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Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data
Tang, WJ (Tang, Wenjun)1,2; Qin, J (Qin, Jun)1; Yang, K (Yang, Kun)1,2; Liu, SM (Liu, Shaomin)3; Lu, N (Lu, Ning)4; Niu, XL (Niu, Xiaolei)1; Tang, WJ
Source PublicationATMOSPHERIC CHEMISTRY AND PHYSICS
2016
Volume16Issue:4Pages:2543-2557
DOI10.5194/acp-16-2543-2016
AbstractCloud parameters (cloud mask, effective particle radius, and liquid/ice water path) are the important inputs in estimating surface solar radiation (SSR). These parameters can be derived from MODIS with high accuracy, but their temporal resolution is too low to obtain high-temporal-resolution SSR retrievals. In order to obtain hourly cloud parameters, an artificial neural network (ANN) is applied in this study to directly construct a functional relationship between MODIS cloud products and Multifunctional Transport Satellite (MTSAT) geostationary satellite signals. In addition, an efficient parameterization model for SSR retrieval is introduced and, when driven with MODIS atmospheric and land products, its root mean square error (RMSE) is about 100Wm(-2) for 44 Baseline Surface Radiation Network (BSRN) stations. Once the estimated cloud parameters and other information (such as aerosol, precipitable water, ozone) are input to the model, we can derive SSR at high spatiotemporal resolution. The retrieved SSR is first evaluated against hourly radiation data at three experimental stations in the Haihe River basin of China. The mean bias error (MBE) and RMSE in hourly SSR estimate are 12.0Wm(-2) (or 3.5 %) and 98.5Wm(-2) (or 28.9 %), respectively. The retrieved SSR is also evaluated against daily radiation data at 90 China Meteorological Administration (CMA) stations. The MBEs are 9.8Wm(-2) (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2Wm(-2) (or 19.1 %) and 22.1Wm(-2) (or 12.3 %), respectively. The accuracy is comparable to or even higher than two other radiation products (GLASS and ISCCP-FD), and the present method is more computationally efficient and can produce hourly SSR data at a spatial resolution of 5 km.
Subject Area自然地理学
WOS IDWOS:000372971500042
Language英语
Indexed BySCI
KeywordPhotosynthetically Active Radiation Global Data Sets Neural-network Shortwave Radiation Optical-properties Satellite Data Irradiance China Algorithm Products
Cooperation Status国内
Department环境
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Cited Times:25[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/7870
Collection图书馆
Corresponding AuthorTang, WJ
Affiliation1.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Tibetan Environm Changes & Land Surface P, Beijing 100101, Peoples R China
2.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
3.Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Tang, WJ ,Qin, J ,Yang, K ,et al. Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data[J]. ATMOSPHERIC CHEMISTRY AND PHYSICS,2016,16(4):2543-2557.
APA Tang, WJ .,Qin, J .,Yang, K .,Liu, SM .,Lu, N .,...&Tang, WJ.(2016).Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data.ATMOSPHERIC CHEMISTRY AND PHYSICS,16(4),2543-2557.
MLA Tang, WJ ,et al."Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data".ATMOSPHERIC CHEMISTRY AND PHYSICS 16.4(2016):2543-2557.
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