A Flux Footprint Model for Large Aperture Scintillometer

Acta Scientiarum Naturalium Universitatis Pekinensis

Previous Articles Next Articles

A Flux Footprint Model for Large Aperture Scintillometer

PENG Guliang¹,CAI Xuhui^1,3,LIU Shaomin²

1 Department of Environment Sciences, Peking University, Beijing, 100871; 2 State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875; 3 Corresponding Author, E-mail: xhcai@pku.edu.cn

Received:2007-01-04 Online:2007-11-20 Published:2007-11-20

大孔径闪烁仪湍流通量印痕模型的建立与应用

彭谷亮¹,蔡旭晖^1,3,刘绍民²

¹北京大学环境科学系，北京，100871；²北京师范大学国家遥感科学重点实验室，地理学与遥感科学学院，北京，100875；³通讯作者，E-mail：xhcai@pku.edu.cn

Abstract

Abstract: The large aperture scintillometer (LAS) is a versatile instrument to measure area-average heat fluxes over heterogeneous surface. In order to analyze the source area of LAS data, a new Eulerian analytical footprint model is developed. The parameters of the LAS footprint are discussed in detail and some outcome of the model application over a heterogeneous field experiment are shown. The good agreement of the results demonstrates that this footprint model is an effective and practical tool for complex surface study.

Key words: footprint analysis, LAS, area-average heat flux, spatial representative, heterogeneous

摘要： 针对近年兴起的适用于大范围区域平均通量观测的大孔径闪烁仪 (LAS)，建立了一个基于欧拉分析方法的LAS印痕模型，用于对LAS测量数据的印痕分析和质量控制。作者介绍模式的建立方法并对模式的输入参数进行了讨论。另外，应用LAS印痕模式来分析2005年小汤山非均匀下垫面的观测实验数据，并与涡动相关数据进行线性回归分析，获得较好的相关性。结果显示该 LAS印痕模型在观测数据的分析中有很大的实用价值，适用于非均匀下垫面的研究。

关键词: 印痕分析, 大孔径闪烁仪(LAS), 区域平均通量, 空间代表性, 非均匀

CLC Number:

P 404

PENG Guliang,CAI Xuhui,LIU Shaomin. A Flux Footprint Model for Large Aperture Scintillometer[J]. Acta Scientiarum Naturalium Universitatis Pekinensis.

彭谷亮,蔡旭晖,刘绍民. 大孔径闪烁仪湍流通量印痕模型的建立与应用[J]. 北京大学学报（自然科学版）.

Add to citation manager EndNote|Ris|BibTeX

URL: https://xbna.pku.edu.cn/EN/

https://xbna.pku.edu.cn/EN/Y2007/V43/I6/822

[1]	LI Ruifan, WEI Zhiyu, FAN Yuantao, YE Shuqin, ZHANG Guangwei. Enhanced Prompt Learning for Few-shot Text Classification Method [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2024, 60(1): 1-12.
[2]	PENG Ke, LIU Yuexi, CHEN Liang, LU Xinpei. Study on Nitrogen Fixation Characteristics of Glow Plasma in Series under Fixed Magnetic Field [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(6): 915-922.
[3]	LIU Xiangcheng, CAO Jian, YAO Hongyi, XU Pengtao, ZHANG Yuan, WANG Yuan. AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(5): 764-772.
[4]	KONG Xiangfu, DONG Bo, XU Ke, TAO Yongliang. Text Classification Model for Livelihood Issues Based on BERT: A Study Based on Hotline Compliant Data of Zhejiang Province [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(3): 456-466.
[5]	JIANG Yanting. English Books Automatic Classification According to CLC [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(1): 11-20.
[6]	LI Zicheng, CHANG Xiaoqin, LI Yameng, LI Shoushan, ZHOU Guodong. A Joint Learning Approach to Few-Shot Learning for Multi-category Sentiment Classification [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2023, 59(1): 57-64.
[7]	ZHANG Tongzhou, XU Yi, GAO Baowei, ZHANG Jiasen. Second Harmonic Generation of Surface Plasmon Polaritons in a Plasmonic Waveguide [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(6): 999-1006.
[8]	LI Fang, CAO Jian, LI Pu, XIE Hao, ZHAO Xiongbo, WANG Yuan, ZHANG Xing. Design and Implementation of Object Detection Acceleration Module Based on an ARM+FPGA Heterogeneous Platform [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(6): 1035-1041.
[9]	DAI Linlin, YU Xuan, LÜ Jinmei. Research and Practice of Village Classification Facing the Demand of Spatial Management and Control: A Case Study of Wuqing District, Tianjin [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(6): 1121-1129.
[10]	XU Pengtao, CAO Jian, SUN Wenyu, LI Pu, WANG Yuan, ZHANG Xing. Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(5): 801-807.
[11]	XU Pengtao, CAO Jian, CHEN Weiqian, LIU Shengrong, WANG Yuan, ZHANG Xing. Post Training Quantization Preprocessing Method of Convolutional Neural Network via Outlier Removal [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(5): 808-812.
[12]	ZHANG Yulin, SHANG Ke, ZHANG Feizhou. Application of Digital Twin Technology to Heterogeneous Traffic Flow Security [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(5): 888-896.
[13]	ZHOU Danqing, LI Dongyu, CHEN Yi, LI Yue, YANG Tong, CHENG Hao, WU Minjian, LI Yuze, YAN Yang, XIA Yadong, LIN Chen, YAN Xueqing, ZHAO Ziqiang. Study on the Irradiation Characteristics of Laser-Accelerated Proton Beam on SiC [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(3): 405-411.
[14]	SANG Yueyang, CHU Yiqi, LIU Zhe, REN Jingjing, TIAN Xiaoqing, WANG Qixi, LI Chengcai. Research on Relations between Atmospheric Mixing Layer Heights and Fine Particle Concentrations with Lidar Measurements [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(3): 412-420.
[15]	LIAO Yiming, OUYANG Chunping, LIU Yongbin, HU Fuyu. Drug-Target Interactions Prediction Based on Meta-path of Heterogeneous Information Network [J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(1): 37-44.

A Flux Footprint Model for Large Aperture Scintillometer

大孔径闪烁仪湍流通量印痕模型的建立与应用

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics