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遥感技术与应用  2023, Vol. 38 Issue (5): 1136-1147    DOI: 10.11873/j.issn.1004-0323.2023.5.1136
遥感应用     
基于MODIS数据的青藏高原遥感云量重构
周厚瑀1,2(),董庆1,3(),孟德利1,2,赵文博1,2,边民1,2
1.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094
2.中国科学院大学,北京 100049
3.中科卫星应用德清研究院 浙江省微波目标特性测量与遥感重点实验室,浙江 湖州 313200
Reconstruction of Remote Sensing Cloud Cover over Tibetan Plateau based on MODIS Data
Houyu ZHOU1,2(),Qing DONG1,3(),Deli MENG1,2,Wenbo ZHAO1,2,Min Bian1,2
1.Laboratory of Digital Earth Science,Aerospace Information Research Institute,CAS,Beijing 100094 China
2.University of Chinese Academy of Sciences,Beijing 100049,China
3.Key Laboratory of Target Microwave Properties of Zhejiang,Deqing Academy of Satellite Applications,Huzhou 313200,China
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摘要:

云是影响青藏高原能量平衡和地气过程的关键参量之一,研究高原的云量对探讨青藏高原的气候变化具有重要意义。采用2001~2020年的MODIS总云量数据与ERA5、CRA40两套再分析资料的总云量数据,以3~11月份MOD06云量资料为真值,评价不同再分析资料在青藏高原的适用性。利用改进自编码器模型,基于ERA5和MOD06重构了1950~2020年的3~11月的高原云量。结果表明:在高原地区,ERA5的云量值偏高,而CRA40云量值偏低,且ERA5与MOD06的相关性明显优于CRA40与MOD06的相关性;通过相关系数、偏差、平均绝对误差、均方根误差四种评价指标,发现改进自编码器模型在重构云量方面具有良好的效果,且能模拟出青藏高原云量的变化趋势,为研究青藏高原地区云量时空演变规律提供了可靠的长时序数据。

关键词: 青藏高原云量MODIS再分析资料数据重构自编码器    
Abstract:

The Tibetan Plateau (TP),with its unique climate characteristics and geographical pattern, plays an important role in global climate change. As an important part of the earth atmosphere system, cloud is key to affecting climate change. Cloud cover can more directly reflect the change of cloud. Therefore, it is of great significance to reconstruct a cloud cover product with longer time series and higher accuracy in the TP. In this paper, Considering the complex underlying surface types and geographical elevations in the TP, We select the cloud cover of MOD06, ERA5 and CRA40 from 2001 to 2020. We take the cloud cover of MOD06 from March to November as the true value and evaluate the applicability of the two reanalysis data in the TP through methods such as climate tendency rate and correlation coefficient.Based on ERA5 and MOD06, the improved auto-encoder model is used to reconstruct the cloud cover of the plateau from March to November of 1950 to 2020. The results show that the cloud cover of ERA5 is higher than that of MOD06, while that of CRA40 is lower than that of MOD06, and the correlation between ERA5 and MOD06 is obviously better than that between CRA40 and MOD06;The improved auto-encoder model evaluated by four evaluation indicators of correlation coefficient(R), bias, Mean Absolute Error(MAE) and Root Mean Square Error(RMSE) has a good effect on cloud amount reconstruction. The correlation coefficient between cloud amount reconstructed by the improved autoencoder model and MOD06 cloud amount data increases by more than 20% on average from March to November, and can simulate the change trend of cloud amount over the TP. The results provide reliable long time series data for studying the temporal and spatial evolution of cloud cover over the TP.

Key words: Tibetan Plateau (TP)    Cloud over    MODIS    Reanalysis data    Data reconstruction    Auto-encoder
收稿日期: 2022-06-30 出版日期: 2023-11-07
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目“基于遥感数据的印太暖池区近30a海洋环境参数变化研究”(41876210)
通讯作者: 董庆     E-mail: zhouhouyu20@mails.ucas.ac.cn;dongqing@aircas.ac.cn
作者简介: 周厚瑀(1999-),男,江苏宿迁人,硕士研究生,主要从事气象遥感研究。E?mail: zhouhouyu20@mails.ucas.ac.cn
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引用本文:

周厚瑀,董庆,孟德利,赵文博,边民. 基于MODIS数据的青藏高原遥感云量重构[J]. 遥感技术与应用, 2023, 38(5): 1136-1147.

Houyu ZHOU,Qing DONG,Deli MENG,Wenbo ZHAO,Min Bian. Reconstruction of Remote Sensing Cloud Cover over Tibetan Plateau based on MODIS Data. Remote Sensing Technology and Application, 2023, 38(5): 1136-1147.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.5.1136        http://www.rsta.ac.cn/CN/Y2023/V38/I5/1136

图1  研究区位置与地形
图2  技术路线图
数据集数据来源分辨率时间长度方法
MODISNASA1 km1999年~至今卫星反演
ERA5ECMWF0.125°1950年~至今4Dvar
CRA40CMA0.50°1979年~至今3Dvar
ASTER_GDEMNASA30 m————
表1  所采用的各数据集的基本参数简介
图3  自编码器原理图
图4  改进自编码器原理图
图5  3种资料2001~2020年青藏高原总体云量年际及月际变化(%)(图(a)中虚线为线性拟合线,y1、y2、y3分别代表ERA5、MOD06、CRA40云量,x代表时间)
34567891011
CRA400.510.520.490.630.730.700.700.670.59
ERA50.610.670.680.820.820.800.850.770.64
表2  MOD06与CRA40、ERA5的1-12月份的相关系数
图6  2011~2020年3~11月份MOD06、IMAE-CF云量时间序列(图中虚线为线性拟合线,y1、y2分别代表MOD06、IMAE-CF云量,x代表时间)
图7  2011~2020年3~11月份MOD06与IMAE-CF密度散点图(图中黑线为对角线)
图8  IMAE-CF、ERA5与MOD06的Bias、MAE、RMSE变化图
图9  2011~2020年ERA5、MOD06、IMAE-CF春、夏、秋云量气候态
图10  2011~2020年春、夏、秋季MOD06与IMAE-CF密度散点图(图中黑线为对角线)

检验

指标

ERA5&MOD06IMAE-CF&MOD06
春季夏季秋季春季夏季秋季
Bias6.509.387.320.65-2.501.08
MAE9.6210.369.312.973.012.24
RMSE11.7812.8711.383.613.652,93
表3  2011~2020年ERA5、IMAE-CF与MOD06春夏秋三季云量气候态各项评价指标(%)
图11  1950~2020年3~11月份ERA5与IMAE-CF的时间序列,叠加2001-2020年3~11月份MOD06时间序列(每两年显示一次)
图12  1950~2020年ERA5与IMAE-CF春、夏、秋云量空间分布图
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