干旱区研究

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2025 , Vol. 42 >Issue 8: 1379 - 1383

DOI: https://doi.org/10.13866/j.azr.2025.08.03

天气与气候

CLDAS和GPM降水数据产品在青海省的适用性评估

  • 申燕玲 ,
  • 曹晓敏 ,
  • 马元仓 ,
  • 王振海
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  • 1.青海省气象科学研究所， 青海 西宁 810001
    2.青海省防灾减灾重点实验室， 青海 西宁 810001
    3.高原与盆地暴雨旱涝灾害四川省重点实验室， 四川 成都 610213
    4.青海省气象局， 青海 西宁 810001
申燕玲（1992-），女，硕士研究生，主要从事数值天气预报、模式检验及高原天气系统机理相关研究. E-mail: shenpika@163.com
曹晓敏. E-mail: qxtcxm@163.com

收稿日期: 2025-03-21

  修回日期: 2025-06-18

  网络出版日期: 2025-11-24

基金资助

青海省科技成果转化专项项目(2023-SF-111);青海省气象局2024年重点科研项目(QXZD2024-02);高原与盆地暴雨旱涝灾害四川省重点实验室开放基金项目(SZKT202211);国家自然科学基金项目(62162053)

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Assessment of the applicability of CLDAS and GPM precipitation data for precipitation in Qinghai Province

  • SHEN Yanling ,
  • CAO Xiaomin ,
  • MA Yuancang ,
  • WANG Zhenhai
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  • 1. Qinghai Institute of Meteorological Science, Xining 810001, Qinghai, China
    2. Qinghai Key Laboratory of Disaster Prevention and Mitigation, Xining 810001, Qinghai, China
    3. Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610213, Sichuan, China
    4. Qinghai Meteorological Bureau, Xining 810001, Qinghai, China

Received date: 2025-03-21

  Revised date: 2025-06-18

  Online published: 2025-11-24

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摘要

基于2005—2021年青海省夏季逐小时站点观测数据，从不同时间尺度、日变化、海拔等多角度对中国气象局陆面数据同化系统（China Meteorological Administration Land Data Assimilation System，CLDAS）与全球降水测量数据（Global Precipitation Measurement，GPM）进行准确性评估，结果表明：CLDAS总体优于GPM。CLDAS与GPM降水产品均倾向于高估弱降水的降水量和降水频率，低估强降水的降水量和降水频率。CLDAS对降水量、降水频率日变化特征及不同海拔的评估准确性表现均优于GPM，但在湖泊群附近存在异常大值。随着海拔升高，CLDAS与GPM的降水量和降水频率与海拔的相关性均逐渐增强，且CLDAS的相关性上升趋势更为显著。GPM则表现出高估低海拔地区的降水量和降水频率，低估高海拔地区的降水量和降水频率的特点。

关键词： CLDAS; GPM; 降水评估; 海拔影响; 青海

本文引用格式

申燕玲 , 曹晓敏 , 马元仓 , 王振海 . CLDAS和GPM降水数据产品在青海省的适用性评估[J]. 干旱区研究, 2025 , 42(8) : 1379 -1383 . DOI: 10.13866/j.azr.2025.08.03

Abstract

Based on summer hourly station observations in Qinghai from 2005 to 2021, this study evaluated the accuracy of China Meteorological Administration Land Data Assimilation System (CLDAS) and Global Precipitation Measurement (GPM) precipitation data across multiple dimensions, including temporal scales, diurnal variation, and elevation impacts. The results showed that: CLDAS outperformed GPM in overall accuracy. Both systems tended to overestimate the amount and frequency of light precipitation but underestimate these metrics for heavy precipitation. CLDAS better captured diurnal variations in precipitation amount/frequency and elevation-dependent patterns than GPM. However, CLDAS exhibited abnormally high values near major lakes. Correlations with elevation strengthened progressively for both products with increasing altitude, with CLDAS exhibiting a more significant trend in correlation enhancement. GPM overestimated the amount and frequency of precipitation at low elevations but underestimated these metrics at high elevations.

Key words： CLDAS; GPM; precipitation evaluation; altitude impact; Qinghai

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