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基于EC观测估算最小冠层阻力分布及其在潜热通量插补中的应用

刘斌 谢正辉 刘双 李锐超

刘斌, 谢正辉, 刘双, 李锐超. 基于EC观测估算最小冠层阻力分布及其在潜热通量插补中的应用[J]. 大气科学, 2018, 42(6): 1235-1244. doi: 10.3878/j.issn.1006-9895.1711.17179
引用本文: 刘斌, 谢正辉, 刘双, 李锐超. 基于EC观测估算最小冠层阻力分布及其在潜热通量插补中的应用[J]. 大气科学, 2018, 42(6): 1235-1244. doi: 10.3878/j.issn.1006-9895.1711.17179
Bin LIU, Zhenghui XIE, Shuang LIU, Ruichao LI. Estimation of Minimum Canopy Resistance by EC Data and Its Application in the Interpolation of Latent Heat Flux[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1235-1244. doi: 10.3878/j.issn.1006-9895.1711.17179
Citation: Bin LIU, Zhenghui XIE, Shuang LIU, Ruichao LI. Estimation of Minimum Canopy Resistance by EC Data and Its Application in the Interpolation of Latent Heat Flux[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(6): 1235-1244. doi: 10.3878/j.issn.1006-9895.1711.17179

基于EC观测估算最小冠层阻力分布及其在潜热通量插补中的应用

doi: 10.3878/j.issn.1006-9895.1711.17179
基金项目: 

国家自然科学基金项目 41575096

国家自然科学基金项目 91125016

中国科学院前沿科学重点研究计划 QYZDY-SSW-DQC012

详细信息
    作者简介:

    刘斌, 男, 1990年出生, 博士研究生, 主要从事陆面过程研究。E-mail:liubin@mail.iap.ac.cn

    通讯作者:

    谢正辉, E-mail:zxie@lasg.iap.ac.cn

  • 中图分类号: P413

Estimation of Minimum Canopy Resistance by EC Data and Its Application in the Interpolation of Latent Heat Flux

Funds: 

National Natural Science Foundation of China 41575096

National Natural Science Foundation of China 91125016

Key Research Program of Frontier Sciences, Chinese Academy of Sciences QYZDY-SSW-DQC012

  • 摘要: 准确估计水热通量对于认识和理解地气交换与水循环变化过程具有重要意义。利用Penman-Monteith(P-M)模型计算季节尺度水热通量变化的不确定性很大程度上依赖于与冠层变化相关的最小冠层阻力参数,但模型中通常将其设为定值。为此,本文基于多年通量观测采用分段与整体相结合的迭代算法拟合出最小冠层阻力的季节分布。以湖南省宁乡通量观测站为例,针对2012~2015年观测拟合计算最小冠层阻力的季节分布曲线,并利用2016年通量数据进行独立数据验证。结果表明:最小冠层阻力曲线具有鲜明夏低冬高的季节变化特征;利用拟合的具有季节分布的最小冠层阻力改进潜热通量计算,独立数据验证表明其该方法的合理性;相比于原阻力方案得出的潜热模拟结果,其在相关系数、均方根误差和一致性指数都有改进;此外,将该估计方法应用于水热通量的数据插补,较常规统计插补方法,其插补稳定性不随连续缺失数据的增加而降低,而且还能通过模型的微分误差分析量化由于数据输入带来的插补不确定性,在保持通量数据完整性的同时也为数据应用场景提供科学依据。
  • 图  1  潜热通量估计流程图

    Figure  1.  The flow chart of latent heat flux estimation

    图  2  最小冠层阻力分布迭代流程图

    Figure  2.  The flow chart of iterative method of the minimum canopy resistance

    图  3  2012~2016年叶面积指数变化图

    Figure  3.  Leaf area index variation from 2012 to 2016

    图  4  2012年8月至2016年6月潜热通量数据质量比例的日均分布

    Figure  4.  Average daily distribution of the quality percentage of latent heat flux from August 2012 to June 2016

    图  5  年均最小冠层阻力分布。虚线为年均最小冠层阻力,阴影区域为年均阻力标准差限范围,实线为拟合曲线

    Figure  5.  Annual variation of the minimum canopy resistance. The dotted line is for annual minimum canopy resistance, the shaded region is for standard deviation of the minimum canopy resistance, the solid line indicates the fitting curve

    图  6  改进前后潜热通量实测与模拟值比较:(a)原始模拟与实测1:1线;(b)改进后模拟值与实测1:1线。红线为所有模拟值的回归线,蓝线为1:1线;黑点为2012~2015年模拟点,蓝点为2016年数据验证点;whole、day、night分别表示全天、白天和夜间

    Figure  6.  Latent heat flux comparison between measurements and simulations: (a) 1:1 line diagram between raw simulated data and measured data; (b) 1:1 line diagram between corrected simulated data and measured data. The red line is for the regression line of all data, the blue line is for 1:1 line; black dots indicate simulated data 2012–2015 and blue dots indicate data verified in 2016; whole, day, night each represent the wole day, the daytime and nighttime

    图  7  2012~2016年潜热通量实测与模拟改进前后日变化的季节平均分布图

    Figure  7.  Seasonally averaged daily variation diagrams of measured and simulated latent heat fluxes from 2012 to 2016

    图  8  各插补方法时序比较

    Figure  8.  Comparison between different interpolation methods

    图  9  单点插补效果的泰勒图。AE分别代表实测、本文模拟方案、非线性插值法、日变化趋势插补法和查表法

    Figure  9.  Taylor diagram of single-point data interpolation. AE represent measured data, method in this paper, nonlinear interpolate method, daily variation interpolation method and lookup method, respectively

    图  10  均方根误差(RMSE)、相关系数(R)和一致性系数(IA)随缺失数据的变化趋势

    Figure  10.  Trends of RMSE (Root Mean Square Error), R (Correlation coefficient) and IA (Index of Agreement) with the increasing of missing data

    表  1  参数初值及限制区间

    Table  1.   Initial parameters and their limitation ranges

    参数 初值 限制区间 参数拟合值
    a1 500 [0, 1000] 42.67
    a2 0.1 [0, 1] 1.00
    a3 0.1 [0, 1] 0.99
    a4 0.1 [0, 3] 1.31
    下载: 导出CSV

    表  2  改进前后模拟与实测的统计量

    Table  2.   Statistics of simulations and measurements

    相关系数(R 均方根误差(RMSE) 一致性指数(IA)
    全天 全天 全天
    2012~2015年 改进前 0.81 0.78 -0.13 62.01 76.00 31.26 0.89 0.86 -0.3
    改进后 0.86 0.83 0.1 58.46 71.80 28.65 0.91 0.89 0.26
    2016年 改进前 0.77 0.76 -0.07 75.58 89.25 46.14 0.89 0.83 0.09
    改进后 0.84 0.77 0.20 70.22 88.93 17.99 0.90 0.83 0.17
    下载: 导出CSV

    表  3  插补方案描述

    Table  3.   Description of different interpolation methods

    插补方法 方案描述
    非线性差值法 采用包含缺失数据在内的相邻11个数据点3次样条差值,插补缺失数据
    日变化趋势插补法 基于单日数据,采用7次线性拟合通量日变化特征,缺失数据采用拟合值替代
    查表法 采用辐射与温度相近原则,从已知数据中遴选最相近的数据以替代缺失数据
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-13
  • 网络出版日期:  2017-11-16
  • 刊出日期:  2018-11-15

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