth Science, 2011, 41(11): 1647-1657.]
[8] 迪丽努尔·托列吾别克,李栋梁. 近115 a中亚干湿气候变化研究[J]. 干旱气象, 2018, 36(2):185-195.
[8] [Dilinuer Tuoliewubieke, Li Dongliang. Characteristics of the dry/wet climate change in Central Asia in recent 115 years[J]. Journal of Arid Meteorology, 2018, 36(2): 185-195. ]
[9] Hu Z Y, Zhou Q M, Chen X, et al. Variations and changes of annual precipitation in Central Asia over the last century[J]. International Journal of Climatology, 2017, 37(S1): 157-170.
[10] 李如琦, 唐冶, 阿布力米提江·阿不力克木, 等. 中亚五国暴雨分布及其环流特征[J]. 沙漠与绿洲气象, 2019, 13(1): 1-7.
[10] [Li Ruqi, Tang Ye, Ablimitjan Ablikim, et al. Characteristics of rainstorm and its circulation in five countries of Central Asian[J]. Desert and Oasis Meteorology, 2019, 13(1): 1-7.]
[11] Yang T, Li Q, Chen X, et al. Spatiotemporal variability of the precipitation concentration and diversity in Central Asia[J]. Atmospheric Research, 2020, 241(35): 104954.
[12] 徐利岗, 杜历, 姚海娇, 等. 中亚干旱区降水时空变化特征及趋势分析[J]. 干旱区资源与环境, 2015, 29(11): 121-127.
[12] [Xu Ligang, Du Li, Yao Haijiao, et al. Spatiotemporal variations and tendency of annual precipitation in the arid Central Asia[J]. Journal of Arid Land Resources and Environment, 2015, 29(11): 121-127.]
[13] Zhang M, Chen Y N, Shen Y J, et al. Changes of precipitation extremes in arid Central Asia[J]. Quaternary International, 2017, 436(29): 16-27.
[14] Zhang M, Chen Y N, Shen Y J, et al. Tracking climate change in Central Asia through temperature and precipitation extremes[J]. Journal of Geographical Sciences, 2019, 29(1): 3-28.
[15] Yu Y, Chen X, Disse M, et al. Climate change in Central Asia: Sino-German cooperative research findings[J]. Science Bulletin, 2020, 65(9): 689-692.
[16] Sorooshian S, Hsu K L, Gao X G, et al. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall[J]. Bulletin of the American Meteorological Society, 2000, 81(9): 2035-2046.
[17] Joyce R J, Janowiak J E, Arkin P A, et al. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution[J]. Journal of Hydrometeorology, 2004, 5(3): 487-503.
[18] Huffman G J, Bolvin D T, Nelkin E J, et al. The TRMM multi-satellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales[J]. Journal of Hydrometeorology, 2007, 8(1): 38-55.
[19] Huffman G J, Bolvin D T. Algorithm Theoretical Basis Document, Version 4.1: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)[EB/OL]. NASA/GSFC: Greenbelt, MD, USA, 2013.
[20] Hong Z K, Han Z Y, Li X Y, et al. Generation of an improved precipitation dataset from multisource information over the Tibetan Plateau[J]. Journal of Hydrometeorology, 2021, 22(5): 1275-1295.
[21] Thies B, Bendix J. Satellite based remote sensing of weather and climate: Recent achievements and future perspectives[J]. Meteorological Applications, 2011, 18(3): 262-295.
[22] Kidd C, Levizzani V. Status of satellite precipitation retrievals[J]. Hydrology and Earth System Sciences, 2011, 15(15): 1109-1116.
[23] Yuan F, Wang B, Shi C X, et al. Evaluation of hydrological utility of IMERG Final run V05 and TMPA 3B42V7 satellite precipitation products in the Yellow River source region, China[J]. Journal of Hydrology, 2018, 567(56): 696-711.
[24] Min M, Wu C Q, Li C, et al. Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series[J]. Journal of Meteorological Research, 2017, 31(4): 708-719.
[25] Yang J, Zhang Z Q, Wei C Y, et al. Introducing the new generation of Chinese geostationary weather satellites, Feng Yun-4[J]. Bulletin of the American Meteorological Society, 2017, 98(8): 1637-1658.
[26] 游然. 卫星定量降水估计方法[C] // 合肥:第35届中国气象学会年会, 2018.
[26] [You Ran. Satellite quantitative precipitation estimation method[C] // Hefei:The 35th Annual Conference of the Chinese Meteorological Society, 2018.]
[27] 钟宇璐. 风云四号卫星定量降水估计产品的检验评估[J]. 农业灾害研究, 2021, 11(3): 96-98.
[27] [Zhong Yulu. Evaluation and verification of FY-4A satellite quantitative precipitation estimation product[J]. Journal of Agricultural Catastropholgy, 2021, 11(3): 96-98.]
[28] 陈爱军, 孔宇, 陆大春. 应用CGDPA评估中国大陆地区IMERG的降水估计精度[J]. 大气科学学报, 2018, 41(6): 797-806.
[28] [Chen Aijun, Kong Yu, Lu Dachun. Evaluation of the precipitation estimation accuracy of IMERG over mainland China with CGDPA[J]. Transactions of Atmospheric Sciences, 2018, 41(6): 797-806.]
[29] 陈爱军, 吴雪菲, 楚志刚. 精细化评估GPM/IMERG产品对台风“妮坦”降水的观测精度[J]. 气象科学, 2021, 41(5): 678-686.
[29] [Chen Aijun, Wu Xuefei, Chu Zhigang. Refined evaluation of the accuracy of GPM/IMERG in the precipitation process of typhoon Nida[J]. Journal of the Meteorological Sciences, 2021, 41(5): 678-686.]
[30] 吴雪菲, 陈爱军, 余安安, 等. 双偏振雷达评估IMERG对不同类型降水的观测精度[J]. 气象科技, 2022, 50(4): 476-484.
[30] [Wu Xuefei, Chen Aijun, Yu An’an, et al. Using dual-polarization radar to evaluate accuracy of GPM/IMERG in different types of precipitation process[J]. Meteorological Science and Technology, 2022, 50(4): 476-484.]
[31] Arshad M, Ma X Y, Yin J, et al. Evaluation of GPM-IMERG and TRMM-3B42 precipitation products over Pakistan[J]. Atmospheric Research, 2021, 249(36): 105341.
[32] Hosseini-Moghari S M, Tang Q H. Validation of GPM IMERG V05 and V06 precipitation products over Iran[J]. Journal of Hydrometeorology, 2020, 21(5): 1011-1037.
[33] Wang N, Liu W B, Sun F, et al. Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China[J]. Atmospheric Research, 2020, 234(35): 104746.
[34] 国家气象中心. 降水量等级(GB/T 28592-2012)[S]. 北京: 中国标准出版社, 2012.
[34] [National Meteorological Centre. Grade of precipitation (GB/T 28592-2012)[S]. Beijing: Standard Press of China, 2012. ]
文章导航

/