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ESTIMATION OF DAILY GLOBAL SOLAR IRRADIANCE FROM HIMAWARI-8 PRODUCTS OVER CHINA.
- Source :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2022, Issue 3, p493-500, 8p
- Publication Year :
- 2022
-
Abstract
- Daily global surface solar irradiance (SSI) is of great importance parameter in the surface energy balance, climate modelling, and solar energy utilization. However, it is still challenging to extend the estimated instantaneous solar radiation to the daily global SSI from either geostationary satellites or polar-orbiting satellites. In this study, a method for estimating the daily global SSI is proposed based on the Himawari-8 hourly SSI products using pixel-by-pixel Gaussian fitting to simulate the diurnal SSI variation. Compared with ground-based observations, the daily global SSI estimated by Gaussian fitting have higher accuracy under various weather conditions than by the simple accumulation or quadratic polynomials, and the coefficient of determination (R<superscript>2</superscript>) between the estimated and observed values exceeds 0.86. The verification results also show different estimation accuracies under different weather conditions. The root-mean-square errors (RMSEs) under clear-sky and all-sky conditions are 2.36 MJ/m<superscript>2</superscript> and 3.06 MJ/m<superscript>2</superscript>, respectively. In addition, experimental results show that the daily global SSI in China has high spatial heterogeneity. For higherelevation areas with low cloud cover, such as the Qinghai-Tibet Plateau, Inner Mongolia, and Northwest China, the daily global SSI is higher than other areas. In contrast, the values of daily global SSI is relatively low in the eastern regions of Southwest and Northeast China. [ABSTRACT FROM AUTHOR]
- Subjects :
- SOLAR radiation
GEOSTATIONARY satellites
GLOBAL radiation
ENERGY consumption
WEATHER
Subjects
Details
- Language :
- English
- ISSN :
- 21949042
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences
- Publication Type :
- Academic Journal
- Accession number :
- 158183905
- Full Text :
- https://doi.org/10.5194/isprs-annals-V-3-2022-493-2022