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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 4 | Copyright
Atmos. Chem. Phys., 16, 2543-2557, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 02 Mar 2016

Research article | 02 Mar 2016

Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data

Wenjun Tang1,2, Jun Qin1, Kun Yang1,2, Shaomin Liu3, Ning Lu4, and Xiaolei Niu1 Wenjun Tang et al.
  • 1Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China
  • 3State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China
  • 4State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract. Cloud 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 100 W m−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 12.0 W m−2 (or 3.5 %) and 98.5 W m−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 9.8 W m−2 (or 5.4 %); the RMSEs in daily and monthly mean SSR estimates are 34.2 W m−2 (or 19.1 %) and 22.1 W m−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.

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In this paper, we develop a new method to quickly retrieve high-resolution surface solar radiation (SSR) over China by combining MODIS and MTSAT data. The RMSEs of the retrieved SSR at hourly, daily, and monthly scales are about 98.5, 34.2, and 22.1 W m−2. The accuracy is comparable to or even higher than other two satellite radiation products. Finally, we derive an 8-year high-resolution SSR data set (hourly, 5 km) from 2007 to 2014, which would contribute to studies of land surface processes.
In this paper, we develop a new method to quickly retrieve high-resolution surface solar...